diff --git "a/app.py" "b/app.py" --- "a/app.py" +++ "b/app.py" @@ -1,939 +1,17 @@ import gradio as gr -import random import json -import re -import os -import shutil -from PIL import Image -import spaces -import torch -from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer -from threading import Thread -import time -import psutil -from sentence_transformers import SentenceTransformer import textwrap from gradio_client import Client -import gc -import sys #Imported Long Variables - comment for each move to search from relatively_constant_variables import * - -# # # Initialize the zero tensor on CUDA -# zero = torch.Tensor([0]).cuda() -# print(zero.device) # This will print 'cpu' outside the @spaces.GPU decorated function - -# # Load the embedding model -# embedding_model = SentenceTransformer('all-MiniLM-L6-v2') - -# # Load the Qwen model and tokenizer -# llmguide_model = AutoModelForCausalLM.from_pretrained( -# "Qwen/Qwen2-0.5B-Instruct", -# torch_dtype="auto", -# device_map="auto" -# ) -# llmguide_tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B-Instruct") - -# #import knowledge_base from relatively_constant_variables - -# # Create embeddings for the knowledge base -# knowledge_base_embeddings = embedding_model.encode([doc["content"] for doc in knowledge_base]) - -# def retrieve(query, k=2): -# query_embedding = embedding_model.encode([query]) -# similarities = torch.nn.functional.cosine_similarity(torch.tensor(query_embedding), torch.tensor(knowledge_base_embeddings)) -# top_k_indices = similarities.argsort(descending=True)[:k] -# return [(knowledge_base[i]["content"], knowledge_base[i]["id"]) for i in top_k_indices] - -# def get_ram_usage(): -# ram = psutil.virtual_memory() -# return f"RAM Usage: {ram.percent:.2f}%, Available: {ram.available / (1024 ** 3):.2f}GB, Total: {ram.total / (1024 ** 3):.2f}GB" - -# @spaces.GPU -# def llmguide_generate_response(prompt, doc_ids=None, stream=False): -# print(zero.device) # This will print 'cuda:0' inside the @spaces.GPU decorated function - -# messages = [ -# {"role": "system", "content": "You are a helpful assistant."}, -# {"role": "user", "content": prompt} -# ] -# text = llmguide_tokenizer.apply_chat_template( -# messages, -# tokenize=False, -# add_generation_prompt=True -# ) -# model_inputs = llmguide_tokenizer([text], return_tensors="pt").to(llmguide_model.device) - -# start_time = time.time() -# total_tokens = 0 - -# if stream: -# streamer = TextIteratorStreamer(llmguide_tokenizer, skip_special_tokens=True) -# generation_kwargs = dict( -# model_inputs, -# streamer=streamer, -# max_new_tokens=512, -# temperature=0.7, -# ) -# thread = Thread(target=llmguide_model.generate, kwargs=generation_kwargs) -# thread.start() - -# generated_text = "" -# for new_text in streamer: -# generated_text += new_text -# total_tokens += 1 -# current_time = time.time() -# tokens_per_second = total_tokens / (current_time - start_time) -# yield generated_text, f"{tokens_per_second:.2f}", "", ", ".join(doc_ids) if doc_ids else "N/A" - -# ram_usage = get_ram_usage() -# yield generated_text, f"{tokens_per_second:.2f}", ram_usage, ", ".join(doc_ids) if doc_ids else "N/A" -# else: -# generated_ids = llmguide_model.generate( -# model_inputs.input_ids, -# max_new_tokens=512 -# ) -# generated_ids = [ -# output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) -# ] -# response = llmguide_tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] -# total_tokens = len(generated_ids[0]) -# end_time = time.time() -# tokens_per_second = total_tokens / (end_time - start_time) -# ram_usage = get_ram_usage() -# yield response, f"{tokens_per_second:.2f}", ram_usage, ", ".join(doc_ids) if doc_ids else "N/A" - -# def process_query(query, use_rag, stream=False): -# if use_rag: -# retrieved_docs = retrieve(query) -# context = " ".join([doc for doc, _ in retrieved_docs]) -# doc_ids = [doc_id for _, doc_id in retrieved_docs] -# prompt = f"Context: {context}\nQuestion: {query}\nAnswer:" -# else: -# prompt = query -# doc_ids = None - -# generator = llmguide_generate_response(prompt, doc_ids, stream) - -# if stream: -# def stream_output(): -# for generated_text, tokens_per_second, ram_usage, doc_references in generator: -# yield generated_text, tokens_per_second, ram_usage, doc_references -# return stream_output() -# else: -# # For non-streaming, we just need to get the final output -# for generated_text, tokens_per_second, ram_usage, doc_references in generator: -# pass # This will iterate to the last yield -# return generated_text, tokens_per_second, ram_usage, doc_references - -#importing FAQAllprompts from relatively_constant_variables - -#----Refactor----- - -# Initialize the zero tensor on CUDA -zero = torch.Tensor([0]).cuda() -print(zero.device) # This will print 'cpu' outside the @spaces.GPU decorated function - -modelnames = ["Qwen/Qwen2-0.5B-Instruct", "Qwen/Qwen2-1.5B-Instruct", "Qwen/Qwen2-7B-Instruct", "Qwen/Qwen1.5-MoE-A2.7B-Chat", "HuggingFaceTB/SmolLM-135M-Instruct", "microsoft/Phi-3-mini-4k-instruct", - "unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit", "Groq/Llama-3-Groq-8B-Tool-Use", "hugging-quants/Meta-Llama-3.1-8B-Instruct-BNB-NF4", "SpectraSuite/TriLM_3.9B_Unpacked", "h2oai/h2o-danube3-500m-chat" - "OuteAI/Lite-Mistral-150M-v2-Instruct"] -current_model_index = 5 -modelname = modelnames[current_model_index] -lastmodelnameinloadfunction = None - -# Load the embedding model -embedding_model = SentenceTransformer('all-MiniLM-L6-v2') - -# Initialize model and tokenizer as global variables -model = None -tokenizer = None - -def get_size_str(bytes): - for unit in ['B', 'KB', 'MB', 'GB', 'TB']: - if bytes < 1024: - return f"{bytes:.2f} {unit}" - bytes /= 1024 - -def load_model(model_name): - global model, tokenizer, lastmodelnameinloadfunction - - print(f"Loading model and tokenizer: {model_name}") - - # Record initial GPU memory usage - initial_memory = torch.cuda.memory_allocated() - - # Clear old model and tokenizer if they exist - if 'model' in globals() and model is not None: - model = None - if 'tokenizer' in globals() and tokenizer is not None: - tokenizer = None - - torch.cuda.empty_cache() - gc.collect() - - model = AutoModelForCausalLM.from_pretrained( - model_name, - torch_dtype="auto", - device_map="auto" - ) - tokenizer = AutoTokenizer.from_pretrained(model_name) - - # Calculate memory usage - final_memory = torch.cuda.memory_allocated() - memory_used = final_memory - initial_memory - - model_size = sum(p.numel() * p.element_size() for p in model.parameters()) - tokenizer_size = sum(sys.getsizeof(v) for v in tokenizer.__dict__.values()) - - lastmodelnameinloadfunction = (model_name, model_size, tokenizer_size) - print(f"Model and tokenizer {model_name} loaded successfully") - print(f"Model size: {get_size_str(model_size)}") - print(f"Tokenizer size: {get_size_str(tokenizer_size)}") - print(f"GPU memory used: {get_size_str(memory_used)}") - - return (f"Model and tokenizer {model_name} loaded successfully. " - f"Model size: {get_size_str(model_size)}, " - f"Tokenizer size: {get_size_str(tokenizer_size)}, " - f"GPU memory used: {get_size_str(memory_used)}") - - -# Initial model load -load_model(modelname) - -# For this example, let's use a knowledge base with close queries -# knowledge_base = [ -# {"id": "1", "content": "The capital of France is Paris. It's known for the Eiffel Tower."}, -# {"id": "2", "content": "The capital of Italy is Rome. It's famous for the Colosseum."}, -# {"id": "3", "content": "Python is a popular programming language, known for its simplicity."}, -# {"id": "4", "content": "Java is a widely-used programming language, valued for its portability."}, -# {"id": "5", "content": "Machine learning is a subset of artificial intelligence focused on data-driven learning."}, -# {"id": "6", "content": "Deep learning is a part of machine learning based on artificial neural networks."}, -# {"id": "7", "content": "Law is a Tekken character"}, -# {"id": "8", "content": "The law is very complicated"}, -# ] - -# Create embeddings for the knowledge base -knowledge_base_embeddings = embedding_model.encode([doc["content"] for doc in knowledge_base]) - -def retrieve(query, k=2): - query_embedding = embedding_model.encode([query]) - similarities = torch.nn.functional.cosine_similarity(torch.tensor(query_embedding), torch.tensor(knowledge_base_embeddings)) - top_k_indices = similarities.argsort(descending=True)[:k] - return [(knowledge_base[i]["content"], knowledge_base[i]["id"]) for i in top_k_indices] - -def get_ram_usage(): - ram = psutil.virtual_memory() - return f"RAM Usage: {ram.percent:.2f}%, Available: {ram.available / (1024 ** 3):.2f}GB, Total: {ram.total / (1024 ** 3):.2f}GB" - -# Global dictionary to store outputs -output_dict = {} - -def empty_output_dict(): - global output_dict - output_dict = {} - print("Output dictionary has been emptied.") - -def get_model_details(model): - return { - "name": model.config.name_or_path, - "architecture": model.config.architectures[0] if model.config.architectures else "Unknown", - "num_parameters": sum(p.numel() for p in model.parameters()), - } - -def get_tokenizer_details(tokenizer): - return { - "name": tokenizer.__class__.__name__, - "vocab_size": tokenizer.vocab_size, - "model_max_length": tokenizer.model_max_length, - } - -@spaces.GPU -def generate_response(prompt, use_rag, stream=False): - global output_dict - - print(zero.device) # This will print 'cuda:0' inside the @spaces.GPU decorated function - if use_rag: - retrieved_docs = retrieve(prompt) - context = " ".join([doc for doc, _ in retrieved_docs]) - doc_ids = [doc_id for _, doc_id in retrieved_docs] - full_prompt = f"Context: {context}\nQuestion: {prompt}\nAnswer:" - else: - full_prompt = prompt - doc_ids = None - messages = [ - {"role": "system", "content": "You are a helpful assistant."}, - {"role": "user", "content": full_prompt} - ] - text = tokenizer.apply_chat_template( - messages, - tokenize=False, - add_generation_prompt=True - ) - model_inputs = tokenizer([text], return_tensors="pt").to(zero.device) - start_time = time.time() - total_tokens = 0 - - print(output_dict) - output_key = f"output_{len(output_dict) + 1}" - print(output_key) - output_dict[output_key] = { - "input_prompt": prompt, - "full_prompt": full_prompt, - "use_rag": use_rag, - "generated_text": "", - "tokens_per_second": 0, - "ram_usage": "", - "doc_ids": doc_ids if doc_ids else "N/A", - "model_details": get_model_details(model), - "tokenizer_details": get_tokenizer_details(tokenizer), - "timestamp": time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(start_time)) - } - print(output_dict) - - if stream: - streamer = TextIteratorStreamer(tokenizer, skip_special_tokens=True) - generation_kwargs = dict( - model_inputs, - streamer=streamer, - max_new_tokens=512, - temperature=0.7, - ) - thread = Thread(target=model.generate, kwargs=generation_kwargs) - thread.start() - for new_text in streamer: - output_dict[output_key]["generated_text"] += new_text - total_tokens += 1 - current_time = time.time() - tokens_per_second = total_tokens / (current_time - start_time) - ram_usage = get_ram_usage() - output_dict[output_key]["tokens_per_second"] = f"{tokens_per_second:.2f}" - output_dict[output_key]["ram_usage"] = ram_usage - yield (output_dict[output_key]["generated_text"], - output_dict[output_key]["tokens_per_second"], - output_dict[output_key]["ram_usage"], - output_dict[output_key]["doc_ids"]) - else: - generated_ids = model.generate( - model_inputs.input_ids, - max_new_tokens=512 - ) - generated_ids = [ - output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) - ] - response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] - total_tokens = len(generated_ids[0]) - end_time = time.time() - tokens_per_second = total_tokens / (end_time - start_time) - ram_usage = get_ram_usage() - - output_dict[output_key]["generated_text"] = response - output_dict[output_key]["tokens_per_second"] = f"{tokens_per_second:.2f}" - output_dict[output_key]["ram_usage"] = ram_usage - print(output_dict) - - yield (output_dict[output_key]["generated_text"], - output_dict[output_key]["tokens_per_second"], - output_dict[output_key]["ram_usage"], - output_dict[output_key]["doc_ids"]) - -def get_output_details(output_key): - if output_key in output_dict: - return output_dict[output_key] - else: - return f"No output found for key: {output_key}" - -# Update the switch_model function to return the load_model message -def switch_model(choice): - global modelname - modelname = choice - load_message = load_model(modelname) - return load_message, f"Current model: {modelname}" - -# Update the model_change_handler function -def model_change_handler(choice): - message, current_model = switch_model(choice) - return message, current_model, message # Use the same message for both outputs - -def format_output_dict(): - global output_dict - formatted_output = "" - for key, value in output_dict.items(): - formatted_output += f"Key: {key}\n" - formatted_output += json.dumps(value, indent=2) - formatted_output += "\n\n" - print(formatted_output) - return formatted_output - -#-------------------------------------------------------------------------------------------------------------------------------- - -#importing default_config from relatively_constant_variables - -# Helper functions to dynamically add items -def add_inventory_item(inventory_items, type, name, description): - new_item = {"type": type, "name": name, "description": description} - inventory_items.append(new_item) - return inventory_items - -def add_skill(skills_items, branch, name, learned): - new_skill = {"branch": branch, "name": name, "learned": learned == 'True'} - skills_items.append(new_skill) - return skills_items - -def add_objective(objectives_items, id, name, complete): - new_objective = {"id": id, "name": name, "complete": complete == 'True'} - objectives_items.append(new_objective) - return objectives_items - -def add_target(targets_items, name, x, y, collisionType, collisiontext): - new_target = {"name": name, "x": int(x), "y": int(y), "collisionType": collisionType, "collisiontext": collisiontext} - targets_items.append(new_target) - return targets_items - -#----------------------------------------------------------------------------------------------------------------------------------- - -#importing player_engagement_items and story_events from relatively_constant_variables - -def pick_random_items(items, n): - return random.sample(items, n) - -def generate_timeline(events, label): - timeline = [] - for event in events: - timeline.append((random.randint(1, 100), label, event)) - return timeline - -def create_story(timeline): - story = [] - for entry in timeline: - if entry[1] == "Story": - story.append(f"The hero {entry[2].replace('engageBattle', 'engaged in a fierce battle').replace('solveRiddle', 'solved a complex riddle').replace('exploreLocation', 'explored a mysterious location')}.") - else: - story.append(f"The player interacted with {entry[2]}.") - return " ".join(story) - -def generate_story_and_timeline(no_story_timeline_points=10, no_ui_timeline_points=10, num_lists=1, items_per_list=1, include_existing_games=False, include_multiplayer=False): # , no_media_timeline_points=5, include_media=True): - # Pick 10 random UI items - random_ui_items = pick_random_items(player_engagement_items, no_ui_timeline_points) - random_story_items = pick_random_items(story_events, no_story_timeline_points) - - # Generate UI and story timelines - ui_timeline = generate_timeline(random_ui_items, "UI") - story_timeline = generate_timeline(random_story_items, "Story") - - # Initialize merged timeline with UI and story timelines - merged_timeline = ui_timeline + story_timeline - #no_media_merged_timeline = ui_timeline + story_timeline - #print(merged_timeline) - #print(no_media_merged_timeline) - - # Include media-related items if specified - # if include_media: - # media_files = generate_media_file_list(no_media_timeline_points) - # #rendered_media = render_media_with_dropdowns(media_files) - # media_timeline = generate_timeline(media_files, "Media") - # merged_timeline += media_timeline - - # print(merged_timeline) - - # Sort the merged timeline based on the random numbers - merged_timeline.sort(key=lambda x: x[0]) - # no_media_merged_timeline.sort(key=lambda x: x[0]) - - # Create the story - story = create_story(merged_timeline) - - # Format the timeline for display - formatted_timeline = "\n".join([f"{entry[0]}: {entry[1]} - {entry[2]}" for entry in merged_timeline]) - # no_media_formatted_timeline = "\n".join([f"{entry[0]}: {entry[1]} - {entry[2]}" for entry in no_media_merged_timeline]) - - # game_structure_with_media = generate_game_structures(formatted_timeline) #, game_structure_without_media = generate_game_structures(formatted_timeline, no_media_formatted_timeline) - game_structure_with_media = convert_timeline_to_game_structure(formatted_timeline) - - print("simulplay debug - good to here 4") - - suggestions, selected_list_names = timeline_get_random_suggestions(num_lists, items_per_list, include_existing_games, include_multiplayer) - - print("simulplay debug - good to here 4") - - return formatted_timeline, story, json.dumps(game_structure_with_media, indent=2), suggestions, selected_list_names #no_media_formatted_timeline, json.dumps(game_structure_without_media, indent=2) #, game_structure_with_media - -media_file_types = ["image", "video", "audio"] - -def generate_media_file_list(n): - return [random.choice(media_file_types) for _ in range(n)] - - -def show_elements(text): - # Parse the input text - pattern = r'(\d+): (UI|Story|Media) - (.+)' - blocks = re.findall(pattern, text) - - # Sort blocks by their timestamp - blocks.sort(key=lambda x: int(x[0])) - - outputs = [] - - for timestamp, block_type, content in blocks: - if block_type == 'UI': - # Create HTML for UI elements - ui_html = f'
{content}
