import gradio as gr import spaces import json import re from gradio_client import Client #fusecap_client = Client("https://noamrot-fusecap-image-captioning.hf.space/") #fuyu_client = Client("https://adept-fuyu-8b-demo.hf.space/") kosmos2_client = Client("https://ydshieh-kosmos-2.hf.space/") def get_caption(image_in): """ fuyu_result = fuyu_client.predict( image_in, # str representing input in 'raw_image' Image component True, # bool in 'Enable detailed captioning' Checkbox component fn_index=2 ) """ kosmos2_result = kosmos2_client.predict( image_in, # str (filepath or URL to image) in 'Test Image' Image component "Detailed", # str in 'Description Type' Radio component fn_index=4 ) print(f"KOSMOS2 RETURNS: {kosmos2_result}") with open(kosmos2_result[1], 'r') as f: data = json.load(f) reconstructed_sentence = [] for sublist in data: reconstructed_sentence.append(sublist[0]) full_sentence = ' '.join(reconstructed_sentence) #print(full_sentence) # Find the pattern matching the expected format ("Describe this image in detail:" followed by optional space and then the rest)... pattern = r'^Describe this image in detail:\s*(.*)$' # Apply the regex pattern to extract the description text. match = re.search(pattern, full_sentence) if match: description = match.group(1) print(description) else: print("Unable to locate valid description.") # Find the last occurrence of "." #last_period_index = full_sentence.rfind('.') # Truncate the string up to the last period #truncated_caption = full_sentence[:last_period_index + 1] # print(truncated_caption) #print(f"\n—\nIMAGE CAPTION: {truncated_caption}") return description def get_caption_from_MD(image_in): client = Client("https://vikhyatk-moondream1.hf.space/") result = client.predict( image_in, # filepath in 'image' Image component "Describe precisely the image.", # str in 'Question' Textbox component api_name="/answer_question" ) print(result) return result def get_magnet(prompt): amended_prompt = f"{prompt}" print(amended_prompt) client = Client("https://fffiloni-magnet.hf.space/") result = client.predict( "facebook/magnet-medium-10secs", # Literal['facebook/magnet-small-10secs', 'facebook/magnet-medium-10secs', 'facebook/magnet-small-30secs', 'facebook/magnet-medium-30secs', 'facebook/audio-magnet-small', 'facebook/audio-magnet-medium'] in 'Model' Radio component "", # str in 'Model Path (custom models)' Textbox component amended_prompt, # str in 'Input Text' Textbox component 3, # float in 'Temperature' Number component 0.9, # float in 'Top-p' Number component 10, # float in 'Max CFG coefficient' Number component 1, # float in 'Min CFG coefficient' Number component 20, # float in 'Decoding Steps (stage 1)' Number component 10, # float in 'Decoding Steps (stage 2)' Number component 10, # float in 'Decoding Steps (stage 3)' Number component 10, # float in 'Decoding Steps (stage 4)' Number component "prod-stride1 (new!)", # Literal['max-nonoverlap', 'prod-stride1 (new!)'] in 'Span Scoring' Radio component api_name="/predict_full" ) print(result) return result[1] import re import torch from transformers import pipeline zephyr_model = "HuggingFaceH4/zephyr-7b-beta" mixtral_model = "mistralai/Mixtral-8x7B-Instruct-v0.1" pipe = pipeline("text-generation", model=zephyr_model, torch_dtype=torch.bfloat16, device_map="auto") agent_maker_sys = f""" You are an AI whose job is to help users create their own music which its genre will reflect the character or scene from an image described by users. In particular, you need to respond succintly with few musical words, in a friendly tone, write a musical prompt for a music generation model. For example, if a user says, "a picture of a man in a black suit and tie riding a black dragon", provide immediately a musical prompt corresponding to the image description. Immediately STOP after that. It should be EXACTLY in this format: "A grand orchestral arrangement with thunderous percussion, epic brass fanfares, and soaring strings, creating a cinematic atmosphere fit for a heroic battle" """ instruction = f""" <|system|> {agent_maker_sys} <|user|> """ @spaces.GPU(enable_queue=True, duration=60) def infer(image_in): gr.Info("Getting image caption with Kosmos2...") user_prompt = get_caption(image_in) prompt = f"{instruction.strip()}\n{user_prompt}" #print(f"PROMPT: {prompt}") gr.Info("Building a musical prompt according to the image caption ...") outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) pattern = r'\<\|system\|\>(.*?)\<\|assistant\|\>' cleaned_text = re.sub(pattern, '', outputs[0]["generated_text"], flags=re.DOTALL) print(f"SUGGESTED Musical prompt: {cleaned_text}") gr.Info("Now calling MAGNet for music ...") music_o = get_magnet(cleaned_text) return cleaned_text.lstrip("\n"), music_o demo_title = "Image to Music V2" description = "Get music from a picture" css = """ #col-container{ margin: 0 auto; max-width: 980px; text-align: left; } """ with gr.Blocks(css=css) as demo: with gr.Column(elem_id="col-container"): gr.HTML(f"""

{demo_title}

{description}

""") with gr.Row(): with gr.Column(): image_in = gr.Image( label = "Image reference", type = "filepath", elem_id = "image-in" ) submit_btn = gr.Button("Make music from my pic !") with gr.Column(): caption = gr.Textbox( label = "Musical prompt", max_lines = 3 ) result = gr.Audio( label = "Music" ) with gr.Row(): gr.Examples( examples = [ ["examples/monalisa.png"], ["examples/santa.png"], ["examples/ocean_poet.jpeg"], ["examples/winter_hiking.png"], ["examples/teatime.jpeg"], ["examples/news_experts.jpeg"], ["examples/chicken_adobo.jpeg"] ], fn = infer, inputs = [image_in], outputs = [caption, result], cache_examples = False ) submit_btn.click( fn = infer, inputs = [ image_in ], outputs =[ caption, result ] ) demo.queue().launch(show_api=False)