Delete app.py

app.py

@@ -1,417 +0,0 @@
-import gradio as gr
-import os
-import spaces
-import json
-import re
-import random
-import numpy as np
-from gradio_client import Client, handle_file
-hf_token = os.environ.get("HF_TOKEN")
-
-MAX_SEED = np.iinfo(np.int32).max
-
-def check_api(model_name):
-    if model_name == "MAGNet":
-        try :
-            client = Client("fffiloni/MAGNet")
-            return "api ready"
-        except : 
-            return "api not ready yet"
-    elif model_name == "AudioLDM-2":
-        try :
-            client = Client("fffiloni/audioldm2-text2audio-text2music-API", hf_token=hf_token)
-            return "api ready"
-        except : 
-            return "api not ready yet"
-    elif model_name == "Riffusion":
-        try :
-            client = Client("fffiloni/spectrogram-to-music")
-            return "api ready"
-        except : 
-            return "api not ready yet"
-    elif model_name == "Mustango":
-        try :
-            client = Client("fffiloni/mustango-API", hf_token=hf_token)
-            return "api ready"
-        except : 
-            return "api not ready yet"
-    elif model_name == "MusicGen":
-        try :
-            client = Client("https://facebook-musicgen.hf.space/")
-            return "api ready"
-        except : 
-            return "api not ready yet"
-    elif model_name == "Stable Audio Open":
-        try:
-            client = Client("fffiloni/Stable-Audio-Open-A10", hf_token=hf_token)
-            return "api ready"
-        except:
-            return "api not ready yet"
-    
-        
-from moviepy.editor import VideoFileClip
-from moviepy.audio.AudioClip import AudioClip
-
-def extract_audio(video_in):
-    input_video = video_in
-    output_audio = 'audio.wav'
-    
-    # Open the video file and extract the audio
-    video_clip = VideoFileClip(input_video)
-    audio_clip = video_clip.audio
-    
-    # Save the audio as a .wav file
-    audio_clip.write_audiofile(output_audio, fps=44100)  # Use 44100 Hz as the sample rate for .wav files  
-    print("Audio extraction complete.")
-
-    return 'audio.wav'
-
-
-
-def get_caption(image_in):
-    kosmos2_client = Client("fffiloni/Kosmos-2-API", hf_token=hf_token)
-    kosmos2_result = kosmos2_client.predict(
-		image_input=handle_file(image_in),
-		text_input="Detailed",
-		api_name="/generate_predictions"
-    )
-    print(f"KOSMOS2 RETURNS: {kosmos2_result}")
-
-    data = kosmos2_result[1]
-
-    # Extract and combine tokens starting from the second element
-    sentence = ''.join(item['token'] for item in data[1:])
-
-    # 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 sentence
-
-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):
-
-    client = Client("fffiloni/MAGNet")
-    result = client.predict(
-        model="facebook/magnet-small-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
-        model_path="",	# str  in 'Model Path (custom models)' Textbox component
-        text=prompt,	# str  in 'Input Text' Textbox component
-        temperature=3,	# float  in 'Temperature' Number component
-        topp=0.9,	# float  in 'Top-p' Number component
-        max_cfg_coef=10,	# float  in 'Max CFG coefficient' Number component
-        min_cfg_coef=1,	# float  in 'Min CFG coefficient' Number component
-        decoding_steps1=20,	# float  in 'Decoding Steps (stage 1)' Number component
-        decoding_steps2=10,	# float  in 'Decoding Steps (stage 2)' Number component
-        decoding_steps3=10,	# float  in 'Decoding Steps (stage 3)' Number component
-        decoding_steps4=10,	# float  in 'Decoding Steps (stage 4)' Number component
-        span_score="prod-stride1 (new!)",	# Literal['max-nonoverlap', 'prod-stride1 (new!)']  in 'Span Scoring' Radio component
-        api_name="/predict_full"
-    )
-    print(result)
-    return result[1]
-
-def get_audioldm(prompt):
-    client = Client("fffiloni/audioldm2-text2audio-text2music-API", hf_token=hf_token)
-    seed = random.randint(0, MAX_SEED)
-    result = client.predict(
-        text=prompt,	# str in 'Input text' Textbox component
-        negative_prompt="Low quality.",	# str in 'Negative prompt' Textbox component
-        duration=10,	# int | float (numeric value between 5 and 15) in 'Duration (seconds)' Slider component
-        guidance_scale=6.5,	# int | float (numeric value between 0 and 7) in 'Guidance scale' Slider component