' - outputs.append(gr.HTML(ui_html)) - elif block_type == 'Story': - # Display story elements as Markdown - outputs.append(gr.Markdown(f"**{content}**")) - elif block_type == 'Media': - if content.lower() == 'audio': - # Placeholder for audio element - outputs.append(gr.Audio(label=f"Audio at {timestamp} in the order")) - elif content.lower() == 'video': - # Placeholder for video element - outputs.append(gr.Video(label=f"Video at {timestamp} in the order")) - elif content.lower() == 'image': - # Placeholder for image element - outputs.append(gr.Image(label=f"Image at {timestamp} in the order")) - - return outputs - -def show_elements_json_input(json_input): - data = json.loads(json_input) - masterlocation1 = data['masterlocation1'] - - outputs = [] - - for location, details in masterlocation1.items(): - if location == 'end': - continue - - with gr.Accordion(f"Location: {location} - Previous description {details['description']}", open=False): - description = gr.Textbox(label="Description", value=details['description'], interactive=True) - outputs.append(description) - - events = gr.Textbox(label="Events", value=json.dumps(details['events']), interactive=True) - outputs.append(events) - - choices = gr.Textbox(label="Choices", value=json.dumps(details['choices']), interactive=True) - outputs.append(choices) - - transitions = gr.Textbox(label="Transitions", value=json.dumps(details['transitions']), interactive=True) - outputs.append(transitions) - - # New media field - media = gr.Textbox(label="Media", value=json.dumps(details['media']), interactive=True) - outputs.append(media) - - # New developernotes field - developernotes = gr.Textbox(label="developernotes", value=json.dumps(details['developernotes']), interactive=True) - outputs.append(developernotes) - - #adding/removing a field means incrementing/decreasing the i+n to match the fields - num_current_unique_fields = 6 - - def update_json(*current_values): - updated_data = {"masterlocation1": {}} - locations = [loc for loc in masterlocation1.keys() if loc != 'end'] - for i, location in enumerate(locations): - updated_data["masterlocation1"][location] = { - "description": current_values[i*num_current_unique_fields], - "events": json.loads(current_values[i*num_current_unique_fields + 1]), - "choices": json.loads(current_values[i*num_current_unique_fields + 2]), - "transitions": json.loads(current_values[i*num_current_unique_fields + 3]), - "media": json.loads(current_values[i*num_current_unique_fields + 4]), # New media field - "developernotes": json.loads(current_values[i*num_current_unique_fields + 5]) - } - updated_data["masterlocation1"]["end"] = masterlocation1["end"] - return json.dumps(updated_data, indent=2) #json.dumps(updated_data, default=lambda o: o.__dict__, indent=2) - - update_button = gr.Button("Update JSON - Still need to copy to correct textbox to load") - json_output = gr.Textbox(label="Updated JSON - Still need to copy to correct textbox to load", lines=10) - #json_output = gr.Code(label="Updated JSON", lines=10) #Locks whole UI so use textbox - - update_button.click(update_json, inputs=outputs, outputs=json_output) - - return outputs + [update_button, json_output] #, json_output_code] - -def create_media_component(file_path): - print(file_path) - _, extension = os.path.splitext(file_path) - extension = extension.lower()[1:] # Remove the dot and convert to lowercase - - if extension in ['jpg', 'jpeg', 'png', 'gif', 'webp']: - return gr.Image(value=file_path, label="Image Input") - elif extension in ['mp4', 'avi', 'mov']: - return gr.Video(value=file_path, label="Video Input") - elif extension in ['mp3', 'wav', 'ogg']: - return gr.Audio(value=file_path, label="Audio Input") - else: - return gr.Textbox(value=file_path, label=f"File: {os.path.basename(file_path)}") - -def convert_timeline_to_game_structure(timeline): - lines = timeline.split('\n') - game_structure = {} - current_location = 0 - sub_location = 0 - - for i, line in enumerate(lines): - if line.strip() == "": - continue - - if line[0].isdigit(): # New location starts - current_location += 1 - sub_location = 0 - location_key = f"location{current_location}" - game_structure[location_key] = { - "description": "", - "events": [], - "choices": ["continue"], - "transitions": {}, - "media": [], - "developernotes": [] - } - else: # Continue with sub-locations or media entries - sub_location += 1 - location_key = f"location{current_location}_{sub_location}" - - # Extract the event description - parts = line.split(': ', 1) - if len(parts) == 2: - prefix, rest = parts - event_parts = rest.split(' - ', 1) - if len(event_parts) == 2: - event_type, event_description = event_parts - else: - event_type, event_description = "Unknown", rest - else: - event_type, event_description = "Unknown", line - - description = rest.strip() if event_type in ["Media", "UI"] else f"{event_type}: {event_description}" - - if sub_location == 0: - game_structure[f"location{current_location}"]["description"] = description - else: - game_structure[f"location{current_location}"]["events"].append({ - "description": description, - "type": event_type - }) - - # Set the transition to the next location or to the end - if i < len(lines) - 1: - next_line = lines[i + 1].strip() - if next_line and next_line[0].isdigit(): # New location starts - game_structure[f"location{current_location}"]["transitions"]["continue"] = f"masterlocation1_location{current_location + 1}" - else: - #game_structure[f"location{current_location}"]["transitions"]["continue"] = f"location_{current_location}_{sub_location + 1}" - game_structure[f"location{current_location}"]["transitions"]["continue"] = "end" - else: - game_structure[f"location{current_location}"]["transitions"]["continue"] = "end" - - # Add an end location - game_structure["end"] = { - "description": "The adventure ends here.", -# "choices": [], -# "transitions": {} - "choices": ["restart"], - "transitions": {"restart": "location1"} # Assuming location_1 is the start - - } - - # Wrap the game structure in master_location1 - wrapped_structure = {"masterlocation1": game_structure} - - return wrapped_structure - -# def generate_game_structures(timeline_with_media): #, timeline_without_media): - -# game_structure_with_media = convert_timeline_to_game_structure(timeline_with_media) -# #game_structure_without_media = convert_timeline_to_game_structure(timeline_without_media) - -# return game_structure_with_media #, game_structure_without_media - - -# def timeline_get_random_suggestions(num_lists, items_per_list): -# """ -# Generate random suggestions from a specified number of lists. - -# :param num_lists: Number of lists to consider -# :param items_per_list: Number of items to select from each list -# :return: A list of randomly selected suggestions -# """ -# selected_lists = random.sample(all_idea_lists, min(num_lists, len(all_idea_lists))) -# suggestions = [] - -# for lst in selected_lists: -# suggestions.extend(random.sample(lst, min(items_per_list, len(lst)))) - -# return suggestions - -def timeline_get_random_suggestions(num_lists, items_per_list, include_existing_games, include_multiplayer): - """ - Generate random suggestions from a specified number of lists. - - :param num_lists: Number of lists to consider - :param items_per_list: Number of items to select from each list - :param include_existing_games: Whether to include existing game inspiration lists - :param include_multiplayer: Whether to include multiplayer features list - :return: A tuple containing the list of randomly selected suggestions and the names of selected lists - """ - available_lists = all_idea_lists.copy() - if not include_existing_games: - available_lists = [lst for lst in available_lists if lst not in existing_game_inspirations] - if not include_multiplayer: - available_lists = [lst for lst in available_lists if lst != multiplayer_features] - - selected_lists = random.sample(available_lists, min(num_lists, len(available_lists))) - suggestions = [] - selected_list_names = [] - - for lst in selected_lists: - suggestions.extend(random.sample(lst, min(items_per_list, len(lst)))) - selected_list_names.append(list_names[all_idea_lists.index(lst)]) - - return suggestions, selected_list_names - -#----------------------------------------------------------------------------------------------------------------------------------- - -class Player: - def __init__(self): - self.inventory = [] - self.money = 20 - self.knowledge = {} - - def add_item(self, item): - self.inventory.append(item) - - def has_item(self, item): - return item in self.inventory - - def update_knowledge(self, topic): - self.knowledge[topic] = True - -#importing all_states from relatively_constant_variables - -def validate_transitions(all_states): - errors = [] - for location, states in all_states.items(): - for state_key, state in states.items(): - for transition_key, transition_state in state['transitions'].items(): - # Check if the transition is to another location - if transition_state in all_states: - trans_location, trans_state = transition_state, 'start' # Assuming 'start' state for new locations - elif '_' in transition_state: - trans_location, trans_state = transition_state.split('_') - else: - trans_location, trans_state = location, transition_state - - # Validate the transition state - if trans_location not in all_states or trans_state not in all_states[trans_location]: - errors.append(f"Invalid transition from {location}.{state_key} to {trans_location}.{trans_state}") - - return errors - -path_errors = validate_transitions(all_states) -if path_errors: - for error in path_errors: - print(error) -else: - print("All transitions are valid.") - -class GameSession: - def __init__(self, starting_location='village', starting_state='start'): - self.player = Player() - self.current_location = starting_location - self.current_state = starting_state - self.game_log = [] - - def make_choice(self, choice_index): - state = all_states[self.current_location][self.current_state] - if 0 <= choice_index < len(state['choices']): - choice = state['choices'][choice_index] - next_state = state['transitions'][choice] - - self.game_log.append(f"You chose: {choice}") - self.game_log.append(state['description']) - - if 'consequences' in state and choice in state['consequences']: - if state['consequences'][choice]: - state['consequences'][choice](self.player) - else: - # Handle empty consequence, e.g., log a message or provide a default action - print(f"No consequence for choice: {choice}") - # You can add any default action here if needed - - if '_' in next_state: - self.current_location, self.current_state = next_state.split('_') - else: - self.current_state = next_state - - return self.get_current_state_info() - else: - return "Invalid choice. Please try again." - - def get_current_state_info(self): - state = all_states[self.current_location][self.current_state] - choices = [f"{idx + 1}. {choice}" for idx, choice in enumerate(state['choices'])] - return state['description'], choices, "\n".join(self.game_log) - - def get_current_state_media(self): - media = all_states[self.current_location][self.current_state]['media'] - return media - - -def start_game(starting_location='village', starting_state='start'): - game_session = GameSession(starting_location, starting_state) - description, choices, game_log = game_session.get_current_state_info() - return description, choices, game_log, game_session - -def make_choice(choice, game_session, with_media=False): #Calls the nested make choice function in the game session class - if not choice: - description, choices, game_log = game_session.get_current_state_info() - return description, choices, "Please select a choice before proceeding.", game_session - - choice_index = int(choice.split('.')[0]) - 1 - result = game_session.make_choice(choice_index) - - if with_media: - media = game_session.get_current_state_media() - return result[0], gr.update(choices=result[1]), result[2], game_session, media - else: - return result[0], gr.update(choices=result[1]), result[2], game_session - -def load_game(custom_config=None, with_media=False): - global all_states - if not custom_config: - return gr.update(value="No custom configuration provided."), None, None, None, None, None, None - - try: - new_config = json.loads(custom_config) - all_states = new_config - - # Determine the starting location and state - starting_location = next(iter(all_states.keys())) - starting_state = next(iter(all_states[starting_location].keys())) - print(f"Starting location: {starting_location}, Starting state: {starting_state}") - - game_session = GameSession(starting_location, starting_state) - description, choices, game_log = game_session.get_current_state_info() - new_path_errors = validate_transitions(all_states) - - output_media = [] - - if with_media: - media_list = all_states[starting_location][starting_state].get('media', []) - print(f"Media list: {media_list}") - - if media_list: - for media_path in media_list: - #media_component = create_media_component(media_path) - output_media.append(media_path) - print(f"Created {len(output_media)} media components") - - success_message = f"Custom configuration loaded successfully!\n{new_path_errors}" - return ( - gr.update(value=success_message), - game_log, - description, - gr.update(choices=choices), - gr.update(value=custom_config), - game_session, - output_media if with_media else None - ) - - except json.JSONDecodeError as e: - error_message = format_json_error(custom_config, e) - return gr.update(value=error_message), None, None, None, None, gr.update(value=custom_config), None - - except Exception as e: - error_message = f"Error loading custom configuration: {str(e)}" - return gr.update(value=error_message), None, None, None, None, gr.update(value=custom_config), None - -def format_json_error(config, error): - lineno, colno = error.lineno, error.colno - lines = config.split('\n') - error_line = lines[lineno - 1] if lineno <= len(lines) else "" - pointer = ' ' * (colno - 1) + '^' - - return f"""Invalid JSON format in custom configuration: -Error at line {lineno}, column {colno}: -{error_line} -{pointer} -Error details: {str(error)}""" - -def display_website(link): - html = f"" - gr.Info("If 404 then the space/page has probably been disabled - normally due to a better alternative") - return html - -initgameinfo = start_game() - -#----------------------------------------------------------------------------------------------------------------------------------- - -# Set the directory where files will be saved -SAVE_DIR = os.path.abspath("saved_media") - -# Ensure the save directory exists -os.makedirs(SAVE_DIR, exist_ok=True) - -# Define supported file extensions -SUPPORTED_EXTENSIONS = { - "image": [".jpg", ".jpeg", ".png", ".gif", ".bmp", ".webp"], - "audio": [".mp3", ".wav", ".ogg"], - "video": [".mp4", ".avi", ".mov", ".webm"] -} - -def save_file(file): - if file is None: - return "No file uploaded.", gr.update() - - try: - # Get the original filename and extension - original_filename = os.path.basename(file.name) - _, extension = os.path.splitext(original_filename) - - # Check if the file extension is supported - if not any(extension.lower() in exts for exts in SUPPORTED_EXTENSIONS.values()): - return f"Unsupported file type: {extension}", gr.update() - - # Create a unique filename to avoid overwriting - base_name = os.path.splitext(original_filename)[0] - counter = 1 - new_filename = f"{base_name}{extension}" - while os.path.exists(os.path.join(SAVE_DIR, new_filename)): - new_filename = f"{base_name}_{counter}{extension}" - counter += 1 - - # Copy the file from the temporary location to our save directory - dest_path = os.path.join(SAVE_DIR, new_filename) - shutil.copy2(file.name, dest_path) - - # Return success message and updated FileExplorer - return f"File saved as {SAVE_DIR}/{new_filename}", gr.update(value=SAVE_DIR), gr.update(value=None) - except Exception as e: - return f"Error saving file: {str(e)}", gr.update(value=SAVE_DIR), gr.update() - -def view_file(file_path): - if not file_path: - return None, None, None, "No file selected." - - try: - full_path = os.path.join(SAVE_DIR, file_path) - _, extension = os.path.splitext(full_path) - extension = extension.lower() - - if extension in SUPPORTED_EXTENSIONS["image"]: - return Image.open(full_path), None, None, None - elif extension in SUPPORTED_EXTENSIONS["audio"]: - return None, full_path, None, None - elif extension in SUPPORTED_EXTENSIONS["video"]: - return None, None, full_path, None - else: - return None, None, None, f"Unsupported file type: {extension}" - except Exception as e: - return None, None, None, f"Error viewing file: {str(e)}" - -def refresh_file_explorer(): - return gr.update() - -#----------------------------------------------------------------------------------------------------------------------------------- +from leveraging_machine_learning import * +from jsconfig_idea_support import * +from timeline_and_UI_generation_functions import * +from my_text_game_engine_attempt import * +from file_explorer_and_upload import * +from state_prompt_fileman_UI_functions import * +from ui_gr_media_management import * LinPEWFprevious_messages = [] @@ -953,143 +31,519 @@ def LinPEWFformat_prompt(current_prompt, prev_messages): #----------------------------------------------------------------------------------------------------------------------------------- -def TestGradioClientQwen270b(text): - # client = Client("Qwen/Qwen2-72B-Instruct") - # result = client.predict( - # query=text, #"Hello!!", - # history=[], - # system="You are a helpful assistant.", - # api_name="/model_chat" - # ) - client = Client("CohereForAI/c4ai-command-r-v01") - result = client.predict( - user_message="Hello!!", - api_name="/generate_response" - ) - print(result) - #print(result[1][0]) #All messages in the conversation - #print(result[2]) # System prompt +def TestGradioClientrandommodel(text): + try: + # client = Client("Qwen/Qwen2-72B-Instruct") + # result = client.predict( + # query=text, #"Hello!!", + # history=[], + # system="You are a helpful assistant.", + # api_name="/model_chat" + # ) + + client = Client("huggingface-projects/gemma-2-9b-it") + result = client.predict( + message=text, #"Hello!!", + max_new_tokens=1024, + temperature=0.6, + top_p=0.9, + top_k=50, + repetition_penalty=1.2, + api_name="/chat" + ) + #print(result) + + #print(result[1][0]) #All messages in the conversation + #print(result[2]) # System prompt + except Exception as e: + result = str(e) return result #result[1][0][1] # If supporting conversations this needs to return the last message instead +#-----=------------------=--------------------=--------------------=----------------------=---- + + +ConfigConversionforExporttoPlatformselector = gr.Dropdown(choices=["playcanvas", "unreal", "gamemaker", "flutter", "2d map related space", "existing game"]) + +def ConfigConversionforExporttoPlatform(platform, config): + FinalOutput = "" + FinalOutputExplanation = "" + + if platform == "playcanvas": + FinalOutput = "" + FinalOutputExplanation = "as the config is for 1d? and the demo has teleport, the conversion to 3d is based location as backdrops for 2d interfaces to essentially use the config in the exact same way for now" + elif platform == "unreal": + FinalOutput = "" + FinalOutputExplanation = "" + elif platform == "gamemaker": + FinalOutput = "" + FinalOutputExplanation = "" + elif platform == "flutter": + FinalOutput = "" + FinalOutputExplanation = "" + elif platform == "2d map related space": + FinalOutput = "" + FinalOutputExplanation = "" + elif platform == "existing game": + FinalOutput = "Mods / Custom creator already in the game" + FinalOutputExplanation = "" + + FinalOutput = "" + return FinalOutputExplanation, FinalOutput + +#--------------------------------------- + +def find_and_visualize_json_errors(json_string): + error_positions = [] + start = 0 + while start < len(json_string): + try: + json.loads(json_string[start:]) + break # If no error, we're done + except json.JSONDecodeError as e: + position = start + e.pos + if not error_positions or position > error_positions[-1] + 10: + error_positions.append(position) + start = position + 1 + + # Insert newlines at error positions + result = "" + last_pos = 0 + for pos in error_positions: + result += json_string[last_pos:pos] + "\n" + last_pos = pos + result += json_string[last_pos:] + + return result + +def join_lines_after_correct_json_errors(lines, separator='\n'): + return separator.join(lines) + + +def testgroupingUIremotely(): + with gr.Accordion("Mini-Tutorial for this space", open=False) as testui: + gr.HTML("https://huggingface.co/spaces/fffiloni/tts-hallo-talking-portrait, https://huggingface.co/spaces/fudan-generative-ai/hallo") + gr.HTML("Cant figure how to make a live version yet so will use video") + gr.HTML("Live portrait for the explanations and slide show / screen record for the tutorial (when UI is changed have to remake so need stable workflow / order independent video)") + gr.HTML("""
Main End Goal is Rapid prototypes for education and training purposes eg. Degree of the future is game score with asecondary goal of boilerplate for context to plan stateful game mechanics (eg. power up or down in sunlight etc.)
A way to prototype basic non-3D parts of a game while trying to understand where models can streamline workflow - Doubles up a game developer quest giver (every decision point can be turned into minigame)
Game Studio 'Studio' = Config -- Assets -- Weak Playtest -- Port
""") + return testui + #----------------------------------------------------------------------------------------------------------------------------------- with gr.Blocks() as demo: - with gr.Accordion("Config and Asset Assistance - Click to open", open=False): - gr.HTML("Jonas Tyroller - This problem changes your perspective on game dev - minimise the cost of exploration so you can explore more (17:00) | dont make the same game again but worse (:)
https://youtu.be/o5K0uqhxgsE") - with gr.Accordion("Leaveraging LLMs"): - with gr.Tab("ZeroGPU - Placeholder for now"): - gr.HTML("Copy paste any old config to llm and ask to remix is the easiest
To bake 'Moral of the story' in you have to be very deliberate") - gr.HTML("UI can be media and all items can have media") - with gr.Tab("Any Request to Qwen2-0.5B"): - gr.HTML("Placeholder for https://huggingface.co/h2oai/h2o-danube3-500m-chat-GGUF and https://huggingface.co/OuteAI/Lite-Mistral-150M-v2-Instruct as alternative") - gr.HTML("https://huggingface.co/spaces/HuggingFaceTB/SmolLM-135M-Instruct-WebGPU 125 mdeol to be tested as alternative (and all up to 1.5b - how to delte a model in your code?) - Need to go over the dataset to see how to prompt it - https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus ") - gr.HTML("Placeholder for qwen 2 72b as alternative use checkbox and gradio client api call") - gr.Markdown("# Qwen-0.5B-Instruct Language Model") - gr.Markdown("This demo uses the Qwen-0.5B-Instruct model to generate responses based on your input.") - gr.HTML("Example prompts:
I am writing a story about a chef. please write dishes to appear on the menu.
What are the most common decisions that a chef story would include?
What are the kinds problems that a chef story would include?
What are the kinds of out of reach goals that a chef story would include?
