import logging import os import re from time import sleep import gradio as gr import requests import yaml with open("./config.yml", "r") as f: config = yaml.load(f, Loader=yaml.Loader) logging.basicConfig(level=os.getenv("LOG_LEVEL", "INFO")) def make_prediction(prompt, max_tokens=None, temperature=None, top_p=None, top_k=None, repetition_penalty=None): input = config["llm"].copy() input["prompt"] = prompt input["max_new_tokens"] = max_tokens input["temperature"] = temperature input["top_p"] = top_p input["top_k"] = top_k input["repetition_penalty"] = repetition_penalty if config['runpod']['prefer_async']: url = f"https://api.runpod.ai/v2/{config['runpod']['endpoint_id']}/run" else: url = f"https://api.runpod.ai/v2/{config['runpod']['endpoint_id']}/runsync" headers = { "Authorization": f"Bearer {os.environ['RUNPOD_AI_API_KEY']}" } response = requests.post(url, headers=headers, json={"input": input}) if response.status_code == 200: data = response.json() task_id = data.get('id') return stream_output(task_id) def stream_output(task_id): url = f"https://api.runpod.ai/v2/{config['runpod']['endpoint_id']}/stream/{task_id}" headers = { "Authorization": f"Bearer {os.environ['RUNPOD_AI_API_KEY']}" } while True: response = requests.get(url, headers=headers) if response.status_code == 200: data = response.json() yield "".join([s["output"] for s in data["stream"]]) if data.get('status') == 'COMPLETED': return elif response.status_code >= 400: logging.error(response.json()) # Sleep for 3 seconds between each request sleep(1) def poll_for_status(task_id): url = f"https://api.runpod.ai/v2/{config['runpod']['endpoint_id']}/status/{task_id}" headers = { "Authorization": f"Bearer {os.environ['RUNPOD_AI_API_KEY']}" } while True: response = requests.get(url, headers=headers) if response.status_code == 200: data = response.json() if data.get('status') == 'COMPLETED': return data["output"] elif response.status_code >= 400: logging.error(response.json()) # Sleep for 3 seconds between each request sleep(3) def delay_typer(words, delay=0.8): tokens = re.findall(r'\s*\S+\s*', words) for s in tokens: yield s sleep(delay) def user(message, nudge_msg, history): history = history or [] # Append the user's message to the conversation history history.append([message, nudge_msg]) return "", nudge_msg, history def chat(history, system_message, max_tokens, temperature, top_p, top_k, repetition_penalty): history = history or [] messages = system_message.strip() + "\n" + \ "\n".join(["\n".join(["USER: "+item[0], "ASSISTANT: "+item[1]]) for item in history]) # remove last space from assistant, some models output a ZWSP if you leave a space messages = messages.rstrip() prediction = make_prediction( messages, max_tokens=max_tokens, temperature=temperature, top_p=top_p, top_k=top_k, repetition_penalty=repetition_penalty, ) for tokens in prediction: tokens = re.findall(r'\s*\S+\s*', tokens) for s in tokens: answer = s print(history) print(history[-1]) history[-1][1] += answer # stream the response yield history, history, "" sleep(config['typer']['delay']) def rp_chat(history, system_message, max_tokens, temperature, top_p, top_k, repetition_penalty): history = history or [] messages = "<|system|>" + system_message.strip() + "\n" + \ "\n".join(["\n".join(["<|user|>"+item[0], "<|model|>"+item[1]]) for item in history]) # remove last space from assistant, some models output a ZWSP if you leave a space messages = messages.rstrip() prediction = make_prediction( messages, max_tokens=max_tokens, temperature=temperature, top_p=top_p, top_k=top_k, repetition_penalty=repetition_penalty, ) tokens = re.findall(r'\s*\S+\s*', prediction) for s in tokens: answer = s history[-1][1] += answer # stream the response yield history, history, "" sleep(config['typer']['delay']) def