-        random_seed=seed,	# int | float in 'Seed' Number component
-        n_candidates=3,	# int | float (numeric value between 1 and 5) in 'Number waveforms to generate' Slider component
-        api_name="/text2audio"
-    )
-    print(result)
-    
-    return result
-
-def get_riffusion(prompt):
-    client = Client("fffiloni/spectrogram-to-music")
-    result = client.predict(
-		prompt=prompt,	# str  in 'Musical prompt' Textbox component
-		negative_prompt="",	# str  in 'Negative prompt' Textbox component
-		audio_input=None,	# filepath  in 'parameter_4' Audio component
-		duration=10,	# float (numeric value between 5 and 10) in 'Duration in seconds' Slider component
-		api_name="/predict"
-    )
-    print(result)
-    return result[1]
-
-def get_mustango(prompt):
-    client = Client("fffiloni/mustango-API", hf_token=hf_token)
-    result = client.predict(
-		prompt=prompt,	# str  in 'Prompt' Textbox component
-		steps=200,	# float (numeric value between 100 and 200) in 'Steps' Slider component
-		guidance=6,	# float (numeric value between 1 and 10) in 'Guidance Scale' Slider component
-		api_name="/predict"
-    )
-    print(result)
-    return result
-
-def get_musicgen(prompt):
-    client = Client("https://facebook-musicgen.hf.space/")
-    result = client.predict(
-        prompt,	# str  in 'Describe your music' Textbox component
-        None,	# str (filepath or URL to file) in 'File' Audio component
-        fn_index=0
-    )
-    print(result)
-    return result[1]
-
-def get_stable_audio_open(prompt):
-    client = Client("fffiloni/Stable-Audio-Open-A10", hf_token=hf_token)
-    result = client.predict(
-		prompt=prompt,
-		seconds_total=10,
-		steps=100,
-		cfg_scale=7,
-		api_name="/predict"
-    )
-    print(result)
-    return result
-
-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")
-
-standard_sys = f"""
-You are a musician 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"
-"""
-
-mustango_sys = f"""
-You are a musician 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, you MUST include chords progression.
-For example, if a user says, "a painting of three old women having tea party", provide immediately a musical prompt corresponding to the image description. 
-Immediately STOP after that. It should be EXACTLY in this format:
-"The song is an instrumental. The song is in medium tempo with a classical guitar playing a lilting melody in accompaniment style. The song is emotional and romantic. The song is a romantic instrumental song. The chord sequence is Gm, F6, Ebm. The time signature is 4/4. This song is in Adagio. The key of this song is G minor."
-"""
-
-@spaces.GPU(enable_queue=True)
-def get_musical_prompt(user_prompt, chosen_model):
-
-    """
-    if chosen_model == "Mustango" :
-        agent_maker_sys = standard_sys
-    else :
-        agent_maker_sys = standard_sys
-    """
-    agent_maker_sys = standard_sys
-    
-    instruction = f"""
-<|system|>
-{agent_maker_sys}</s>
-<|user|>
-"""
-    
-    prompt = f"{instruction.strip()}\n{user_prompt}</s>"    
-    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}")
-    return cleaned_text.lstrip("\n")
-
-def infer(image_in, chosen_model, api_status):
-    if image_in == None :
-        raise gr.Error("Please provide an image input")
-
-    if chosen_model == [] :
-        raise gr.Error("Please pick a model")
-
-    if api_status == "api not ready yet" :
-        raise gr.Error("This model is not ready yet, you can pick another one instead :)")
-    
-    gr.Info("Getting image caption with Kosmos-2...")
-    user_prompt = get_caption(image_in)
-    #user_prompt = get_caption_from_MD(image_in)
-    
-    gr.Info("Building a musical prompt according to the image caption ...")
-    musical_prompt = get_musical_prompt(user_prompt, chosen_model)
-
-    if chosen_model == "MAGNet" :
-        gr.Info("Now calling MAGNet for music...")
-        music_o = get_magnet(musical_prompt)
-    elif chosen_model == "AudioLDM-2" :
-        gr.Info("Now calling AudioLDM-2 for music...")
-        music_o = get_audioldm(musical_prompt)
-    elif chosen_model == "Riffusion" :
-        gr.Info("Now calling Riffusion for music...")
-        music_o = get_riffusion(musical_prompt)
-    elif chosen_model == "Mustango" :
-        gr.Info("Now calling Mustango for music...")