Continue this config - Paste any complete block of the config") + testgroupingUIremotely() + ui_gr_media_management_acc() + + with gr.Tab("Test"): + with gr.Accordion("New Config Errors", open=False): + gr.HTML("Currently the fastest option I know for config = https://huggingface.co/spaces/KwabsHug/SimpleGameConfigIdea | End goal is to use NLP to suggest points to base intertaction on so that text stories can be supported as input as well") + with gr.Row(): + with gr.Column(): + gr.Interface(find_and_visualize_json_errors, inputs=["text"], outputs=["text"], description="Splits the JSON where suppected errors are - Remove extra double quotes and non-pair symbols") + with gr.Column(): + gr.Interface(join_lines_after_correct_json_errors, inputs=["text"], outputs=["text"], description="needs one line to load in the game section") + + with gr.Row(): + with gr.Column(scale=1): + NewConfErrorinput = gr.Textbox(label="Enter a new config here") + NewConfErrorinputbtn = gr.Button("Find Error") + NewConfErroroutput = gr.Code(language="json") + + with gr.Column(scale=2): + NewConfErrorEditedSplitinput = gr.TextArea() + NewConfErrorEditedSplitinputbtn = gr.Button("Remove seperate lines after edit") + + NewConfErrorinputbtn.click(find_and_visualize_json_errors, inputs=[NewConfErrorinput], outputs=[NewConfErrorEditedSplitinput]) + NewConfErrorEditedSplitinputbtn.click(join_lines_after_correct_json_errors, inputs=[NewConfErrorEditedSplitinput], outputs=[NewConfErroroutput]) + + with gr.Accordion("My Previous Quick Config Attempts", open=False): + for experimetal_config_name, experimetal_config in ExampleGameConfigs.items(): + with gr.Tab(experimetal_config_name): + gr.Code(json.dumps(experimetal_config, default=lambda o: o.__dict__, indent=2), label=experimetal_config_name) #str(experimetal_config) + + with gr.Tab("Demo of 'Finished Product Example'"): + gr.HTML("Incomplete") + with gr.Tab("Original with media"): + with gr.Row(): + with gr.Column(scale=2): + fpewadescription = gr.Textbox(label="Current Situation", lines=4, value=fpeinitgameinfo[0]) + fpewamediabool = gr.State(value=True) + fpewamedia = gr.State([]) #"testmedia/Stable Audio - Raindrops, output.wav"]) + + @gr.render(inputs=fpewamedia) + def dynamic_with_media(media_items): + print(media_items) + with gr.Group() as fpewamediagrouping: + gr.HTML("Placeholder to load all media tests - still need to test clearing media on ") + if media_items == []: + gr.Markdown("No media items to display.") + else: + for item in media_items: + render = create_media_component(item) + + return fpewamediagrouping + + fpewagame_session = gr.State(value=fpeinitgameinfo[3]) + + with gr.Column(scale=1): + fpewachoices = gr.Radio(label="Your Choices", choices=fpeinitgameinfo[1]) + fpewasubmit_btn = gr.Button("Make Choice") + fpewagame_log = gr.Textbox(label="Game Log", lines=20, value=fpeinitgameinfo[2]) + fpewagme_restart = gr.Button('Restart Game') + fpewacustom_configstate = gr.State(value=json.dumps(finished_product_demo, default=lambda o: o.__dict__, indent=2)) + + #Placeholders + #fpewacustom_config = gr.State() + fpewaerror_box = gr.State() + + fpewasubmit_btn.click( + make_choice, + inputs=[fpewachoices, fpewagame_session, fpewamediabool], + outputs=[fpewadescription, fpewachoices, fpewagame_log, fpewagame_session, fpewamedia] + ) + + fpewagme_restart.click(load_game, inputs=[fpewacustom_configstate, fpewamediabool], outputs=[fpewaerror_box, fpewagame_log, fpewadescription, fpewachoices, fpewacustom_configstate, fpewagame_session, fpewamedia]) + + with gr.Tab("After Extention / Enhancement Attempt"): + gr.HTML("") + + with gr.Tab("Manual - Paste Config"): + gr.HTML("Placeholder as not complete yet (3D not supported, and time (esp need for audio)") + with gr.Row(): + with gr.Column(scale=2): + gr.Markdown("# Text-based Adventure Game") + + wamediabool = gr.State(value=True) + wamedia = gr.State(["testmedia/Stable Audio - Raindrops, output.wav"]) + + @gr.render(inputs=wamedia) + def dynamic_with_media(media_items): + print(media_items) + with gr.Group() as wamediagrouping: + gr.HTML("Placeholder to load all media tests - still need to test clearing media on ") + if media_items == []: + gr.Markdown("No media items to display.") + else: + for item in media_items: + render = create_media_component(item) + + return wamediagrouping + wadescription = gr.Textbox(label="Current Situation", lines=4, value=initgameinfo[0]) + wachoices = gr.Radio(label="Your Choices", choices=initgameinfo[1]) + wasubmit_btn = gr.Button("Make Choice") + wagame_log = gr.Textbox(label="Game Log", lines=20, value=initgameinfo[2]) + wagame_session = gr.State(value=initgameinfo[3]) + wasubmit_btn.click( + make_choice, + inputs=[wachoices, wagame_session, wamediabool], + outputs=[wadescription, wachoices, wagame_log, wagame_session, wamedia] + ) + with gr.Column(scale=1): + gr.Markdown("# Debugging") + waerror_box = gr.Textbox(label="Path Errors - Incomplete debug process", lines=4, value=path_errors) + with gr.Accordion("Config (Game Spoiler and Example for llm to remix)", open=False): + wacustom_config = gr.Textbox(label="Custom Configuration (JSON)", value=json.dumps(all_states, default=lambda o: o.__dict__, indent=2), lines=8) + wacustom_configbtn = gr.Button("Load Custom Config") + + wacustom_configbtn.click( + load_game, + inputs=[wacustom_config, wamediabool], + outputs=[waerror_box, wagame_log, wadescription, wachoices, wacustom_config, wagame_session, wamedia] + ) + with gr.Tab("Manual - With Asset generation and state change options"): + gr.HTML("Edit Complexity - Story Cohesion + Valid JSON formating + Correct branching to further match the story") + with gr.Row(): + with gr.Column(scale=2): + gr.Markdown("# Text-based Adventure Game") + + edwamediabool = gr.State(value=True) + edwamedia = gr.State(["testmedia/Stable Audio - Raindrops, output.wav"]) + + @gr.render(inputs=edwamedia) + def dynamic_with_media(media_items): + print(media_items) + with gr.Group() as edwamediagrouping: + gr.HTML("Placeholder to load all media tests - still need to test clearing media on ") + if media_items == []: + gr.Markdown("No media items to display.") + else: + for item in media_items: + render = create_media_component(item) + + return edwamediagrouping + + edwadescription = gr.Textbox(label="Current Situation", lines=4, value=initgameinfo[0]) + edwachoices = gr.Radio(label="Your Choices", choices=initgameinfo[1]) + edwasubmit_btn = gr.Button("Make Choice") + edwagame_log = gr.Textbox(label="Game Log", lines=20, value=initgameinfo[2]) + edwagame_session = gr.State(value=initgameinfo[3]) + edwasubmit_btn.click( + make_choice, + inputs=[edwachoices, edwagame_session, edwamediabool], + outputs=[edwadescription, edwachoices, edwagame_log, edwagame_session, edwamedia] + ) + with gr.Column(scale=1): + gr.Markdown("# Debugging") + edwaerror_box = gr.Textbox(label="Path Errors - Incomplete debug process", lines=4, value=path_errors) + with gr.Accordion("Config - Game Spoiler - skip around here", open=False): + edwacustom_config = gr.Textbox(label="Custom Configuration (JSON)", value=json.dumps(all_states, default=lambda o: o.__dict__, indent=2), lines=8) + with gr.Row(): + edwacustom_location = gr.Textbox(label="starting location") + edwacustom_start_state = gr.Textbox(label="starting state") + edwacustom_configbtn = gr.Button("Load Custom Config") + + edwacustom_configbtn.click( + load_game_edit_version, + inputs=[edwacustom_config, edwamediabool, edwacustom_location, edwacustom_start_state], + outputs=[edwaerror_box, edwagame_log, edwadescription, edwachoices, edwacustom_config, edwagame_session, edwamedia] + ) + gr.HTML("Export currently loaded config") + with gr.Row(): + edexportstatus = gr.Textbox("", ) + gr.Button(value="Export loaded Config").click(export_config_with_media, inputs=[edwacustom_config], outputs=[edexportstatus]) + with gr.Accordion("Edit with Format Assist - Game Spoiler"): + @gr.render(inputs=edwacustom_config) #ewpgame_structure_output_text_with_media + def update(edwacustom_config): + return show_elements_json_input_play_and_edit_version(edwacustom_config) + + with gr.Tab("Semi-Auto - Edit config while playing game"): + gr.HTML("-- Incomplete -- Current problem is passing values from rendered items to the config box
Need a way have dropdowns for the filelist and transitions eg. changing transitions must auto update choices
Config to components has hardcoded variables based on the auto gen so changes break it") + with gr.Column(scale=1): + gr.Markdown("# Debugging") with gr.Row(): with gr.Column(): - llmguide_prompt = gr.Textbox(lines=2, placeholder="Enter your prompt here...") - llmguide_stream_checkbox = gr.Checkbox(label="Enable streaming") - llmguide_submit_button = gr.Button("Generate") + ewpwaerror_box = gr.Textbox(label="Path Errors - Incomplete debug process", lines=4, value=path_errors) + + + with gr.Accordion("Generate a new config"): + with gr.Accordion("Lists for config - only linear path for now", open=False): + gr.Markdown("# Story and Timeline Generator") + gr.Markdown("Click the button to generate a random timeline and story based on UI elements and story events.
Ask an LLM to use this to write a story around") + #with gr.Row(): + #ewpgame_structure_output_text_with_media = gr.Code(language="json") + #ewpgame_structure_output_text = gr.Code(language="json") + with gr.Row(): + ewptimeline_output_with_assets = gr.Textbox(label="Timeline with Assets Considered", lines=20) + #ewptimeline_output = gr.Textbox(label="Timeline (Order might be different for now)", lines=20) + with gr.Column(): + ewptimeline_output_text = gr.Textbox(label="Random Suggestions", lines=10) + ewptimeline_selected_lists_text = gr.Textbox(label="Selected Idea Lists for Inspiration", lines=2) + ewpstory_output = gr.Textbox(label="Generated Story (Order might be different for now)", lines=20) + with gr.Row(): + ewpgenerate_no_story_timeline_points = gr.Slider(minimum=1, value=10, step=1, maximum=30, label="Choose the amount of story timeline points") + ewpgenerate_no_ui_timeline_points = gr.Slider(minimum=1, value=10, step=1, maximum=30, label="Choose the amount of ui timeline points") + #ewpgenerate_no_media_timeline_points = gr.Slider(minimum=1, value=5, step=1, maximum=30, label="Choose the amount of media timeline points") + #ewpgenerate_with_media_check = gr.Checkbox(label="Generate with media", value=True) + with gr.Row(): + ewptimeline_num_lists_slider = gr.Slider(minimum=1, maximum=len(all_idea_lists), step=1, label="Number of Lists to Consider", value=3) + ewptimeline_items_per_list_slider = gr.Slider(minimum=1, maximum=10, step=1, label="Items per List", value=3) + ewptimeline_include_existing_games = gr.Checkbox(label="Include Existing Game Inspirations", value=True) + ewptimeline_include_multiplayer = gr.Checkbox(label="Include Multiplayer Features", value=True) + + ewpgenerate_button = gr.Button("Generate Story and Timeline") + + ewpwacustom_config = gr.Textbox(label="Custom Configuration (JSON)", lines=4) #value=json.dumps(all_states, default=lambda o: o.__dict__, indent=2), lines=4) #Commented out due to initial load issues + ewpwacustom_configbtn = gr.Button("Load Custom Config") + + with gr.Row(): + with gr.Column(scale=1): + with gr.Group(): + gr.Markdown("# Text-based Adventure Game") + + ewpwadescription = gr.Textbox(label="Current Situation", lines=4, value=initgameinfo[0]) + ewpwamediabool = gr.State(value=True) + ewpwamedia = gr.State(["testmedia/Stable Audio - Raindrops, output.wav"]) + + @gr.render(inputs=ewpwamedia) + def dynamic_with_media(media_items): + print(media_items) + with gr.Group() as ewpwamediagrouping: + gr.HTML("Placeholder to load all media tests - still need to test clearing media on ") + if media_items == []: + gr.Markdown("No media items to display.") + else: + for item in media_items: + render = create_media_component(item) + + return ewpwamediagrouping - with gr.Column(): - llmguide_output = gr.Textbox(lines=10, label="Generated Response") - llmguide_tokens_per_second = gr.Textbox(label="Tokens per Second") - - # llmguide_submit_button.click( - # llmguide_generate_response, - # inputs=[llmguide_prompt, llmguide_stream_checkbox], - # outputs=[llmguide_output, llmguide_tokens_per_second], - # ) - with gr.Tab("General RAG (Pathfinder?) Attempt"): - gr.HTML("https://huggingface.co/spaces/mteb/leaderboard - Source for SOTA - current using all-MiniLM-L6-v2") - gr.HTML("Placeholder for weak RAG Type Charcter interaction test aka input for JSON 'Knowledge Base' Input") - # gr.Interface( - # fn=process_query, - # inputs=[ - # gr.Textbox(lines=2, placeholder="Enter your question here..."), - # gr.Checkbox(label="Use RAG"), - # gr.Checkbox(label="Stream output") - # ], - # outputs=[ - # gr.Textbox(label="Generated Response"), - # gr.Textbox(label="Tokens per second"), - # gr.Textbox(label="RAM Usage"), - # gr.Textbox(label="Referenced Documents") - # ], - # title="RAG/Non-RAG Q&A System", - # description="Ask a question with or without using RAG. The response is generated using a GPU-accelerated model. RAM usage and referenced document IDs (for RAG) are logged." - # ) - with gr.Tab("General FAQ Attempt"): - with gr.Tab("Front end as FAQ"): - FAQMainOutput = gr.TextArea(placeholder='Output will show here', value='') - FAQCustomButtonInput = gr.TextArea(lines=1, placeholder='Prompt goes here') - - for category_name, category_prompts in FAQAllprompts.items(): - with gr.Accordion(f"General {category_name} Pattern based", open=False): - with gr.Group(): - for index, (prompt, _) in enumerate(category_prompts): - button = gr.Button(prompt) - # button.click(llmguide_generate_response, inputs=[FAQCustomButtonInput, gr.State(index), gr.State(category_name)], outputs=FAQMainOutput) - with gr.Tab("Function Call as FAQ"): - gr.HTML("Placeholder for media task query routing as dual purpose in workflow and for user queries as psuedo RAG engine") - gr.HTML("https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/#built-in-tooling - The three built-in tools (brave_search, wolfram_alpha, and code interpreter) can be turned on using the system prompt") - - with gr.Tab("ZeroGPU refactor"): - model_name = gr.State(modelname) - gr.Markdown(f"# Language Model with RAG and Model Switching") - gr.Markdown("This demo allows you to switch between different Qwen models and use Retrieval-Augmented Generation (RAG).") - gr.Markdown("**Note:** Model switching is intended for testing output quality. Due to GPU limitations, speed differences may not be apparent. Models requiring over 50GB to load will likely not work.") - gr.Markdown("Need to add support for switching models and loading GGUF and GPTQ and BNB") - gr.Markdown("57b MOE takes 6min to load and gets workload evicted - storage limit over 100G") + ewpwachoices = gr.Radio(label="Your Choices", choices=initgameinfo[1]) + ewpwasubmit_btn = gr.Button("Make Choice") + ewpwagame_log = gr.Textbox(label="Game Log", lines=20, value=initgameinfo[2]) + ewpwagame_session = gr.State(value=initgameinfo[3]) + ewpwasubmit_btn.click( + make_choice, + inputs=[ewpwachoices, ewpwagame_session, ewpwamediabool], + outputs=[ewpwadescription, ewpwachoices, ewpwagame_log, ewpwagame_session, ewpwamedia] + ) - with gr.Row(): - with gr.Column(): - model_dropdown = gr.Dropdown(choices=modelnames, value=modelname, label="Select Model") - current_model_info = gr.Markdown(f"Current model: {modelname}") - current_model_info2 = gr.Interface(lambda x: f"Current model: {lastmodelnameinloadfunction[0]} ({lastmodelnameinloadfunction[1]}) (tokeniser = {lastmodelnameinloadfunction[2]})", inputs=None, outputs=["markdown"], description="Check what was last loaded (As the space has memory and I havent figured how spaces work enough eg. how does multiple users affect this)") # gr.Markdown(f"Current model: {lastmodelnameinloadfunction}") - gr.HTML("Need to figure out my test function calling for groq-8b as it doesnt seem to answer chat properly - will need a seperate space - eg. letter counting, plural couting, using a function as form for llm to fill (like choosing which model and input parameters for media in game)?") - prompt = gr.Textbox(lines=2, placeholder="Enter your prompt here...") - stream_checkbox = gr.Checkbox(label="Enable streaming") - rag_checkbox = gr.Checkbox(label="Enable RAG") - submit_button = gr.Button("Generate") + ewpwacustom_configbtn.click( + load_game, + inputs=[ewpwacustom_config, ewpwamediabool], + outputs=[ewpwaerror_box, ewpwagame_log, ewpwadescription, ewpwachoices, ewpwacustom_config, ewpwagame_session, ewpwamedia] + ) + with gr.Column(scale=1): + @gr.render(inputs=ewpwacustom_config) #ewpgame_structure_output_text_with_media + def update(ewpwacustom_config): + return show_elements_json_input(ewpwacustom_config) - with gr.Column(): - with gr.Tab("Current Response"): - output = gr.Textbox(lines=10, label="Generated Response") - tokens_per_second = gr.Textbox(label="Tokens per Second") - ram_usage = gr.Textbox(label="RAM Usage") - doc_references = gr.Textbox(label="Document References") - with gr.Tab("All Responses So far"): - gr.Markdown("As we want a iterative process all old responses are saved for now - will figure how to make per user solution - need some kind of hook onto the loading a space to assign random usercount with timestamp") - gr.Interface(format_output_dict, inputs=None, outputs=["textbox"]) + ewpgenerate_button.click(generate_story_and_timeline, inputs=[ewpgenerate_no_story_timeline_points, ewpgenerate_no_ui_timeline_points, ewptimeline_num_lists_slider, ewptimeline_items_per_list_slider, ewptimeline_include_existing_games, ewptimeline_include_multiplayer], outputs=[ewptimeline_output_with_assets, ewpstory_output, ewpwacustom_config, ewptimeline_output_text, ewptimeline_selected_lists_text]) #ewptimeline_output_with_assets, ewptimeline_output, ewpstory_output, ewpwacustom_config, ewpgame_structure_output_text]) #ewpgame_structure_output_text_with_media, ewpgame_structure_output_text]) + + with gr.Tab("Config Development Assistance"): #, open=False): + gr.HTML("Jonas Tyroller (https://youtu.be/o5K0uqhxgsE)- This problem changes your perspective on game dev - minimise the cost of exploration so you can explore more (17:00) | dont make the same game again but worse (:)
How can we measure worse?") + gr.Markdown("# Current [Timeline] Workflow = [Precreation] Mermaid Diagram to (1) Story to (2) [Creation] Initial JSON (through LLM and fix JSON by hand) to (3) [Postcreation] JSON Corrections (through LLM and fix JSON by hand) to (4) Media prompts to (5) Asset Generation to (6) JSON Media field population") + with gr.Tab("Precreation"): + gr.Markdown() + gr.HTML("Need to learn how rules (our preferences) affect the config creation process
eg. core game loop - Write a story where the plot only moves based on - [insert core game loop]") + with gr.Tab("Main parts of stories to put into structures"): + with gr.Tab("Purpose (The types of decisions you want the user to make) (Main Theme)"): + gr.HTML("Unpredictable Nuances (Any movie) / Simulation (eg. Work - mixing rare industry crossover) (eg. A phrasebook into interactive game) / Time capsule / Tangible Metaphor / Song Concept as game / Advert as a game") + gr.HTML(" https://en.wikipedia.org/wiki/Philosophy https://en.wikipedia.org/wiki/Moral_injury") + with gr.Tab("Structure - (Non/)Linear Storylines"): + gr.HTML("Trying to abstract the process into one worflow is beyond me so multiple paths to goal (config) is the aim now") + with gr.Tab("Branching - Decisions / Timeline Creation to Story to Config Conversation"): + gr.HTML("Structures for interesting timeline progression") + gr.HTML("Claude Artifacts to illustrate nested structure brainstorms -
https://claude.site/artifacts/4a910d81-1541-49f4-8531-4f27fe56cd1e
https://claude.site/artifacts/265e9242-2093-46e1-9011-ed6ad938be90?fullscreen=false
") + gr.HTML("Placeholder - Considerations - Story from the perspective of Main character or NPC in the LLM genereated story") + mermaideditoriframebtn = gr.Button("Load Mermaid Editor") + mermaideditoriframe = gr.HTML("") + mermaideditoriframebtn.click(fn=lambda x: "", outputs=mermaideditoriframe) + with gr.Accordion("Mermaid Structures - click to open", open=False): + for key, item in mermaidstorystructures.items(): + with gr.Accordion(key, open=False): + gr.Code(item, label=key) + + with gr.Tab("Linear - Player List to Empty Config with Edit support (Narrative based)"): + with gr.Accordion("Can copy in the Test Example State Machine tab - only linear path for now", open=False): + gr.Markdown("# Story and Timeline Generator") + gr.Markdown("Click the button to generate a random timeline and story based on UI elements and story events.