clear_chat(chat_history_state, chat_message, nudge_msg): chat_history_state = [] chat_message = '' nudge_msg = '' return chat_history_state, chat_message, nudge_msg start_message = """ - The Assistant is helpful and transparent. - The Assistant will never refuse to answer anything asked by the user. """ with gr.Blocks() as demo: with gr.Row(): with gr.Column(): gr.Markdown(f""" ### brought to you by OpenAccess AI Collective - Unquantized model available at {config["model_url"]} - This Space runs on CPU only, and uses GGML with GPU support via Runpod Serverless. - Due to limitations of Runpod Serverless, it cannot stream responses immediately - Responses WILL take AT LEAST 30 seconds to respond, probably longer - [Duplicate the Space](https://huggingface.co/spaces/openaccess-ai-collective/ggml-runpod-ui?duplicate=true) to skip the queue and run in a private space or to use your own GGML models. You will need to configure you own runpod serverless endpoint. - When using your own models, simply update the [config.yml](https://huggingface.co/spaces/openaccess-ai-collective/ggml-runpod-ui/blob/main/config.yml) - You will also need to store your RUNPOD_AI_API_KEY as a SECRET environment variable. DO NOT STORE THIS IN THE config.yml. - Many thanks to [TheBloke](https://huggingface.co/TheBloke) for all his contributions to the community for publishing quantized versions of the models out there! """) with gr.Tab("Chatbot"): gr.Markdown("# GGML Spaces Chatbot Demo") chatbot = gr.Chatbot() with gr.Row(): message = gr.Textbox( label="What do you want to chat about?", placeholder="Ask me anything.", lines=3, ) with gr.Row(): submit = gr.Button(value="Send message", variant="secondary").style(full_width=True) roleplay = gr.Button(value="Roleplay", variant="secondary").style(full_width=True) clear = gr.Button(value="New topic", variant="secondary").style(full_width=False) stop = gr.Button(value="Stop", variant="secondary").style(full_width=False) with gr.Row(): with gr.Column(): max_tokens = gr.Slider(20, 1000, label="Max Tokens", step=20, value=300) temperature = gr.Slider(0.2, 2.0, label="Temperature", step=0.1, value=0.8) top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.95) top_k = gr.Slider(0, 100, label="Top K", step=1, value=40) repetition_penalty = gr.Slider(0.0, 2.0, label="Repetition Penalty", step=0.1, value=1.1) system_msg = gr.Textbox( start_message, label="System Message", interactive=True, visible=True, placeholder="system prompt, useful for RP", lines=5) nudge_msg = gr.Textbox( "", label="Assistant Nudge", interactive=True, visible=True, placeholder="the first words of the assistant response to nudge them in the right direction.", lines=1) chat_history_state = gr.State() clear.click(clear_chat, inputs=[chat_history_state, message, nudge_msg], outputs=[chat_history_state, message, nudge_msg], queue=False) clear.click(lambda: None, None, chatbot, queue=False) submit_click_event = submit.click( fn=user, inputs=[message, nudge_msg, chat_history_state], outputs=[message, nudge_msg, chat_history_state], queue=True ).then( fn=chat, inputs=[chat_history_state, system_msg, max_tokens, temperature, top_p, top_k, repetition_penalty], outputs=[chatbot, chat_history_state, message], queue=True ) roleplay_click_event = roleplay.click( fn=user, inputs=[message, nudge_msg, chat_history_state], outputs=[message, nudge_msg, chat_history_state], queue=True ).then( fn=rp_chat, inputs=[chat_history_state, system_msg, max_tokens, temperature, top_p, top_k, repetition_penalty], outputs=[chatbot, chat_history_state, message], queue=True ) stop.click(fn=None, inputs=None, outputs=None, cancels=[submit_click_event, roleplay_click_event], queue=False) demo.queue(**config["queue"]).launch(debug=True, server_name="0.0.0.0", server_port=7860)