-        music_o = get_mustango(musical_prompt)
-    elif chosen_model == "MusicGen" :
-        gr.Info("Now calling MusicGen for music...")
-        music_o = get_musicgen(musical_prompt)
-    elif chosen_model == "Stable Audio Open" :
-        gr.Info("Now calling Stable Audio Open for music...")
-        music_o = get_stable_audio_open(musical_prompt)
-    
-    return gr.update(value=musical_prompt, interactive=True), gr.update(visible=True), music_o
-
-def retry(chosen_model, caption):
-    musical_prompt = caption
-    music_o = None
-
-    if chosen_model == "MAGNet" :
-        gr.Info("Now calling MAGNet for music...")
-        music_o = get_magnet(musical_prompt)
-    elif chosen_model == "AudioLDM-2" :
-        gr.Info("Now calling AudioLDM-2 for music...")
-        music_o = get_audioldm(musical_prompt)
-    elif chosen_model == "Riffusion" :
-        gr.Info("Now calling Riffusion for music...")
-        music_o = get_riffusion(musical_prompt)
-    elif chosen_model == "Mustango" :
-        gr.Info("Now calling Mustango for music...")
-        music_o = get_mustango(musical_prompt)
-    elif chosen_model == "MusicGen" :
-        gr.Info("Now calling MusicGen for music...")
-        music_o = get_musicgen(musical_prompt)
-    elif chosen_model == "Stable Audio Open" :
-        gr.Info("Now calling Stable Audio Open for music...")
-        music_o = get_stable_audio_open(musical_prompt)
-
-    return music_o
-
-demo_title = "Image to Music V2"
-description = "Get music from a picture, compare text-to-music models"
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 980px;
-    text-align: left;
-}
-#inspi-prompt textarea {
-    font-size: 20px;
-    line-height: 24px;
-    font-weight: 600;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    
-    with gr.Column(elem_id="col-container"):
-    
-        gr.HTML(f"""
-        <h2 style="text-align: center;">{demo_title}</h2>
-        <p style="text-align: center;">{description}</p>
-        """)
-        
-        with gr.Row():
-            
-            with gr.Column():
-                image_in = gr.Image(
-                    label = "Image reference",
-                    type = "filepath",
-                    elem_id = "image-in"
-                )
-                
-                with gr.Row():
-                    
-                    chosen_model = gr.Dropdown(
-                        label = "Choose a model",
-                        choices = [
-                            #"MAGNet",
-                            "AudioLDM-2",
-                            "Riffusion",
-                            "Mustango",
-                            #"MusicGen",
-                            "Stable Audio Open"
-                        ],
-                        value = None,
-                        filterable = False
-                    )
-                    
-                    check_status = gr.Textbox(
-                        label="API status",
-                        interactive=False
-                    )
-                
-                submit_btn = gr.Button("Make music from my pic !")
-
-                gr.Examples(
-                    examples = [
-                        ["examples/ocean_poet.jpeg"],
-                        ["examples/jasper_horace.jpeg"],
-                        ["examples/summer.jpeg"],
-                        ["examples/mona_diner.png"],
-                        ["examples/monalisa.png"],
-                        ["examples/santa.png"],
-                        ["examples/winter_hiking.png"],
-                        ["examples/teatime.jpeg"],
-                        ["examples/news_experts.jpeg"]
-                    ],
-                    fn = infer,
-                    inputs = [image_in, chosen_model],
-                    examples_per_page = 4
-                )
-            
-            with gr.Column():
-            
-                caption = gr.Textbox(
-                    label = "Inspirational musical prompt",
-                    interactive = False,
-                    elem_id = "inspi-prompt"
-                )
-                
-                retry_btn = gr.Button("Retry with edited prompt", visible=False)
-                
-                result = gr.Audio(
-                    label = "Music"
-                )
-        
-
-    chosen_model.change(
-        fn = check_api,
-        inputs = chosen_model,
-        outputs = check_status,
-        queue = False
-    )
-
-    retry_btn.click(
-        fn = retry,
-        inputs = [chosen_model, caption],
-        outputs = [result]
-    )
-    
-    submit_btn.click(
-        fn = infer,
-        inputs = [
-            image_in,
-            chosen_model,
-            check_status
-        ],
-        outputs =[
-            caption,
-            retry_btn,
-            result
-        ]
-    )
-
-demo.queue(max_size=16).launch(show_api=False, show_error=True)