Ask an LLM to use this to write a story around") + with gr.Row(): + game_structure_output_text_with_media = gr.Code(language="json") + #game_structure_output_text = gr.Code(language="json") + with gr.Accordion("JSON with no edits"): + gr.HTML("A long game is a bunch of short games") + with gr.Row(): + timeline_output_with_assets = gr.Textbox(label="Timeline with Assets Considered (gaps = side quests)", lines=25) + #timeline_output = gr.Textbox(label="Timeline (Order might be different for now)", lines=20) + with gr.Column(): + timeline_output_text = gr.Textbox(label="Random Suggestions", lines=10) + timeline_selected_lists_text = gr.Textbox(label="Selected Idea Lists for Inspiration", lines=2) + story_output = gr.Textbox(label="Generated Story (Order might be different for now)", lines=20) + with gr.Row(): + generate_no_story_timeline_points = gr.Slider(minimum=1, value=10, step=1, maximum=30, label="Choose the amount of story timeline points") + generate_no_ui_timeline_points = gr.Slider(minimum=1, value=10, step=1, maximum=30, label="Choose the amount of ui timeline points") + #generate_no_media_timeline_points = gr.Slider(minimum=1, value=5, step=1, maximum=30, label="Choose the amount of media timeline points") + #generate_with_media_check = gr.Checkbox(label="Generate with media", value=True) + with gr.Row(): + timeline_num_lists_slider = gr.Slider(minimum=1, maximum=len(all_idea_lists), step=1, label="Number of Lists to Consider", value=3) + timeline_items_per_list_slider = gr.Slider(minimum=1, maximum=10, step=1, label="Items per List", value=3) + timeline_include_existing_games = gr.Checkbox(label="Include Existing Game Inspirations", value=True) + timeline_include_multiplayer = gr.Checkbox(label="Include Multiplayer Features", value=True) + # timeline_generate_button = gr.Button("Generate Random Suggestions").click( + # timeline_get_random_suggestions, + # inputs=[timeline_num_lists_slider, timeline_items_per_list_slider, timeline_include_existing_games, timeline_include_multiplayer], + # outputs=[timeline_output_text, timeline_selected_lists_text] + # ) + generate_button = gr.Button("Generate Story and Timeline (Click to get UI that will assist with JSON formatting)") + + @gr.render(inputs=game_structure_output_text_with_media) + def update(game_structure_output_text_with_media): + return show_elements_json_input(game_structure_output_text_with_media) + + generate_button.click(generate_story_and_timeline, inputs=[generate_no_story_timeline_points, generate_no_ui_timeline_points, timeline_num_lists_slider, timeline_items_per_list_slider, timeline_include_existing_games, timeline_include_multiplayer], outputs=[timeline_output_with_assets, story_output, game_structure_output_text_with_media, timeline_output_text, timeline_selected_lists_text]) #, generate_no_media_timeline_points, generate_with_media_check], outputs=[timeline_output_with_assets, timeline_output, story_output, game_structure_output_text_with_media, game_structure_output_text]) + + with gr.Tab("Linear - Existing Media eg. Songs and Screenshots"): + gr.HTML("Media position in the story part beginning, during or end") + gr.HTML("Create media first and then ask llm to make join points in the config.
For images need to add the location to every prompt or images will be in random lo") + + with gr.Tab("Linear - Machine Leaning Architectures as game maps"): + gr.HTML("Transformers, SSMs, Image and Video Generation Architectures, GANs, RNNS, etc.") + + with gr.Tab("Linear - Prompt Engineering as basis for ideation process"): + gr.HTML("Current Assited workflow idea - Story timeline events suggestions (LLM / Premade List) | Merging events with premade mermaid structures (LLM + Story Text + Mermaid Text) | Edit mermaid till satisfied (LLM + Story Text) | Ask LLM to convert to config (LLM + JSON Text) | Edit config (LLM / User with format assistance or not) | Playtest and go back to mermaaid or config if there are problems") + gr.HTML("Interactive movie (UI interaction or no progress) vs Branching Paths (Maze)") + gr.HTML("Things that can change the workflow - Asset First (Make Asset and make the transitions using LLM), Export First (Custom JS config, Playcanvas, Unreal Engine reverse engineered to this spaces config?) Game Mechanics First (eg. Player Stats, Inventory and NPCS not implemented yet, so traversal type games best aka graph like structures)") + gr.HTML("Config writing = Remix old one, Ask LLM to make one, Endless combination testing using the prompt engineering above or writing by hand (prompt engineering on yourself)") + gr.HTML("Can use song lyrics as thematic source") + gr.HTML("Placeholder for each below prompt getting a Textbox") + # for item in Storycraftprompts: + # input = gr.State(item) + # output = gr.Textbox("", label=item) + # outputbtn = gr.Button(item).click(fn=llmguide_generate_response, inputs=input, outputs=output) + # for i, item in enumerate(Storycraftprompts, 1): + # input = gr.State(item) + # previous_input = gr.State(lambda: LinPEWFprevious_messages) + # output = gr.Textbox("", label=f"Output {i}") + + # def LinPEWF_update_and_generate(prompt, prev_msgs): + # prev_msgs.append(prompt) + # formatted_prompt = LinPEWFformat_prompt(prompt, prev_msgs) + # response = llmguide_generate_response(formatted_prompt) + # full_response = "" + # for chunk in response: + # full_response += chunk + # prev_msgs.append(f"Response: {full_response}") + # return full_response + + # outputbtn = gr.Button(f"Generate {i}").click( + # fn=LinPEWF_update_and_generate, + # inputs=[input, previous_input], + # outputs=output + # ) + # LinPEWFprevious_messages.append(item) + + #with gr.Accordion("Decisions / Timeline Creation to Story to Config Conversation", open=False): + with gr.Tab("Branching - Network analysis to Game config"): + gr.HTML("Placeholder for analysing multiple stories for their network structures and creating general rules for a strucutre generator based of named entity recognition and bias to locations or people - The extreme long way") + + with gr.Tab("Linear - Chess PNG to Game config"): + gr.HTML("Any Chess match can serve as end of game final battle") - model_dropdown.change( - model_change_handler, - inputs=[model_dropdown], - outputs=[model_name, current_model_info, output] - ) - - submit_button.click( - generate_response, - inputs=[prompt, rag_checkbox, stream_checkbox], - outputs=[output, tokens_per_second, ram_usage, doc_references], - ) - with gr.Tab("Hugging Chat"): - gr.HTML("https://huggingface.co/chat
Huggingface chat supports - State Management (Threads), Image Generation and editing, Websearch, Document parsing (PDF?), Assistants and larger models than zero gpu can support in July 2024 (Unquantised 30B and above)") - gr.HTML("Existing Assistants to use and planning custom assistants placeholder") - with gr.Tab("Embedded Spaces and gradio client"): - gr.HTML("In Asset Generation Tab under Text") - with gr.Tab("Gradio Client"): - gr.Interface(fn=TestGradioClientQwen270b, inputs="text", outputs="markdown", description="Single response test of gradio client - Cohere for test as api not working on Qwen/Qwen2-72B-Instruct, Use for testing like using a space and duplicate for heavy testing") - with gr.Tab("Preview APIs"): - gr.HTML("July 2024 - Gemini, Cohere and Groq rate limit free APIs") - gr.Markdown("# Current Workflow = Mermaid Diagram to (1) Story to (2) Initial JSON (through LLM and fix JSON by hand) to JSON Corrections (through LLM and fix JSON by hand) to (4) Media prompts to (5) Asset Generation to (6) JSON Media field population") - with gr.Tab("Workflow Piplines"): - gr.HTML("
Workflow is currently based on using ZeroGPU space and resources - will add platform specific (chat changes prompts and native image generation changes order) flows much later
Prompt Testing has to be add each parameter level of models as smaller models can get it right with different wording") + with gr.Tab("Conflicts"): + gr.HTML("Most stories are based around") + with gr.Tab("Setting"): + gr.HTML("https://en.wikipedia.org/wiki/History#Periods") + gr.HTML("") + with gr.Tab("Locations"): + gr.HTML("Jungle, Sea, Desert, Snow, City, Village, Space") + with gr.Tab("Character Relations"): + gr.HTML("Friend or foe") + with gr.Tab("Character Archetypes"): + gr.HTML("") + with gr.Tab("Additional Themes"): + gr.HTML("") + with gr.Tab("Generalized Inspiration from existing games"): + gr.HTML("Consequences of buying into Hype quickly (I was going to find a way to fit 70B into zerogpu) - https://www.reddit.com/r/LocalLLaMA/comments/1fc98fu/confirmed_reflection_70bs_official_api_is_sonnet/") + with gr.Tab("Old Ideas to merge"): + gr.HTML("Random Scenario / Song to 'full game' manual or auto is end goal ") + gr.HTML("Componets (outside Code Support for Config): Decisions (and context explanation), Nested Sections, Media (Especially to affect decisions), Replayability (GTA and Tekken type mechanics in text form), Theme integration (Modified Varibles that affect UI or config order)") + gr.HTML("Existing Games eg. GTA Heists - Same Map with overlapping branching narratives, Battlefront - Elites amongst Commoners, Tekken Casino (one mistake = 1/2 or 1/3 of your Resources) and Turn based: 'Tactics' type nintendo games, Chess (and any other tile based game) ") + gr.HTML("Existing Game Rules for text - Cyberpunk RED, ") + gr.HTML("Community playthrough = Tally of players choices, Random item placed in a random location - first person to get it wins, Survival by location or characters met") + gr.HTML("Some Kinds of game skeletons ideas - Timelines, Graph as State machine paths, Economy ecosystem") + gr.HTML("One prompt to be used to test models -
Please make 10 python lists for the types of media files and their purposes in a game and then use those lists to random generate a timeline of 20 items when the function is called
Great next suggest ways to improve this function to create better timelines") + with gr.Tab("Structural Inspirations"): + gr.HTML("GTA Heists - Replayability and stakes, Tekken - 2/3 mistakes = lost round ") + gr.HTML("Elden Ring - Story telling by traversal of map") + gr.HTML("Sports Scores, ") + + with gr.Tab("Worldbuilding tools"): + gr.HTML("Good wordbuilding makes new scenarios automatic") + gr.HTML("In a game awareness = directly affect one of the characters - Any time fights are involved, paralympics categorisations can be inspiration (watched paralympics and wanted to learn the categories eg. s10)") + + with gr.Tab("Concept combination brainstorm"): + gr.HTML("The story and the gameplay dont have to occur at the same time - eg. ") + gr.Markdown("## Prompts / Mermaid diagrams to be made from the ideas for workflow") + with gr.Tab("Using Time as a proxy for all conepts?"): + gr.HTML("A timeline is the most important part of the story - once that is set you can do anything?") + with gr.Tab("Concept Bashing? Ideas"): + with gr.Row(): + gr.Textbox(TimeRelatedConceptsForIdeaGeneration, lines=30) + gr.Textbox(Nonlinearprogressionideas, lines=30) + gr.Textbox(Adjectivebasedcombinationideatextsv2, lines=30) + gr.Textbox(Adjectivebasedcombinationideatexts, lines=30) + gr.HTML("Media Critiques (eg. Youtube Rants) as Prompts to whole games as interactive explanation") with gr.Tab("Mermaid Diagram to (1) Story"): gr.HTML("Below 70B seem to struggle here") gr.Code(WFStage1prompt , label="Prompt Used") @@ -1099,149 +553,57 @@ with gr.Blocks() as demo: gr.Textbox(TimeRelatedMermaidStoryAttempttoRefinefrom[2], lines=30) gr.Textbox(TimeRelatedMermaidStoryAttempttoRefinefrom[3], lines=30) gr.Textbox(TimeRelatedMermaidStoryAttempttoRefinefrom[4], lines=30) - with gr.Tab("Story to (2) JSON (through LLM and fix JSON by hand)"): - gr.HTML("This Step specifically has to be function call only if you explain the tool can take as many blocks as neccesary") - gr.Code(WFStage2prompt , label="Prompt Used") + with gr.Tab("Mermaid Diagrams"): + with gr.Accordion("Mermaid Structures - click to open", open=False): + for key, item in examplemermaidconceptblendingstrutures.items(): + gr.Code(item, label=key) + with gr.Tab("HF datasets as scenario inspiration"): + gr.HTML("eg. https://huggingface.co/datasets/thesven/gsm8k-reasoning | https://huggingface.co/datasets/HuggingFaceFW/fineweb | HuggingFaceFW/fineweb-edu | https://huggingface.co/datasets/HuggingFaceFV/finevideo - https://huggingface.co/spaces/HuggingFaceFV/FineVideo-Explorer | even translations - https://huggingface.co/datasets/opencsg/chinese-fineweb-edu") + with gr.Tab("Creation"): + with gr.Tab("AI generates scaffolds"): + gr.HTML("8b size models make Format mistakes. For extention purposes this step specifically might have to be function call only if you explain the tool can take as many blocks as neccesary
Prompts without extra considerations") + with gr.Row(): + gr.Code(WFStage2prompt , label="Prompt Used (working on 70B and above)") + gr.Code(WFStage2prompt2 , label="Alternate prompt to use (working on 70B and above)") + with gr.Row(): + gr.HTML("The theme is waking up on the day AGI is created and how life will change") + gr.HTML("") + with gr.Tab("Extension ideas"): + gr.HTML("The basic idea is just make a new story and fight the JSON issues to make them fit and then rinse and repeat") + with gr.Tab("Follow-up prompts ideas"): + gr.HTML("Extension = any description in original JSON as the theme using the prompt template") + with gr.Tab("Change flow of existing config"): + gr.HTML("Ask for incorporation of mermaid structure into JSON or ask for how the mermaid structure would change the JSON / story") + + with gr.Tab("Post-creation"): + gr.HTML("Assumed story is complete is complete at this stage / will be continued in seperate config") + with gr.Tab("Initial Media Generation"): + gr.HTML("Placeholder for verb extraction from description and choices - to help guide initial media population") + gr.HTML("To do a full test need media - Fast and quality - https://huggingface.co/spaces/black-forest-labs/FLUX.1-schnell") + with gr.Tab("Initial JSON (through LLM and fix JSON by hand) to (3) JSON Corrections (through LLM and fix JSON by hand)"): gr.Code("Lets a critique this JSON to find areas fix", label="prompt used") with gr.Tab("JSON Corrections (through LLM and fix JSON by hand) to (4) Media prompts"): gr.HTML("This Step specifically has to be function call only") gr.HTML("Gemma-9b and Mistral 8x7b is better at this prompt than llama 3.1 8b and 70b
Can add (each media field must get an entry) and (in python list of list format for plug and play) but they affect final output") gr.Code("Lets a make a list for the prompts we will use to make media objects in this JSON. Make one for a person to interpret and one for direct media generators that focus on keywords: ", label="prompt used") - with gr.Tab("Media prompts to (5) Asset Generation"): + with gr.Tab("Media prompts to (5) Asset Generation to (6) JSON Media field population"): gr.HTML("This Step specifically has to be function call only") gr.Code("For each Media item described classify it by media type and comment if in a story setting it would need timing ", label="prompt used") gr.HTML("This Step can be merged with the next if we can make a editor like in the semi-Auto space in test and edit tailored to just accepting the JSON and exposing only media part for editing") - with gr.Tab("Asset Generation to (6) JSON Media field population"): gr.Code("Here is a list of file names - assume they are in the order of the empty media sections of the JSON and rewrite the JSON", label="prompt used") - with gr.Tab("All Variations / Themes for entire workflow proccess - to be completed"): - gr.HTML("Trying to abstract the process into one worflow is beyond me so multiple paths to goal (config) is the aim now") - with gr.Tab("Branching - Decisions / Timeline Creation to Story to Config Conversation"): - gr.HTML("Structures for interesting timeline progression") - gr.HTML("Claude Artifacts to illustrate nested structure brainstorms -
https://claude.site/artifacts/4a910d81-1541-49f4-8531-4f27fe56cd1e
https://claude.site/artifacts/265e9242-2093-46e1-9011-ed6ad938be90?fullscreen=false
") - gr.HTML("Placeholder - Considerations - Story from the perspective of Main character or NPC in the LLM genereated story") - mermaideditoriframebtn = gr.Button("Load Mermaid Editor") - mermaideditoriframe = gr.HTML("") - mermaideditoriframebtn.click(fn=lambda x: "", outputs=mermaideditoriframe) - with gr.Accordion("Mermaid Structures - click to open", open=False): - for key, item in mermaidstorystructures.items(): - with gr.Accordion(key, open=False): - gr.Code(item, label=key) - - with gr.Tab("Linear - Player List to Empty Config with Edit support (Narrative based)"): - with gr.Accordion("Can copy in the Test Example State Machine tab - only linear path for now", open=False): - gr.Markdown("# Story and Timeline Generator") - gr.Markdown("Click the button to generate a random timeline and story based on UI elements and story events.
Ask an LLM to use this to write a story around") - with gr.Row(): - game_structure_output_text_with_media = gr.Code(language="json") - #game_structure_output_text = gr.Code(language="json") - with gr.Accordion("JSON with no edits"): - gr.HTML("A long game is a bunch of short games") - with gr.Row(): - timeline_output_with_assets = gr.Textbox(label="Timeline with Assets Considered (gaps = side quests)", lines=25) - #timeline_output = gr.Textbox(label="Timeline (Order might be different for now)", lines=20) - with gr.Column(): - timeline_output_text = gr.Textbox(label="Random Suggestions", lines=10) - timeline_selected_lists_text = gr.Textbox(label="Selected Idea Lists for Inspiration", lines=2) - story_output = gr.Textbox(label="Generated Story (Order might be different for now)", lines=20) - with gr.Row(): - generate_no_story_timeline_points = gr.Slider(minimum=1, value=10, step=1, maximum=30, label="Choose the amount of story timeline points") - generate_no_ui_timeline_points = gr.Slider(minimum=1, value=10, step=1, maximum=30, label="Choose the amount of ui timeline points") - #generate_no_media_timeline_points = gr.Slider(minimum=1, value=5, step=1, maximum=30, label="Choose the amount of media timeline points") - #generate_with_media_check = gr.Checkbox(label="Generate with media", value=True) - with gr.Row(): - timeline_num_lists_slider = gr.Slider(minimum=1, maximum=len(all_idea_lists), step=1, label="Number of Lists to Consider", value=3) - timeline_items_per_list_slider = gr.Slider(minimum=1, maximum=10, step=1, label="Items per List", value=3) - timeline_include_existing_games = gr.Checkbox(label="Include Existing Game Inspirations", value=True) - timeline_include_multiplayer = gr.Checkbox(label="Include Multiplayer Features", value=True) - # timeline_generate_button = gr.Button("Generate Random Suggestions").click( - # timeline_get_random_suggestions, - # inputs=[timeline_num_lists_slider, timeline_items_per_list_slider, timeline_include_existing_games, timeline_include_multiplayer], - # outputs=[timeline_output_text, timeline_selected_lists_text] - # ) - generate_button = gr.Button("Generate Story and Timeline (Click to get UI that will assist with JSON formatting)") - - @gr.render(inputs=game_structure_output_text_with_media) - def update(game_structure_output_text_with_media): - return show_elements_json_input(game_structure_output_text_with_media) - - generate_button.click(generate_story_and_timeline, inputs=[generate_no_story_timeline_points, generate_no_ui_timeline_points, timeline_num_lists_slider, timeline_items_per_list_slider, timeline_include_existing_games, timeline_include_multiplayer], outputs=[timeline_output_with_assets, story_output, game_structure_output_text_with_media, timeline_output_text, timeline_selected_lists_text]) #, generate_no_media_timeline_points, generate_with_media_check], outputs=[timeline_output_with_assets, timeline_output, story_output, game_structure_output_text_with_media, game_structure_output_text]) - - with gr.Tab("Linear - Existing Media eg. Songs and Screenshots"): - gr.HTML("Media position in the story part beginning, during or end") - - with gr.Tab("Linear - Machine Leaning Architectures as game maps"): - gr.HTML("Transformers, SSMs, Image and Video Generation Architectures, GANs, RNNS, etc.") - - with gr.Tab("Linear - Prompt Engineering as basis for ideation process"): - gr.HTML("Current Assited workflow idea - Story timeline events suggestions (LLM / Premade List) | Merging events with premade mermaid structures (LLM + Story Text + Mermaid Text) | Edit mermaid till satisfied (LLM + Story Text) | Ask LLM to convert to config (LLM + JSON Text) | Edit config (LLM / User with format assistance or not) | Playtest and go back to mermaaid or config if there are problems") - gr.HTML("Interactive movie (UI interaction or no progress) vs Branching Paths (Maze)") - gr.HTML("Things that can change the workflow - Asset First (Make Asset and make the transitions using LLM), Export First (Custom JS config, Playcanvas, Unreal Engine reverse engineered to this spaces config?) Game Mechanics First (eg. Player Stats, Inventory and NPCS not implemented yet, so traversal type games best aka graph like structures)") - gr.HTML("Config writing = Remix old one, Ask LLM to make one, Endless combination testing using the prompt engineering above or writing by hand (prompt engineering on yourself)") - gr.HTML("Can use song lyrics as thematic source") - gr.HTML("Placeholder for each below prompt getting a Textbox") - # for item in Storycraftprompts: - # input = gr.State(item) - # output = gr.Textbox("", label=item) - # outputbtn = gr.Button(item).click(fn=llmguide_generate_response, inputs=input, outputs=output) - # for i, item in enumerate(Storycraftprompts, 1): - # input = gr.State(item) - # previous_input = gr.State(lambda: LinPEWFprevious_messages) - # output = gr.Textbox("", label=f"Output {i}") - - # def LinPEWF_update_and_generate(prompt, prev_msgs): - # prev_msgs.append(prompt) - # formatted_prompt = LinPEWFformat_prompt(prompt, prev_msgs) - # response = llmguide_generate_response(formatted_prompt) - # full_response = "" - # for chunk in response: - # full_response += chunk - # prev_msgs.append(f"Response: {full_response}") - # return full_response + with gr.Tab("Existing Config Crafting Progression"): + with gr.Accordion("Test config format assist - to merge with the one in the test area", open=False ): + gr.HTML("Placeholder for media field uploading / dropdowns for all files that have been uploaded") + gr.HTML("Splits by new line and is looking something like this 1: UI/Story/Media - content/type - Need to adjust it to current config format ") + input_text = gr.Textbox(label="Input Text", lines=10) + output_group = gr.Group() - # outputbtn = gr.Button(f"Generate {i}").click( - # fn=LinPEWF_update_and_generate, - # inputs=[input, previous_input], - # outputs=output - # ) - - # LinPEWFprevious_messages.append(item) - - #with gr.Accordion("Decisions / Timeline Creation to Story to Config Conversation", open=False): - with gr.Tab("Branching - Network analysis to Game config"): - gr.HTML("Placeholder for analysing multiple stories for their network structures and creating general rules for a strucutre generator based of named entity recognition and bias to locations or people - The extreme long way") - - with gr.Tab("Linear - Chess PNG to Game config"): - gr.HTML("Any Chess match can serve as end of game final battle") - - with gr.Tab("Main problem to solve - Concept combination / integration and non-linear progression planning"): - gr.HTML("The story and the gameplay dont have to occur at the same time - eg. ") - gr.Markdown("## Prompts / Mermaid diagrams to be made from the ideas for workflow") - with gr.Tab("Using Time as a proxy for all conepts?"): - gr.HTML("A timeline is the most important part of the story - once that is set you can do anything?") - with gr.Tab("Concept Bashing? Ideas"): - with gr.Row(): - gr.Textbox(TimeRelatedConceptsForIdeaGeneration, lines=30) - gr.Textbox(Nonlinearprogressionideas, lines=30) - gr.Textbox(Adjectivebasedcombinationideatextsv2, lines=30) - gr.Textbox(Adjectivebasedcombinationideatexts, lines=30) - gr.HTML("Media Critiques (eg. Youtube Rants) as Prompts to whole games as interactive explanation") - with gr.Tab("Mermaid Diagrams"): - with gr.Accordion("Mermaid Structures - click to open", open=False): - for key, item in examplemermaidconceptblendingstrutures.items(): - gr.Code(item, label=key) - - with gr.Tab("Existing Config Crafting Progression"): - with gr.Accordion("Test for config to gradio components order - ignore for now", open=False ): - gr.HTML("Placeholder for changing the render below to the one above for new config but with the ability to upload files aka the media field should be file uploader / dropdowns for all files that have been uploaded") - gr.Markdown("Asset Generation") - gr.HTML("Splits by new line - The idea here was to allow for saving the file ") - input_text = gr.Textbox(label="Input Text", lines=10) - output_group = gr.Group() - - @gr.render(inputs=input_text) - def update(text): - return show_elements(text) + @gr.render(inputs=input_text) + def update(text): + return show_elements(text) + +# import originalconfigatbeinningofthisspace, claude3_5_06072024configtips, tipsupdatedconfigatbeinningofthisspace from relatively_constant_variables with gr.Tab("Quick Ways to evaluate current config"): gr.HTML("Ask SOTA LLMs This prompt:
This config is for a basic text based game engine. I dont have any structural metrics to assess the quality of the config. What JSON things can we look at to see if it may be too bland for a person testing the game?
Then Paste the Config with the prompt") gr.HTML("""Original Claude 3.5 Sonnet Response snippets:
@@ -1260,9 +622,6 @@ Implementing more consequences for player actions Expanding descriptions to create a richer narrative Incorporating media elements Creating more diverse paths through the game""") - - # import originalconfigatbeinningofthisspace, claude3_5_06072024configtips, tipsupdatedconfigatbeinningofthisspace from relatively_constant_variables - with gr.Tab("Improvement of the default config"): gr.HTML("Example of how to advance a game config with LLM - end goal is to have automatic worflow that takes these considerations into account
Things missing from the game engine - Economics and Basic Politics (NPC affiliation)") gr.HTML("Suggestions from claude 3.5 on how to change config") @@ -1280,509 +639,400 @@ Creating more diverse paths through the game""") """ + display_tipsupdatedconfigatbeinningofthisspace + """ """) - with gr.Tab("Themes and Topics"): - gr.HTML("https://en.wikipedia.org/wiki/History#Periods https://en.wikipedia.org/wiki/Philosophy https://en.wikipedia.org/wiki/Moral_injury") - with gr.Tab("Old Ideas to merge"): - gr.HTML("Random Scenario / Song to 'full game' manual or auto is end goal ") - gr.HTML("Componets (outside Code Support for Config): Decisions (and context explanation), Nested Sections, Media (Especially to affect decisions), Replayability (GTA and Tekken type mechanics in text form), Theme integration (Modified Varibles that affect UI or config order)") - gr.HTML("Existing Games eg. GTA Heists - Same Map with overlapping branching narratives, Battlefront - Elites amongst Commoners, Tekken Casino (one mistake = 1/2 or 1/3 of your Resources) and Turn based: 'Tactics' type nintendo games, Chess (and any other tile based game) ") - gr.HTML("Existing Game Rules for text - Cyberpunk RED, ") - gr.HTML("Community playthrough = Tally of players choices, Random item placed in a random location - first person to get it wins, Survival by location or characters met") - gr.HTML("Some Kinds of game skeletons ideas - Timelines, Graph as State machine paths, Economy ecosystem") - gr.HTML("One prompt to be used to test models -
Please make 10 python lists for the types of media files and their purposes in a game and then use those lists to random generate a timeline of 20 items when the function is called
Great next suggest ways to improve this function to create better timelines") - with gr.Tab("Main areas of considerations"): - gr.HTML("") - with gr.Tab("Structural Inspirations"): - gr.HTML("GTA Heists - Replayability and stakes, Tekken - 2/3 mistakes = lost round ") - gr.HTML("Sports Scores, ") - with gr.Tab("Themes"): - gr.HTML("") - with gr.Tab("Current Engine Defects"): - gr.HTML("All realtime events - Text still needs realtime as well") - with gr.Tab("Inventory and Skill Support"): - gr.HTML("Each decision affects Skills or inventory") - with gr.Tab("NPC Support"): - gr.HTML("Shared timeline that the player interfere with") - with gr.Tab("Economics Support"): - gr.HTML("Style Idea for a Basic Idea - Endless Economy (Tiny Tower as well) - Paperclip maximiser and inspirations - https://huggingface.co/spaces/osanseviero/TheMLGame") - - with gr.Row(): - with gr.Column(scale=1): - gr.HTML("""Main End Goal is Rapid prototypes for education and training purposes eg. Degree of the future is game score
A way to prototype basic non-3D parts of a game while trying to understand where models can streamline workflow
""") - with gr.Accordion("Temporary Asset Management Assist - click to open", open=False): - gr.HTML("Make Files and Text ideas for the field and paste
When Space is restarted it will clear - zip export and import will be added later") - with gr.Accordion("Upload Files for config"): - gr.Markdown("# Media Saver and Explorer (refresh file list to be resolved - for now upload all files and reload the space - they persist as long as the space creator doesnt reset/update the space - will add manual clear options later)") - with gr.Tab("Upload Files"): - file_input = gr.File(label="Choose File to Upload") - save_output = gr.Textbox(label="Upload Status") - - with gr.Tab("File Explorer"): + + with gr.Tab("Leaveraging Machine Learning"): # , open=False): + gr.HTML("https://www.reddit.com/r/singularity/comments/1fhbipv/some_video_games_made_entirely_by_o1preview_and/") + gr.HTML("""Artificial Analysis""") + with gr.Tab("Chat UIs - Free Tier"): + gr.HTML("""Hugging Face Chat""") + gr.HTML("You can turn any space into a tool for huggingchat and the default image tool can do 5-10 secs per image - paste the current description and ask for images") + gr.HTML("Huggingface chat supports - State Management (Threads), Image Generation and editing, Websearch, Document parsing (PDF?), Assistants and larger models than zero gpu can support in July 2024 (Unquantised 30B and above)") + gr.HTML("ChatGPT is good for multiple images in one image - eg game maps and for this space all decisions in one picture") + gr.HTML("Gemini") + gr.HTML("Claude - Coding") + gr.HTML("Existing Assistants to use and planning custom assistants placeholder") + with gr.Tab("Leveraging ZeroGPU Attempt"): + gr.HTML("https://huggingface.co/posts/cbensimon/747180194960645
https://github.com/NexaAI/Awesome-LLMs-on-device/
13092024 - https://www.reddit.com/r/LocalLLaMA/comments/1fg3bin/6_months_out_of_date_what_has_changed/") + + with gr.Accordion("Seperate interfaces I want to mix and space restraint management"): + gr.HTML("Text generation (RAG, Front End prompt engineering, Streaming output, Access to contextual generation accross the other interfaces)
Image generation (prompt only, context guided generation)
") + gr.HTML("Tracking models to build with first to save money and privacy - https://github.com/NexaAI/Awesome-LLMs-on-device/, https://huggingface.co/gpt-omni/mini-omni https://www.reddit.com/r/LocalLLaMA/comments/1f84p1g/an_opensource_voicetovoice_llm_miniomni/, ") + + model_name = gr.State(modelname) + gr.Markdown(f"# Language Model with RAG and Model Switching, and FLUX Integration") + gr.Markdown("This demo allows you to switch between different Qwen models and use Retrieval-Augmented Generation (RAG).") + gr.Markdown("**Note:** Model switching is intended for testing output quality. Due to GPU limitations, speed differences may not be apparent. Models requiring over 50GB to load will likely not work.") + gr.Markdown("Need to add support for switching models and loading GGUF and GPTQ and BNB") + gr.Markdown("57b MOE takes 6min to load and gets workload evicted - storage limit over 100G") + + with gr.Tab("Text Generation"): + with gr.Row(): + with gr.Column(): + model_dropdown = gr.Dropdown(choices=modelnames, value=modelname, label="Select Model") + current_model_info = gr.Markdown(f"Current model: {modelname}") + current_model_info2 = gr.Interface(lambda x: f"Current model: {lastmodelnameinloadfunction[0]} ({lastmodelnameinloadfunction[1]}) (tokeniser = {lastmodelnameinloadfunction[2]})", inputs=None, outputs=["markdown"], description="Check what was last loaded (As the space has memory and I havent figured how spaces work enough eg. how does multiple users affect this)") # gr.Markdown(f"Current model: {lastmodelnameinloadfunction}") + gr.HTML("Need to figure out my test function calling for groq-8b as it doesnt seem to answer chat properly - will need a seperate space - eg. letter counting, plural couting, using a function as form for llm to fill (like choosing which model and input parameters for media in game)?") + prompt = gr.Textbox(lines=2, placeholder="Enter your prompt here...") + stream_checkbox = gr.Checkbox(label="Enable streaming") + rag_checkbox = gr.Checkbox(label="Enable RAG") + submit_button = gr.Button("Generate") + clear_models_button = gr.Button("Clear All Models") + loaded_models_button = gr.Button("Load Model List") + model_list_input = gr.Textbox(lines=2, placeholder="Enter model names separated by commas") + + with gr.Column(): + with gr.Tab("Current Response"): + output = gr.Textbox(lines=10, label="Generated Response") + tokens_per_second = gr.Textbox(label="Tokens per Second") + ram_usage = gr.Textbox(label="RAM Usage") + doc_references = gr.Textbox(label="Document References") + with gr.Tab("All Responses So far"): + gr.Markdown("As we want a iterative process all old responses are saved for now - will figure how to make per user solution -- need some kind of hook onto the loading a space to assign random usercount with timestamp") + gr.Interface(format_output_dict, inputs=None, outputs=["textbox"]) + with gr.Tab("Image Generation"): + gr.HTML("Real Time Image Gen in this space is next aim like sd versions and https://huggingface.co/spaces/KingNish/Realtime-FLUX") + gr.Interface(generate_image, inputs=["text"], outputs=["text", "text", "image"], description="Current image") + #image_output = gr.Image(label="Generated Image (FLUX)") + + + model_dropdown.change( + model_change_handler, + inputs=[model_dropdown], + outputs=[model_name, current_model_info, output] + ) + + submit_button.click( + generate_response, + inputs=[prompt, rag_checkbox, stream_checkbox], + outputs=[output, tokens_per_second, ram_usage, doc_references], + ) + + clear_models_button.click( + clear_all_models, + inputs=[], + outputs=[output] + ) + + loaded_models_button.click( + loaded_model_list, + outputs=[output] + ) + + with gr.Tab("Preview APIs / Gradio Client"): + gr.HTML("July 2024 - Gemini, Cohere and Groq rate limit free APIs") + gr.HTML("https://modal.com/pricing ($30 a month?), https://fireworks.ai/pricing ($1 free trial), https://sambanova.ai/fast-api?api_ref=577164 (free rate limitied api - llama 405B 114 toks/s)") + gr.Interface(fn=TestGradioClientrandommodel, inputs="text", outputs="markdown", description="Single response test of gradio client - Gemma 2 9b for test as api, Use for testing like using a space and duplicate for heavy testing") + + + with gr.Tab("Everything Else"): + #with gr.Accordion("Game Customisation Tools", open=False): + + with gr.Tab("Portability"): + with gr.Tab("Export"): + with gr.Row(): + with gr.Column(): + gr.Markdown("""# Suggestions from Llama 405b through Lambdachat + +To create 2D and 3D versions of your 1D text-based game, you'll need to expand your game engine to support additional features. Here's a high-level overview of the steps you can follow: + +1. For 2D (top-down traversal): + +a. Create a grid-based system: Instead of just having a linear sequence of decisions, create a 2D grid that represents the game world. Each cell in the grid can represent a location in the game world, and the player can move between adjacent cells. + +b. Implement movement: Allow the player to move in four directions (up, down, left, right) by updating their position on the grid. You can use input commands like "go north", "go south", "go east", and "go west". + +c. Add obstacles and interactive objects: Place obstacles (e.g., walls, locked doors) and interactive objects (e.g., keys, items) on the grid. The player will need to navigate around obstacles and interact with objects to progress in the game. + +d. Update game logic: Modify your game's logic to account for the player's position on the grid and any interactions with objects or NPCs. + +2. For 3D: + +a. Add a third dimension: Expand your 2D grid to a 3D grid by adding a new dimension (e.g., height). This will allow you to create multi-level environments with stairs, elevators, or other vertical traversal methods. + +b. Implement movement in 3D: Extend your movement system to allow the player to move up and down in addition to the four cardinal directions. You can use input commands like "go up" and "go down". + +c. Update game logic for 3D: Modify your game's logic to account for the player's position in 3D space, as well as any interactions with objects or NPCs that may occur at different heights. + +d. Add 3D-specific features: Consider adding features that take advantage of the 3D environment, such as jumping, falling, or flying. + +In both cases, you'll need to update your game's user interface to accommodate the additional dimensions. This may involve displaying a top-down view of the game world, rendering a 3D environment, or providing additional text descriptions to convey the player's surroundings. + +Keep in mind that creating 2D and 3D games will require more complex programming and design compared to a 1D text-based game. You may need to learn new programming concepts and tools to achieve your goals. + - file_explorer = gr.FileExplorer( - root_dir=SAVE_DIR, - glob="*.*", - file_count="single", - height=300, - label="Select a file to view" - ) - with gr.Row(): - refresh_button = gr.Button("Refresh", scale=1) - view_button = gr.Button("View File") - image_output = gr.Image(label="Image Output", type="pil") - audio_output = gr.Audio(label="Audio Output") - video_output = gr.Video(label="Video Output") - error_output = gr.Textbox(label="Error") - - file_input.upload( - save_file, - inputs=file_input, - outputs=[save_output, file_explorer, file_input] - ) +# More ideas for expansion of '1d' ideas + +Expanding a 1D text-based game into 2D and 3D versions can introduce various new gameplay elements and features. Here are 10 additional points for each expansion, assuming we can debug and code any idea we want: - view_button.click( - view_file, - inputs=file_explorer, - outputs=[image_output, audio_output, video_output, error_output] - ) +2D Expansion: - refresh_button.click( - refresh_file_explorer, - outputs=file_explorer - ) +1. Add a mini-map: Implement a mini-map feature that displays the player's current position and the surrounding area, making navigation more intuitive. - with gr.Tab("Batch add files to config"): - gr.HTML("Placeholder for Config parser to allow dropdowns for the media parts of the config inserted to make assigning media quick") - gr.HTML("Placeholder for Config parser to allow for current zerospace creation and placement into the config (LLM can give list of media but still have to figure out workflow from there)") +2. Introduce NPCs with movement: Create non-player characters (NPCs) that can move around the grid, adding life to the game world and offering new interaction opportunities. - gr.HTML("Placeholder for clearing uploaded assets (public space and temporary persistence = sharing and space problems)") - with gr.Accordion("My Previous Quick Config Attempts", open=False): - for experimetal_config_name, experimetal_config in ExampleGameConfigs.items(): - with gr.Tab(experimetal_config_name): - gr.Code(json.dumps(experimetal_config, default=lambda o: o.__dict__, indent=2), label=experimetal_config_name) #str(experimetal_config) - - - with gr.Column(scale=5): - with gr.Tab("Test and Edit Config"): - with gr.Tab("Semi-Auto - Edit config while playing game"): - gr.HTML("-- Incomplete -- Current problem is passing values from rendered items to the config box
Need a way have dropdowns for the filelist and transitions eg. changing transitions must auto update choices
Config to components has hardcoded variables based on the auto gen so changes break it") - with gr.Column(scale=1): - gr.Markdown("# Debugging") - with gr.Row(): - with gr.Column(): - ewpwaerror_box = gr.Textbox(label="Path Errors", lines=4, value=path_errors) - - - with gr.Accordion("Generate a new config"): - with gr.Accordion("Lists for config - only linear path for now", open=False): - gr.Markdown("# Story and Timeline Generator") - gr.Markdown("Click the button to generate a random timeline and story based on UI elements and story events.
Ask an LLM to use this to write a story around") - #with gr.Row(): - #ewpgame_structure_output_text_with_media = gr.Code(language="json") - #ewpgame_structure_output_text = gr.Code(language="json") - with gr.Row(): - ewptimeline_output_with_assets = gr.Textbox(label="Timeline with Assets Considered", lines=20) - #ewptimeline_output = gr.Textbox(label="Timeline (Order might be different for now)", lines=20) - with gr.Column(): - ewptimeline_output_text = gr.Textbox(label="Random Suggestions", lines=10) - ewptimeline_selected_lists_text = gr.Textbox(label="Selected Idea Lists for Inspiration", lines=2) - ewpstory_output = gr.Textbox(label="Generated Story (Order might be different for now)", lines=20) - with gr.Row(): - ewpgenerate_no_story_timeline_points = gr.Slider(minimum=1, value=10, step=1, maximum=30, label="Choose the amount of story timeline points") - ewpgenerate_no_ui_timeline_points = gr.Slider(minimum=1, value=10, step=1, maximum=30, label="Choose the amount of ui timeline points") - #ewpgenerate_no_media_timeline_points = gr.Slider(minimum=1, value=5, step=1, maximum=30, label="Choose the amount of media timeline points") - #ewpgenerate_with_media_check = gr.Checkbox(label="Generate with media", value=True) - with gr.Row(): - ewptimeline_num_lists_slider = gr.Slider(minimum=1, maximum=len(all_idea_lists), step=1, label="Number of Lists to Consider", value=3) - ewptimeline_items_per_list_slider = gr.Slider(minimum=1, maximum=10, step=1, label="Items per List", value=3) - ewptimeline_include_existing_games = gr.Checkbox(label="Include Existing Game Inspirations", value=True) - ewptimeline_include_multiplayer = gr.Checkbox(label="Include Multiplayer Features", value=True) - - ewpgenerate_button = gr.Button("Generate Story and Timeline") - - ewpwacustom_config = gr.Textbox(label="Custom Configuration (JSON)", lines=4) #value=json.dumps(all_states, default=lambda o: o.__dict__, indent=2), lines=4) #Commented out due to initial load issues - ewpwacustom_configbtn = gr.Button("Load Custom Config") - - with gr.Row(): - with gr.Column(scale=1): - with gr.Group(): - gr.Markdown("# Text-based Adventure Game") - - ewpwadescription = gr.Textbox(label="Current Situation", lines=4, value=initgameinfo[0]) - ewpwamediabool = gr.State(value=True) - ewpwamedia = gr.State(["testmedia/Stable Audio - Raindrops, output.wav"]) - - @gr.render(inputs=ewpwamedia) - def dynamic_with_media(media_items): - print(media_items) - with gr.Group() as ewpwamediagrouping: - gr.HTML("Placeholder to load all media tests - still need to test clearing media on ") - if media_items == []: - gr.Markdown("No media items to display.") - else: - for item in media_items: - render = create_media_component(item) - - return ewpwamediagrouping - - ewpwachoices = gr.Radio(label="Your Choices", choices=initgameinfo[1]) - ewpwasubmit_btn = gr.Button("Make Choice") - ewpwagame_log = gr.Textbox(label="Game Log", lines=20, value=initgameinfo[2]) - ewpwagame_session = gr.State(value=initgameinfo[3]) - ewpwasubmit_btn.click( - make_choice, - inputs=[ewpwachoices, ewpwagame_session, ewpwamediabool], - outputs=[ewpwadescription, ewpwachoices, ewpwagame_log, ewpwagame_session, ewpwamedia] - ) - - ewpwacustom_configbtn.click( - load_game, - inputs=[ewpwacustom_config, ewpwamediabool], - outputs=[ewpwaerror_box, ewpwagame_log, ewpwadescription, ewpwachoices, ewpwacustom_config, ewpwagame_session, ewpwamedia] - ) - with gr.Column(scale=1): - @gr.render(inputs=ewpwacustom_config) #ewpgame_structure_output_text_with_media - def update(ewpwacustom_config): - return show_elements_json_input(ewpwacustom_config) - - ewpgenerate_button.click(generate_story_and_timeline, inputs=[ewpgenerate_no_story_timeline_points, ewpgenerate_no_ui_timeline_points, ewptimeline_num_lists_slider, ewptimeline_items_per_list_slider, ewptimeline_include_existing_games, ewptimeline_include_multiplayer], outputs=[ewptimeline_output_with_assets, ewpstory_output, ewpwacustom_config, ewptimeline_output_text, ewptimeline_selected_lists_text]) #ewptimeline_output_with_assets, ewptimeline_output, ewpstory_output, ewpwacustom_config, ewpgame_structure_output_text]) #ewpgame_structure_output_text_with_media, ewpgame_structure_output_text]) +3. Develop a dialogue system: Design a dialogue system that allows players to engage in conversations with NPCs, further enriching the game's narrative. - with gr.Tab("Manual - Config With Assets"): - gr.HTML("Placeholder as not complete yet (3D not supported, and time (esp need for audio)") - with gr.Row(): - with gr.Column(scale=2): - gr.Markdown("# Text-based Adventure Game") - - wadescription = gr.Textbox(label="Current Situation", lines=4, value=initgameinfo[0]) - wamediabool = gr.State(value=True) - wamedia = gr.State(["testmedia/Stable Audio - Raindrops, output.wav"]) - - @gr.render(inputs=wamedia) - def dynamic_with_media(media_items): - print(media_items) - with gr.Group() as wamediagrouping: - gr.HTML("Placeholder to load all media tests - still need to test clearing media on ") - if media_items == []: - gr.Markdown("No media items to display.") - else: - for item in media_items: - render = create_media_component(item) - - return wamediagrouping - - wachoices = gr.Radio(label="Your Choices", choices=initgameinfo[1]) - wasubmit_btn = gr.Button("Make Choice") - wagame_log = gr.Textbox(label="Game Log", lines=20, value=initgameinfo[2]) - wagame_session = gr.State(value=initgameinfo[3]) - wasubmit_btn.click( - make_choice, - inputs=[wachoices, wagame_session, wamediabool], - outputs=[wadescription, wachoices, wagame_log, wagame_session, wamedia] - ) - with gr.Column(scale=1): - gr.Markdown("# Debugging") - waerror_box = gr.Textbox(label="Path Errors", lines=4, value=path_errors) - with gr.Accordion("Config (Game Spoiler and Example for llm to remix)", open=False): - wacustom_config = gr.Textbox(label="Custom Configuration (JSON)", value=json.dumps(all_states, default=lambda o: o.__dict__, indent=2), lines=8) - wacustom_configbtn = gr.Button("Load Custom Config") - - wacustom_configbtn.click( - load_game, - inputs=[wacustom_config, wamediabool], - outputs=[waerror_box, wagame_log, wadescription, wachoices, wacustom_config, wagame_session, wamedia] - ) - - - with gr.Tab("HF - Asset Generation Considerations"): - with gr.Accordion("Some Idea / Inspiration Sources / Demos", open=False): - with gr.Row(): - gr.HTML("Licenses for the spaces still to be evaluated - June 2024
Users to follow with cool spaces -
https://huggingface.co/osanseviero - https://huggingface.co/spaces/osanseviero/TheMLGame
https://huggingface.co/jbilcke-hf
https://huggingface.co/dylanebert
https://huggingface.co/fffiloni
https://huggingface.co/artificialguybr
https://huggingface.co/radames
https://huggingface.co/multimodalart, ") - gr.HTML("Some Youtube Channels to keep up with updates

https://www.youtube.com/@lev-selector
https://www.youtube.com/@fahdmirza/videos") - gr.HTML("Social media that shows possiblities

https://www.reddit.com/r/aivideo/ https://www.reddit.com/r/StableDiffusion/ https://www.reddit.com/r/midjourney/ https://x.com/blizaine https://www.reddit.com/r/singularity/comments/1ead7vp/not_sure_if_this_is_the_right_place_to_post_but_i/ https://www.reddit.com/r/singularity/comments/1ebpra6/the_big_reveal_book_trailer_made_with_runway_gen3/ https://www.reddit.com/r/LocalLLaMA/comments/1e3aboz/folks_with_one_24gb_gpu_you_can_use_an_llm_sdxl/") - gr.HTML("Currently - need to create then save to pc then reupload to use here in test tab") - gr.HTML("Whole game engine in a space? - https://huggingface.co/spaces/thomwolf/test_godot_editor

https://huggingface.co/godot-demo https://huggingface.co/spaces/thomwolf/test_godot") - with gr.Tab("Text-based"): - gr.HTML("Some Benchmark Leaderboards - https://huggingface.co/spaces/allenai/ZebraLogic | https://huggingface.co/spaces/allenai/WildBench https://scale.com/leaderboard https://livebench.ai") - with gr.Accordion("LLM HF Spaces/Sites (Click Here to Open) - Ask for a story and suggestions based on the autoconfig", open=False): - with gr.Row(): - linktochat = gr.Dropdown(choices=["--Function Calling--", "https://groq-demo-groq-tool-use.hf.space", - "--Multiple Models--", "https://lmsys-gpt-4o-mini-battles.hf.space", "https://labs.perplexity.ai/", "https://chat.lmsys.org", "https://sdk.vercel.ai/docs", "https://cyzgab-catch-me-if-you-can.hf.space", - "--70B and above--", "https://llamameta-llama3-1-405b.static.hf.space", "https://qwen-qwen-max-0428.hf.space", "https://cohereforai-c4ai-command-r-plus.hf.space", "https://qwen-qwen1-5-110b-chat-demo.hf.space", "https://snowflake-snowflake-arctic-st-demo.hf.space", "https://databricks-dbrx-instruct.hf.space", "https://qwen-qwen1-5-72b-chat.hf.space", - "--20B and above--", "https://gokaygokay-gemma-2-llamacpp.hf.space", "https://01-ai-yi-34b-chat.hf.space", "https://cohereforai-c4ai-command-r-v01.hf.space", "https://ehristoforu-mixtral-46-7b-chat.hf.space", "https://mosaicml-mpt-30b-chat.hf.space", - "--7B and above--", "https://vilarin-mistral-nemo.hf.space", "https://arcee-ai-arcee-scribe.hf.space", "https://vilarin-llama-3-1-8b-instruct.hf.space", "https://ysharma-chat-with-meta-llama3-8b.hf.space", "https://qwen-qwen1-5-moe-a2-7b-chat-demo.hf.space", "https://deepseek-ai-deepseek-coder-7b-instruct.hf.space", "https://osanseviero-mistral-super-fast.hf.space", "https://artificialguybr-qwen-14b-chat-demo.hf.space", "https://huggingface-projects-llama-2-7b-chat.hf.space", - "--1B and above--", "https://huggingface.co/spaces/eswardivi/Phi-3-mini-128k-instruct", "https://eswardivi-phi-3-mini-4k-instruct.hf.space", "https://stabilityai-stablelm-2-1-6b-zephyr.hf.space", - "--under 1B--", "unorganised", "https://ysharma-zephyr-playground.hf.space", "https://huggingfaceh4-zephyr-chat.hf.space", "https://ysharma-explore-llamav2-with-tgi.hf.space", "https://huggingfaceh4-falcon-chat.hf.space", "https://uwnlp-guanaco-playground-tgi.hf.space", "https://stabilityai-stablelm-tuned-alpha-chat.hf.space", "https://mosaicml-mpt-7b-storywriter.hf.space", "https://huggingfaceh4-starchat-playground.hf.space", "https://bigcode-bigcode-playground.hf.space", "https://mosaicml-mpt-7b-chat.hf.space", "https://huggingchat-chat-ui.hf.space", "https://togethercomputer-openchatkit.hf.space"], label="Choose/Cancel type any .hf.space link here (can also type a link)'", allow_custom_value=True) - chatspacebtn = gr.Button("Use the chosen URL to load interface with a chat model. For sdk.vercel click the chat button on the top left. For lymsys / chat arena copy the link and use a new tab") - chatspace = gr.HTML("Chat Space Chosen will load here") - chatspacebtn.click(display_website, inputs=linktochat, outputs=chatspace) - with gr.Tab("NPCS"): - gr.HTML("For ideas on NPCS check: https://lifearchitect.ai/leta/, ") - with gr.Tab("Save files"): - gr.HTML("For Dynamic events overnight or when player is not active what can LLMS edit?

eg. Waiting for a letter from a random npc can be decided by the llm
eg. Improved Stats on certain days (eg. bitrthday)
Privacy
User Directed DLC eg. Rockstar Editor with local llm guide") - gr.HTML("Some ideas - In game websites eg. GTA esp stock markets, news; ") - gr.HTML("Placeholder for huggingface spaces that can assist - https://huggingface.co/nvidia/Nemotron-4-340B-Instruct (Purpose is supposed to be synthetic data generation), https://huggingface.co/spaces/gokaygokay/Gemma-2-llamacpp ") - gr.HTML("Placeholder for models small enough to run on cpu here in this space that can assist (9b and under)
initial floor for testing can be https://huggingface.co/spaces/Qwen/Qwen2-0.5B-Instruct, https://huggingface.co/spaces/Qwen/Qwen2-1.5b-instruct-demo, https://huggingface.co/spaces/stabilityai/stablelm-2-1_6b-zephyr, https://huggingface.co/spaces/IndexTeam/Index-1.9B, https://huggingface.co/microsoft/Phi-3-mini-4k-instruct") - with gr.Tab("Diagrams"): - gr.HTML("Claude 3.5 sonnet is very good with mermaid graphs - can used for maps, situational explanations") - with gr.Tab("Maths"): - gr.HTML("https://huggingface.co/spaces/AI-MO/math-olympiad-solver") - - with gr.Tab("Media Understanding"): - gr.HTML("NPC Response Engines? Camera, Shopkeeper, Companion, Enemies, etc.") - with gr.Accordion("Media understanding model Spaces/Sites (Click Here to Open)", open=False): - with gr.Row(): - linktomediaunderstandingspace = gr.Dropdown(choices=[ "--Weak Audio Understanding = Audio to text, Weak Video Understanding = Video to Image to Image Understanding", "https://skalskip-florence-2-video.hf.space", "https://kingnish-opengpt-4o.hf.space", - "--Video Understanding--", "https://ivgsz-flash-vstream-demo.hf.space", - "--Image Understanding--", "https://gokaygokay-florence-2.hf.space", "https://vilarin-vl-chatbox.hf.space", "https://qnguyen3-nanollava.hf.space", "https://skalskip-better-florence-2.hf.space", "https://merve-llava-interleave.hf.space", - "--Img-to-img Understanding--", "https://merve-draw-to-search-art.hf.space", - "--Image Understanding without conversation--", "https://epfl-vilab-4m.hf.space", "https://epfl-vilab-multimae.hf.space", "https://gokaygokay-sd3-long-captioner.hf.space" ], - label="Choose/Cancel type any .hf.space link here (can also type a link)'", allow_custom_value=True) - mediaunderstandingspacebtn = gr.Button("Use the chosen URL to load interface with a media understanding space") - mediaunderstandingspace = gr.HTML("Mdeia Understanding Space Chosen will load here") - mediaunderstandingspacebtn.click(display_website, inputs=linktomediaunderstandingspace, outputs=mediaunderstandingspace) - gr.HTML("Image Caption = https://huggingface.co/spaces/microsoft/Promptist (Prompt Lengthen) ") - - - with gr.Tab("Images"): - with gr.Accordion("Image Gen or Animation HF Spaces/Sites (Click Here to Open) - Have to download and upload at the the top", open=False): - # with gr.Tabs("General"): - with gr.Row(): - linktoimagegen = gr.Dropdown(choices=["--Text-Interleaved--", "https://ethanchern-anole.hf.space", - "--Hidden Image--", "https://ap123-illusiondiffusion.hf.space", - "--Panorama--", "https://gokaygokay-360panoimage.hf.space", - "--General--", "https://pixart-alpha-pixart-sigma.hf.space", "https://stabilityai-stable-diffusion-3-medium.hf.space", "https://prodia-sdxl-stable-diffusion-xl.hf.space", "https://prodia-fast-stable-diffusion.hf.space", "https://bytedance-hyper-sdxl-1step-t2i.hf.space", "https://multimodalart-cosxl.hf.space", "https://cagliostrolab-animagine-xl-3-1.hf.space", "https://stabilityai-stable-diffusion.hf.space", - "--Speed--", "https://radames-real-time-text-to-image-sdxl-lightning.hf.space", "https://ap123-sdxl-lightning.hf.space", - "--LORA Support--", "https://artificialguybr-artificialguybr-demo-lora.hf.space", "https://artificialguybr-studio-ghibli-lora-sdxl.hf.space", "https://artificialguybr-pixel-art-generator.hf.space", "https://fffiloni-sdxl-control-loras.hf.space", "https://ehristoforu-dalle-3-xl-lora-v2.hf.space", - "--Image to Image--", "https://gokaygokay-kolorsplusplus.hf.space", "https://lllyasviel-ic-light.hf.space", "https://gparmar-img2img-turbo-sketch.hf.space", - "--Upscaler--", "https://gokaygokay-tile-upscaler.hf.space", - "--Control of Pose--", "https://instantx-instantid.hf.space", "https://modelscope-transferanything.hf.space", "https://okaris-omni-zero.hf.space" - "--Control of Shapes--", "https://linoyts-scribble-sdxl-flash.hf.space", - "--Control of Text--", "" - "--Foreign Language Input--", "https://gokaygokay-kolors.hf.space"], label="Choose/Cancel type any .hf.space link here (can also type a link)'", allow_custom_value=True) - imagegenspacebtn = gr.Button("Use the chosen URL to load interface with a image generation model") - - imagegenspace = gr.HTML("Image Space Chosen will load here") - imagegenspacebtn.click(display_website, inputs=linktoimagegen, outputs=imagegenspace) - - linkstobecollectednoembed = "https://artgan-diffusion-api.hf.space", "https://multimodalart-stable-cascade.hf.space", "https://google-sdxl.hf.space", "https://visionmaze-magic-me.hf.space", "https://segmind-segmind-stable-diffusion.hf.space", "https://simianluo-latent-consistency-model.hf.space", - gr.HTML("Concept Art, UI elements, Static/3D Characters, Environments and Objects") - gr.HTML("Images Generation General (3rd Party) = https://www.craiyon.com/") - gr.HTML("Images Generation Posters with text - https://huggingface.co/spaces/GlyphByT5/Glyph-SDXL-v2") - gr.HTML("SVG Generation = Coding models / SOTA LLM ") - gr.HTML("Images Generation - Upscaling - https://huggingface.co/spaces/gokaygokay/Tile-Upscaler") - gr.HTML("Vision Models for descriptions
https://huggingface.co/spaces/gokaygokay/Florence-2
https://huggingface.co/spaces/vilarin/VL-Chatbox - glm 4v 9b
") - gr.HTML("Upscalers (save data transfer costs? highly detailed characters?) - https://huggingface.co/spaces/gokaygokay/AuraSR") - gr.HTML("Placeholder for huggingface spaces that can assist ") - gr.HTML("Placeholder for models small enough to run on cpu here in this space that can assist") - - with gr.Tab("Video"): - with gr.Accordion("Video Spaces/Sites (Click Here to Open)", open=False): - with gr.Row(): - linktovideogenspace = gr.Dropdown(choices=["--Genral--", "https://zheyangqin-vader.hf.space", "https://kadirnar-open-sora.hf.space", - "--Talking Portrait--", "https://fffiloni-tts-hallo-talking-portrait.hf.space", - "--Gif / ImgtoImg based video--", "https://wangfuyun-animatelcm-svd.hf.space", "https://bytedance-animatediff-lightning.hf.space", "https://wangfuyun-animatelcm.hf.space", "https://guoyww-animatediff.hf.space",], - label="Choose/Cancel type any .hf.space link here (can also type a link)'", allow_custom_value=True) - videogenspacebtn = gr.Button("Use the chosen URL to load interface with video generation") - videogenspace = gr.HTML("Video Space Chosen will load here") - videogenspacebtn.click(display_website, inputs=linktovideogenspace, outputs=videogenspace) - - gr.HTML("Cutscenes, Tutorials, Trailers") - gr.HTML("Portrait Video eg. Solo Taking NPC - https://huggingface.co/spaces/fffiloni/tts-hallo-talking-portrait (Image + Audio and combination) https://huggingface.co/spaces/KwaiVGI/LivePortrait (Non verbal communication eg. in a library, when running from a pursuer)") - gr.HTML("Placeholder for huggingface spaces that can assist - https://huggingface.co/spaces/KingNish/Instant-Video, https://huggingface.co/spaces/multimodalart/stable-video-diffusion, https://huggingface.co/spaces/multimodalart/stable-video-diffusion") - gr.HTML("Placeholder for models small enough to run on cpu here in this space that can assist") - gr.HTML("3rd Party / Closed Source - https://runwayml.com/
") - with gr.Tab("Animations (for lower resource use)"): - gr.HTML("Characters, Environments, Objects") - gr.HTML("Placeholder for huggingface spaces that can assist - image as 3d object in video https://huggingface.co/spaces/ashawkey/LGM") - gr.HTML("Placeholder for models small enough to run on cpu here in this space that can assist") - - with gr.Tab("Audio"): - with gr.Accordion("Audio Spaces/Sites (Click Here to Open)", open=False): - with gr.Row(): - linktoaudiogenspace = gr.Dropdown(choices=["General", "https://artificialguybr-stable-audio-open-zero.hf.space", "", - "--Talking Portrait--","https://fffiloni-tts-hallo-talking-portrait.hf.space"], - label="Choose/Cancel type any .hf.space link here (can also type a link)'", allow_custom_value=True) - audiogenspacebtn = gr.Button("Use the chosen URL to load interface with audio generation") - audiogenspace = gr.HTML("Audio Space Chosen will load here") - audiogenspacebtn.click(display_website, inputs=linktoaudiogenspace, outputs=audiogenspace) - gr.HTML("Music - Background, Interactive, Cutscene, Menu
Sound Effects - Environment, character, action (environmental triggered by user eg. gun), UI
Speech - Dialouge, narration, voiceover
The new render function means the Config can be made and iframe/api functions can be ordered as neccessary based on the part of the config that needs it to streamline workflows based on current state of config ") - gr.HTML("Placeholder for huggingface spaces that can assist") - gr.HTML("Audio Sound Effects - https://huggingface.co/spaces/artificialguybr/Stable-Audio-Open-Zero") - gr.HTML("Voices - Voice clone eg. actors part of your project - https://huggingface.co/spaces/tonyassi/voice-clone") - gr.HTML("Placeholder for models small enough to run on cpu here in this space that can assist") - gr.HTML("3rd Party / Closed Source - https://suno.com/
https://www.udio.com/") - - with gr.Tab("3D"): - with gr.Accordion("3D Model Spaces/Sites (Click Here to Open)", open=False): - with gr.Row(): - linktoThreedModel = gr.Dropdown(choices=["--Image prompt--", "https://vast-ai-charactergen.hf.space" - "--Video prompt--", "https://facebook-vggsfm.hf.space", - "--Text prompt--", "https://wuvin-unique3d.hf.space", "https://stabilityai-triposr.hf.space", "https://hysts-shap-e.hf.space", "https://tencentarc-instantmesh.hf.space", "https://ashawkey-lgm.hf.space", "https://dylanebert-lgm-mini.hf.space", "https://dylanebert-splat-to-mesh.hf.space", "https://dylanebert-multi-view-diffusion.hf.space"], label="Choose/Cancel type any .hf.space link here (can also type a link)'", allow_custom_value=True) - ThreedModelspacebtn = gr.Button("Use the chosen URL to load interface with a 3D model") - ThreedModelspace = gr.HTML("3D Space Chosen will load here") - ThreedModelspacebtn.click(display_website, inputs=linktoThreedModel, outputs=ThreedModelspace) - gr.HTML("Characters, Environments, Objects") - gr.HTML("Placeholder for huggingface spaces that can assist - https://huggingface.co/spaces/dylanebert/3d-arena") - gr.HTML("Closed Source - https://www.meshy.ai/") - - with gr.Tab("Fonts"): - gr.HTML("Style of whole game, or locations, or characters") - gr.HTML("Placeholder for huggingface spaces that can assist - there was a space that could make letter into pictures based on the prompt but I cant find it now") - gr.HTML("Placeholder for models small enough to run on cpu here in this space that can assist") - - with gr.Tab("Shaders and related"): - with gr.Accordion("'Special Effects' Spaces/Sites (Click Here to Open)", open=False): - with gr.Row(): - linktospecialeffectsgenspace = gr.Dropdown(choices=["--Distorted Image--", "https://epfl-vilab-4m.hf.space", "https://epfl-vilab-multimae.hf.space"], - label="Choose/Cancel type any .hf.space link here (can also type a link)'", allow_custom_value=True) - specialeffectsgenspacebtn = gr.Button("Use the chosen URL to load interface with special effects generation") - specialeffectsgenspace = gr.HTML("Special Effects Space Chosen will load here") - specialeffectsgenspacebtn.click(display_website, inputs=linktospecialeffectsgenspace, outputs=specialeffectsgenspace) - gr.HTML("Any output that is not understood by the common person can be used as special effects eg. depth map filters on images etc.") - gr.HTML("Post-processing Effects, material effects, Particle systems, visual feedback") - gr.HTML("Visual Effects - eg. explosion can turn all items white for a moment, losing conciousness blurs whole screen") - gr.HTML("Placeholder for models small enough to run on cpu here in this space that can assist") - - with gr.Tab("3rd Party - Asset Generation"): - gr.HTML("Image - https://www.midjourney.com/showcase") - gr.HTML("Audio - https://www.udio.com/home") - gr.HTML("Video - https://klingai.com/") - - with gr.Tab("Basic Game Engine Mechanics"): - gr.HTML("Placeholder for explanations of Player and Game Session") - with gr.Tab("LLM play testing"): - gr.HTML("LLM can read the contents in full and give critiques but they can also play the game if you make a api interface - gradio allows this in the form of gradio client but you can also reroute the user inputs to function calling") - - with gr.Tab("Custom JS Config Creator"): - gr.HTML("-- Incomplete -- Companion Space for zerogpu / client api workflow planning for a way to send a zip to the Basic Game Engine at the bottom of https://huggingface.co/spaces/KwabsHug/TestSvelteStatic (Also to test how much can be done majority on cpu)") - with gr.Tab("Simple Config Creator"): - inventory_items = gr.State([]) - skills_items = gr.State([]) - objectives_items = gr.State([]) - targets_items = gr.State([]) - - with gr.Tabs(): - with gr.TabItem("Inventory"): - inventory_type = gr.Textbox(label="Type") - inventory_name = gr.Textbox(label="Name") - inventory_description = gr.Textbox(label="Description") - add_inventory = gr.Button("Add Inventory Item") - inventory_textbox = gr.JSON(label="Inventory Items", value=[]) - - with gr.TabItem("Skills"): - skills_branch = gr.Textbox(label="Branch") - skills_name = gr.Textbox(label="Name") - skills_learned = gr.Dropdown(choices=["True", "False"], label="Learned") - add_skill_button = gr.Button("Add Skill") - skills_textbox = gr.JSON(label="Skills", value=[]) - - with gr.TabItem("Objectives"): - objectives_id = gr.Textbox(label="ID") - objectives_name = gr.Textbox(label="Name") - objectives_complete = gr.Dropdown(choices=["True", "False"], label="Complete") - add_objective_button = gr.Button("Add Objective") - objectives_textbox = gr.JSON(label="Objectives", value=[]) - - with gr.TabItem("Targets"): - targets_name = gr.Textbox(label="Name") - targets_x = gr.Textbox(label="X Coordinate") - targets_y = gr.Textbox(label="Y Coordinate") - targets_collisionType = gr.Textbox(label="Collision Type") - targets_collisiontext = gr.Textbox(label="Collision Text") - add_target_button = gr.Button("Add Target") - targets_textbox = gr.JSON(label="Targets", value=[]) - - with gr.TabItem("Placeholders for Modal Target"): - gr.HTML("Placeholder") - - with gr.TabItem("Placeholders for State Machine Modal Target"): - gr.HTML("Placeholder") - - with gr.TabItem("Placeholders for Background"): - gr.HTML("Placeholder") - - config_output = gr.JSON(label="Updated Configuration") - - @gr.render(inputs=[inventory_items, skills_items, objectives_items, targets_items]) #, outputs=config_output) - def aggregate_config(inventory, skills, objectives, targets): - config = default_config.copy() - config['inventory'] = inventory - config['skills'] = skills - config['objectives'] = objectives - config['targets'] = targets - return config - - add_inventory.click(add_inventory_item, inputs=[inventory_items, inventory_type, inventory_name, inventory_description], outputs=inventory_textbox) - add_inventory.click(aggregate_config, inputs=[inventory_items, skills_items, objectives_items, targets_items], outputs=config_output) - - add_skill_button.click(add_skill, inputs=[skills_items, skills_branch, skills_name, skills_learned], outputs=skills_textbox) - add_skill_button.click(aggregate_config, inputs=[inventory_items, skills_items, objectives_items, targets_items], outputs=config_output) - - add_objective_button.click(add_objective, inputs=[objectives_items, objectives_id, objectives_name, objectives_complete], outputs=objectives_textbox) - add_objective_button.click(aggregate_config, inputs=[inventory_items, skills_items, objectives_items, targets_items], outputs=config_output) - - add_target_button.click(add_target, inputs=[targets_items, targets_name, targets_x, targets_y, targets_collisionType, targets_collisiontext], outputs=targets_textbox) - add_target_button.click(aggregate_config, inputs=[inventory_items, skills_items, objectives_items, targets_items], outputs=config_output) - - with gr.Tab("Advanced Config Creator"): - gr.HTML("Config with More than text and images") - - with gr.Tab("LLM/Robotics as custom controllers"): - gr.HTML("Controls changed the scope of the game eg. mouse vs keyboard vs console controller vs remote vs touch screen
LLM can be vision/surveilance based controler (eg. MGS/GTA camera gauged by an actual camera in real life) or it can be a companion (offline/off console game progrssion ideas)") - gr.HTML("https://github.com/Shaka-Labs/ACT $250 imitation learning/teleoperation - eg. a win loss result alert / NPC 'scout' telling you go or stay") - gr.HTML("https://huggingface.co/posts/thomwolf/809364796644704") - gr.HTML("Robotics - https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/ https://huggingface.co/lerobot https://github.com/tonyzhaozh/aloha https://github.com/Shaka-Labs/ACT https://github.com/OpenTeleVision/TeleVision https://www.stereolabs.com/ ") - with gr.Tab("Existing Game Developemnt Resources"): - gr.HTML("https://develop.games/#nav-tools-engine ") - with gr.Tab("Other Considerations"): - with gr.Tab("General"): - gr.HTML("https://huggingface.co/docs/hub/api - daily papers is an endpoint so you can turn paper abstract into games with the help of LLM") - - gr.HTML("Experiment for https://huggingface.co/spaces/ysharma/open-interpreter/blob/main/app.py inplementation with gradio client api") - - gr.HTML("https://huggingface.co/spaces/HuggingFaceTB/SmolLM-135M-Instruct-WebGPU") - - gr.HTML("Useful Spaces and links: https://huggingface.co/spaces/artificialguybr/Stable-Audio-Open-Zero https://huggingface.co/spaces/stabilityai/TripoSR https://huggingface.co/spaces/wangfuyun/AnimateLCM-SVD https://huggingface.co/spaces/multimodalart/face-to-all https://huggingface.co/spaces/facebook/MusicGen https://huggingface.co/spaces/Doubiiu/tooncrafter") - - gr.HTML("langchain docs as awareness for alot of the integration use cases and providers that are possible - https://python.langchain.com/v0.2/docs/integrations/tools/") - - gr.HTML("https://huggingface.co/spaces/linoyts/scribble-sdxl-flash as map planner") - - gr.HTML("---------------------------------------Gameplay Ideas-------------------------------") - gr.HTML("https://huggingface.co/spaces/Lin-Chen/ShareCaptioner-Video - game use example police questions a event with multiple eye witnesses needs to give as close to the caption description to win") - with gr.Tab("State management through huggingface?"): - gr.HTML("Huggingface as the login provider? - https://huggingface.co/spaces/Wauplin/gradio-user-history/tree/main https://huggingface.co/spaces/AP123/IllusionDiffusion https://huggingface.co/docs/hub/en/spaces-oauth https://huggingface.co/docs/hub/en/oauth, persistent storage - https://huggingface.co/docs/hub/en/spaces-storage") - with gr.Tab("Finetuning options"): - gr.HTML("https://docs.mistral.ai/guides/finetuning/") - gr.HTML("Unsloth and Colab? - https://github.com/unslothai/unsloth https://huggingface.co/unsloth
Mistral Nemo Base - https://huggingface.co/unsloth/Mistral-Nemo-Base-2407 - https://colab.research.google.com/drive/17d3U-CAIwzmbDRqbZ9NnpHxCkmXB6LZ0?usp=sharing
Llama 3 8B https://huggingface.co/unsloth/llama-3-8b-Instruct-bnb-4bit") - gr.HTML("Price - https://openpipe.ai/pricing") - with gr.Tab("Backend and/or Hosting?"): - gr.HTML("Deployemnt options - https://huggingface.co/SpacesExamples", "https://huggingface.co/templates") - gr.HTML("Prototyping and freemium
free api
HF Pro subscription") - gr.HTML("GPU (Data privacy) = No Rate limits? - https://replicate.com/pricing, https://lambdalabs.com/service/gpu-cloud https://huggingface.co/pricing#endpoints https://tensordock.com/cloud-gpus", "https://massedcompute.com/home/pricing/" ) - gr.HTML("Speed - Groq, SambaNova, https://www.etched.com/announcing-etched ") - gr.HTML("Price - Coding - https://aider.chat/docs/leaderboards/ - https://www.deepseek.com/ 0.3 per million - is this per token or chinese character as that means converting code to chinese if possible can save api cost?") - gr.HTML("Llama 3.1 405B - https://ai.meta.com/blog/meta-llama-3-1/ https://replicate.com/meta/meta-llama-3.1-405b-instruct https://fireworks.ai/pricing https://www.ibm.com/products/watsonx-ai/foundation-models") - with gr.Tab("Some Interesting Git Repos"): - gr.HTML("https://github.com/NVIDIA/Megatron-LM https://github.com/OpenGVLab/EfficientQAT https://github.com/evintunador/minLlama3/blob/main/model.py https://github.com/evintunador/micro-GPT-sandbox") - with gr.Tab("Old Ideas"): - gr.HTML("""
Main ideas for this space is (June 2024) (Custom component planning?):
-
-
We can generate almost any media data and more
-
A program exist around data
-
Time moves in a straight so all considerations are flattend by the nature of time
-
llms good at short questions
-
HF + Gradio allows for api use so this my prototype tool for tool use test
-
""") - - with gr.Tab("Asset loading test"): - gr.HTML("SDXL (linoyts/scribble-sdxl-flash), SVD and Stable Audio used for the test assets (For commercial use need a licence)
testmedia/") - with gr.Row(): - gr.Image(value="testmedia/Flash scribble SDXL - random squiggles as roads.webp") - gr.Video(value="testmedia/SVD - random squiggles as roads video 004484.mp4") - gr.Audio(value="testmedia/Stable Audio - Raindrops, output.wav") - gr.HTML(TestmedialoadinHTML) # imported from relatively_constant_variables +4. Implement a day-night cycle: Add a day-night cycle that affects gameplay, such as certain events or NPCs only being available during specific times. + +5. Design puzzles: Create grid-based puzzles that players must solve to progress in the game, such as pushing blocks to activate switches or navigating mazes. + +6. Add character customization: Allow players to customize their character's appearance and attributes, giving them a sense of ownership over their avatar. + +7. Introduce a crafting system: Implement a crafting system that enables players to combine items found in the game world to create new, more powerful items. + +8. Add weather effects: Introduce weather effects like rain, snow, or fog that can impact gameplay, such as reducing visibility or making certain areas inaccessible. + +9. Create a quest system: Design a quest system with various objectives for players to complete, providing a sense of progression and purpose. + +10. Develop a party system: Allow players to recruit NPCs to join their party, assisting them in combat and providing unique abilities. + +3D Expansion: + +1. Add camera controls: Implement camera controls that allow players to rotate and zoom their view of the 3D game world, providing greater flexibility in exploration. + +2. Introduce platforming elements: Add platforming elements like jumping, climbing, and ledge-grabbing to enhance traversal in the 3D environment. + +3. Develop a physics system: Create a physics system that governs object interactions, enabling realistic behaviors like collisions, gravity, and buoyancy. + +4. Implement destructible environments: Design destructible environments that react to player actions, such as breaking walls or collapsing structures. + +5. Add aerial gameplay: Introduce flying mounts or vehicles that allow players to explore the game world from the air, opening up new traversal possibilities. + +6. Create underwater sections: Design underwater areas with unique gameplay mechanics, such as swimming, diving, and underwater combat. + +7. Introduce mounted combat: Allow players to engage in combat while riding mounts, adding a new strategic layer to battles. + +8. Develop a dynamic weather system: Create a dynamic weather system that affects gameplay in real-time, such as strong winds impacting movement or lightning strikes causing damage. + +9. Implement a stealth system: Design a stealth system that enables players to avoid detection by enemies, using cover, distractions, and silent takedowns. + +10. Add virtual reality support: Implement virtual reality support, allowing players to fully immerse themselves in the 3D game world using VR headsets and motion controls. + +Remember that these ideas can be mixed and matched to create unique gameplay experiences tailored to your vision for the game. + +# More ideas for '1d' game idea + +1D text-based games have their own unique strengths and limitations. Here are some ideas that can be best explored or are exclusive to 1D text-based games: + +1. Focus on narrative: 1D text-based games can focus primarily on storytelling, allowing for rich, branching narratives without the need for complex graphics or animations. + +2. Emphasize imagination: With no visual elements, 1D text-based games rely on the player's imagination to visualize the game world, making the experience more personal and engaging. + +3. Develop complex dialogue trees: In a 1D text-based game, you can create intricate dialogue trees with multiple choices and consequences, allowing for deep character interactions. + +4. Create puzzles based on text manipulation: Design puzzles that involve manipulating or deciphering text, such as riddles, word scrambles, or cipher-based challenges. + +5. Implement a text-based inventory system: Create an inventory system that relies on text descriptions of items, allowing players to use their imagination to determine how to use or combine objects. + +6. Explore experimental storytelling techniques: 1D text-based games can experiment with unconventional narrative structures, such as non-linear storytelling, interactive fiction, or second-person narration. + +7. Focus on resource management: Implement resource management mechanics that rely on text descriptions, such as managing a character's hunger, thirst, or fatigue levels. + +8. Develop a text-based skill tree: Create a skill tree system that uses text descriptions to convey character progression and abilities, allowing players to make meaningful choices about their character's development. + +9. Create a text-based AI companion: Design an AI companion that communicates with the player through text, offering guidance, hints, or engaging in conversations. + +10. Emphasize accessibility: 1D text-based games can be more accessible to players with visual impairments, as they can be played using screen readers or other assistive technologies. + +While some of these ideas can be adapted to 2D and 3D games, they are particularly well-suited for 1D text-based games, where the focus is on narrative, imagination, and text-driven gameplay. + + """) + with gr.Column(): + gr.HTML("Main idea for this tab is ways to reuse the config in same/different way in other platforms - Need a conversion management interface") + with gr.Accordion("Basic planning for conversions", open=False): + gr.HTML("MikeTheTech Tutorials (He has many more playlists so check the rest of his channel) -
playcanvas https://www.youtube.com/playlist?list=PLJKGfra8Jl0B845PNo9cMxAQfghJrYH1Y,
unreal engine - https://www.youtube.com/watch?v=STHdIdWO0tk&list=PLJKGfra8Jl0CSjCzk1r0peTp0orIleqdG,
gamemaker - https://www.youtube.com/watch?v=ka_fVrGMOwo&list=PLJKGfra8Jl0AW3rNuwcmi_aq_PdsjQtbu ") + gr.Interface(ConfigConversionforExporttoPlatform, inputs=[ConfigConversionforExporttoPlatformselector, "text"], outputs=["text", "code"], description="Helper for Exporting to other platforms") + gr.HTML("Reskinng and extending the Tutorials / smallest examples = fastest way to gain intuition for whats important") + gr.HTML("For 3D with regard to this space 3 is backdrop for the UI until major modifications are decided by the user") + with gr.Tab("Play Canvas"): + gr.HTML("Playcanvas Tutorials - https://developer.playcanvas.com/tutorials/webxr-hello-world/, Wow - https://developer.playcanvas.com/user-manual/assets/types/wasm/
https://github.com/playcanvas/playcanvas.github.io - https://playcanvas.vercel.app/#/misc/hello-world ") + gr.HTML("https://youtu.be/a3pJiZ86XgA (Learn.Nimagin) - Youtube video showing user interface in playcanvas (My observation is you can have 3d items moving behind the interface like how 2d fighter use 3d space)") + gr.HTML("How can add 100 shapes in playcanvas very quickly? - Seems one at a time during editor phase") + gr.HTML("Each location is new scene?") + gr.HTML("Repurpose the ball deno - ball replaced with a custom 3D object
platform can used as map floor by changing dimensions, islands unlock = hidden platforms unhidden by a trigger (verticality as location access control)
Teleport can be loader for UI ") + gr.HTML("") + with gr.Tab("Unreal Engine"): + gr.HTML("Learning Centre - https://dev.epicgames.com/community/unreal-engine/learning") + gr.HTML("How can add 100 shapes in playcanvas very quickly? - Can run python scripts and possibly c++") + gr.HTML("") + gr.HTML("") + gr.HTML("") + with gr.Tab("Existing Game as game engine"): + gr.HTML("Unreal Engine for Fortnite https://dev.epicgames.com/community/fortnite/getting-started/uefn") + gr.HTML("https://www.reddit.com/r/singularity/comments/1fd4oo1/roblox_announces_ai_tool_for_generating_3d_game/ - roblox and genai") + gr.HTML("Modding Tools? eg. Skyrim, GTA, sims 4 custom scenarios") + gr.HTML("Other games as Iframes and MLM as the judge (visual confirmation) aka the quest completion algorithm") + with gr.Tab("In-browser / mobile IDE to Mobile (Mainly Android)"): + gr.Interface(fn=lambda inputtoconvert: inputtoconvert + " + placeholder code", inputs=["text"], outputs=["text"], description="placeholder code for conversion from JSON to basic text UI (eg. terminal = nested while loop + input + if in python)" ) + gr.HTML("'Host' Dart app on github - create hyperlink like the page suggests: https://codewithandrea.com/tips/create-dartpad-from-github-gist/") + gr.HTML("https://docs.flutter.dev/ui, https://docs.flutter.dev/data-and-backend/state-mgmt/intro
https://developer.android.com/ai/aicore") + gr.HTML("Some ideas - https://github.com/divyanshub024/awesome-dart-pad, Dart Website - https://dart.dev/tools/dartpad") + gr.HTML("Flutter / Pydroid / Kivy / Colab") + gr.HTML("Flutter - Dartpad, Project IDX, Replit?") + + with gr.Tab("Import"): + gr.HTML("Textbooks/Memes/Sayings/Songs/Books to Games. NLP as a way to guide the generation process") + with gr.Tab("Basic Game Engine Mechanics"): + gr.HTML("There is an issue with the loading of the choices above 4 - only 3 load max it seems") + gr.HTML("Placeholder for explanations of Player and Game Session") + with gr.Tab("Endless Commerce support"): + gr.HTML("Need to be able to support this type of code for upgrades - https://www.decisionproblem.com/paperclips/index2.html - https://www.reddit.com/r/incremental_games/comments/rc7ks7/the_unique_storytelling_of_universal_paperclips/
https://huggingface.co/spaces/osanseviero/TheMLGame/blob/main/main.js") + with gr.Tab("Current '1D Engine' Defects"): + gr.HTML("To test the config idea I (with llm assistance) had to make an 'engine' that was based around the config - so there are many potholes ") + gr.HTML("All realtime events - Text still needs realtime as well") + with gr.Tab("Inventory and Skill Support"): + gr.HTML("Each decision affects Skills or inventory") + with gr.Tab("NPC Support"): + gr.HTML("Shared timeline that the player interfere with") + with gr.Tab("Economics Support"): + gr.HTML("Style Idea for a Basic Idea - Endless Economy (Tiny Tower as well) - Paperclip maximiser and inspirations - https://huggingface.co/spaces/osanseviero/TheMLGame") + with gr.Tab("Time Support"): + gr.HTML("No urgency / patience mechanics") + with gr.Tab("LLM play testing"): + gr.HTML("LLM can read the contents in full and give critiques but they can also play the game if you make a api interface - gradio allows this in the form of gradio client but you can also reroute the user inputs to function calling") + with gr.Tab("Custom JS Config Creator"): + gr.HTML("-- Incomplete -- Companion Space for zerogpu / client api workflow planning for a way to send a zip to the Basic Game Engine at the bottom of https://huggingface.co/spaces/KwabsHug/TestSvelteStatic (Also to test how much can be done majority on cpu)") + with gr.Tab("Simple Config Creator"): + inventory_items = gr.State([]) + skills_items = gr.State([]) + objectives_items = gr.State([]) + targets_items = gr.State([]) + + with gr.Tabs(): + with gr.TabItem("Inventory"): + inventory_type = gr.Textbox(label="Type") + inventory_name = gr.Textbox(label="Name") + inventory_description = gr.Textbox(label="Description") + add_inventory = gr.Button("Add Inventory Item") + inventory_textbox = gr.JSON(label="Inventory Items", value=[]) + + with gr.TabItem("Skills"): + skills_branch = gr.Textbox(label="Branch") + skills_name = gr.Textbox(label="Name") + skills_learned = gr.Dropdown(choices=["True", "False"], label="Learned") + add_skill_button = gr.Button("Add Skill") + skills_textbox = gr.JSON(label="Skills", value=[]) + + with gr.TabItem("Objectives"): + objectives_id = gr.Textbox(label="ID") + objectives_name = gr.Textbox(label="Name") + objectives_complete = gr.Dropdown(choices=["True", "False"], label="Complete") + add_objective_button = gr.Button("Add Objective") + objectives_textbox = gr.JSON(label="Objectives", value=[]) + + with gr.TabItem("Targets"): + targets_name = gr.Textbox(label="Name") + targets_x = gr.Textbox(label="X Coordinate") + targets_y = gr.Textbox(label="Y Coordinate") + targets_collisionType = gr.Textbox(label="Collision Type") + targets_collisiontext = gr.Textbox(label="Collision Text") + add_target_button = gr.Button("Add Target") + targets_textbox = gr.JSON(label="Targets", value=[]) + + with gr.TabItem("Placeholders for Modal Target"): + gr.HTML("Placeholder") + + with gr.TabItem("Placeholders for State Machine Modal Target"): + gr.HTML("Placeholder") + + with gr.TabItem("Placeholders for Background"): + gr.HTML("Placeholder") + + config_output = gr.JSON(label="Updated Configuration") + + @gr.render(inputs=[inventory_items, skills_items, objectives_items, targets_items]) #, outputs=config_output) + def aggregate_config(inventory, skills, objectives, targets): + config = default_config.copy() + config['inventory'] = inventory + config['skills'] = skills + config['objectives'] = objectives + config['targets'] = targets + return config + + add_inventory.click(add_inventory_item, inputs=[inventory_items, inventory_type, inventory_name, inventory_description], outputs=inventory_textbox) + add_inventory.click(aggregate_config, inputs=[inventory_items, skills_items, objectives_items, targets_items], outputs=config_output) + + add_skill_button.click(add_skill, inputs=[skills_items, skills_branch, skills_name, skills_learned], outputs=skills_textbox) + add_skill_button.click(aggregate_config, inputs=[inventory_items, skills_items, objectives_items, targets_items], outputs=config_output) + + add_objective_button.click(add_objective, inputs=[objectives_items, objectives_id, objectives_name, objectives_complete], outputs=objectives_textbox) + add_objective_button.click(aggregate_config, inputs=[inventory_items, skills_items, objectives_items, targets_items], outputs=config_output) + + add_target_button.click(add_target, inputs=[targets_items, targets_name, targets_x, targets_y, targets_collisionType, targets_collisiontext], outputs=targets_textbox) + add_target_button.click(aggregate_config, inputs=[inventory_items, skills_items, objectives_items, targets_items], outputs=config_output) + with gr.Tab("Advanced Config Creator"): + gr.HTML("Config with More than text and images") + + with gr.Tab("Real World reach of the game"): + gr.HTML("3D printed trophies or game items as real items") + gr.HTML("Smart Watch and Phone - Notifications to watch and eg. character messages, authorisation requests, in game internet - Cloud computing / SBC or controller project ideas") + with gr.Tab("LLM/Robotics as custom controllers"): + gr.HTML("https://www.reddit.com/r/singularity/comments/1fm7fup/ihmc_and_boardwalk_robotics_show_their_humanoid/") + gr.HTML("Controls changed the scope of the game eg. mouse vs keyboard vs console controller vs remote vs touch screen
LLM can be vision/surveilance based controler (eg. MGS/GTA camera gauged by an actual camera in real life) or it can be a companion (offline/off console game progrssion ideas)") + gr.HTML("https://github.com/Shaka-Labs/ACT $250 imitation learning/teleoperation - eg. a win loss result alert / NPC 'scout' telling you go or stay") + gr.HTML("https://huggingface.co/posts/thomwolf/809364796644704") + gr.HTML("Robotics - https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/ https://huggingface.co/lerobot https://github.com/tonyzhaozh/aloha https://github.com/Shaka-Labs/ACT https://github.com/OpenTeleVision/TeleVision https://www.stereolabs.com/ ") + gr.HTML("https://www.reddit.com/r/singularity/comments/1f88z58/the_first_ever_agent_civilization_1000_truly/") + with gr.Tab("Existing Game Developemnt Resources"): + gr.HTML("https://enginesdatabase.com/") + gr.HTML("https://develop.games/#nav-tools-engine ") + with gr.Tab("Other Considerations"): + with gr.Tab("General"): + gr.HTML("https://www.reddit.com/r/singularity/comments/1fiugew/wonderworld_a_novel_framework_for_interactive_3d/") + + gr.HTML("https://huggingface.co/docs/hub/api - daily papers is an endpoint so you can turn paper abstract into games with the help of LLM") + + gr.HTML("Experiment for https://huggingface.co/spaces/ysharma/open-interpreter/blob/main/app.py inplementation with gradio client api") + + gr.HTML("https://huggingface.co/spaces/HuggingFaceTB/SmolLM-135M-Instruct-WebGPU") + + gr.HTML("Useful Spaces and links: https://huggingface.co/spaces/artificialguybr/Stable-Audio-Open-Zero https://huggingface.co/spaces/stabilityai/TripoSR https://huggingface.co/spaces/wangfuyun/AnimateLCM-SVD https://huggingface.co/spaces/multimodalart/face-to-all https://huggingface.co/spaces/facebook/MusicGen https://huggingface.co/spaces/Doubiiu/tooncrafter") + + gr.HTML("langchain docs as awareness for alot of the integration use cases and providers that are possible - https://python.langchain.com/v0.2/docs/integrations/tools/") + + gr.HTML("https://huggingface.co/spaces/linoyts/scribble-sdxl-flash as map planner") + + gr.HTML("---------------------------------------Gameplay Ideas-------------------------------") + gr.HTML("https://huggingface.co/spaces/Lin-Chen/ShareCaptioner-Video - game use example police questions a event with multiple eye witnesses needs to give as close to the caption description to win") + with gr.Tab("State management through huggingface?"): + gr.HTML("Huggingface as the login provider? - https://huggingface.co/spaces/Wauplin/gradio-user-history/tree/main https://huggingface.co/spaces/AP123/IllusionDiffusion https://huggingface.co/docs/hub/en/spaces-oauth https://huggingface.co/docs/hub/en/oauth, persistent storage - https://huggingface.co/docs/hub/en/spaces-storage") + with gr.Tab("Finetuning options"): + gr.HTML("https://docs.mistral.ai/guides/finetuning/
https://openpipe.ai/blog/fine-tuning-best-practices-chapter-2-models") + gr.HTML("Unsloth and Colab? - https://github.com/unslothai/unsloth https://huggingface.co/unsloth
Mistral Nemo Base - https://huggingface.co/unsloth/Mistral-Nemo-Base-2407 - https://colab.research.google.com/drive/17d3U-CAIwzmbDRqbZ9NnpHxCkmXB6LZ0?usp=sharing
Llama 3 8B https://huggingface.co/unsloth/llama-3-8b-Instruct-bnb-4bit") + gr.HTML("Price - https://openpipe.ai/pricing") + with gr.Tab("Backend and/or Hosting?"): + gr.HTML("Deployemnt options - https://huggingface.co/SpacesExamples", "https://huggingface.co/templates") + gr.HTML("Prototyping and freemium
free api
HF Pro subscription") + gr.HTML("GPU (Data privacy) = No Rate limits? - https://replicate.com/pricing, https://lambdalabs.com/service/gpu-cloud https://huggingface.co/pricing#endpoints https://tensordock.com/cloud-gpus", "https://massedcompute.com/home/pricing/" ) + gr.HTML("Speed - Groq, SambaNova, https://www.etched.com/announcing-etched ") + gr.HTML("Price - Coding - https://aider.chat/docs/leaderboards/ - https://www.deepseek.com/ 0.3 per million - is this per token or chinese character as that means converting code to chinese if possible can save api cost?") + gr.HTML("Llama 3.1 405B - https://ai.meta.com/blog/meta-llama-3-1/ https://replicate.com/meta/meta-llama-3.1-405b-instruct https://fireworks.ai/pricing https://www.ibm.com/products/watsonx-ai/foundation-models") + gr.HTML("Covered by Anythingllm - https://github.com/Mintplex-Labs/anything-llm : https://repocloud.io/details/?app_id=276, https://render.com/pricing, https://docs.railway.app/reference/pricing/free-trial, https://repocloud.io/pricing, https://elest.io/pricing ") + with gr.Tab("Some Interesting Git Repos"): + gr.HTML("https://github.com/NVIDIA/Megatron-LM https://github.com/OpenGVLab/EfficientQAT https://github.com/evintunador/minLlama3/blob/main/model.py https://github.com/evintunador/micro-GPT-sandbox") + with gr.Tab("Old Ideas"): + gr.HTML("""
Main ideas for this space is (June 2024) (Custom component planning?):
+
+
We can generate almost any media data and more
+
A program exist around data
+
Time moves in a straight so all considerations are flattend by the nature of time
+
llms good at short questions
+
HF + Gradio allows for api use so this my prototype tool for tool use test
+
""") + with gr.Tab("Licensing"): + gr.HTML("Need to find the press release to see license eg. https://blackforestlabs.ai/announcing-black-forest-labs/") + with gr.Tabs("Links to go over when free"): + gr.HTML("https://www.reddit.com/r/singularity/comments/1ecuu8j/you_can_now_use_ai_for_3d_model_creation/ | ") + + with gr.Tab("Asset loading test"): + gr.HTML("SDXL (linoyts/scribble-sdxl-flash), SVD and Stable Audio used for the test assets (For commercial use need a licence)
testmedia/") + with gr.Row(): + gr.Image(value="testmedia/Flash scribble SDXL - random squiggles as roads.webp") + gr.Video(value="testmedia/SVD - random squiggles as roads video 004484.mp4") + gr.Audio(value="testmedia/Stable Audio - Raindrops, output.wav") + gr.HTML(TestmedialoadinHTML) # imported from relatively_constant_variables + demo.queue().launch()