app.py

import gradio as gr
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from diffusers import StableDiffusionPipeline, DiffusionPipeline
from huggingface_hub import HfApi

# Set up Hugging Face API
api = HfApi()

# Define a function to load a language model
def load_language_model(model_name):
    model = AutoModelForSequenceClassification.from_pretrained(model_name)
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    return model, tokenizer

# Define a function to generate text with a language model
def generate_text(model, tokenizer, prompt):
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model(**inputs)
    return tokenizer.decode(outputs.logits[0], skip_special_tokens=True)

# Define a function to generate an image with Stable Diffusion
def generate_image(prompt, model_name):
    pipe = StableDiffusionPipeline.from_pretrained(model_name)
    image = pipe(prompt, num_inference_steps=50).images[0]
    return image

# Define a function to generate video or music with other diffusion models
def generate_media(prompt, model_name, media_type):
    pipe = DiffusionPipeline.from_pretrained(model_name)
    if media_type == "video":
        output = pipe(prompt, num_inference_steps=50).videos[0]
    elif media_type == "music":
        output = pipe(prompt, num_inference_steps=50).audios[0]
    return output

# Create a Gradio interface
with gr.Blocks() as demo:
    with gr.Tab("Chat"):
        with gr.Row():
            language_model_input = gr.Textbox(label="Language Model")
            query_button = gr.Button("Query HuggingFace Hub")
        chat_input = gr.Textbox(label="Chat Input")
        chat_output = gr.Textbox(label="Chat Output")
        generate_button = gr.Button("Generate Text")
    
    with gr.Tab("Image Generation"):
        image_input = gr.Textbox(label="Image Prompt")
        image_model_input = gr.Textbox(label="Image Model")
        generate_image_button = gr.Button("Generate Image")
        image_output = gr.Image(label="Generated Image")
    
    with gr.Tab("Media Generation"):
        media_input = gr.Textbox(label="Media Prompt")
        media_model_input = gr.Textbox(label="Media Model")
        media_type_input = gr.Radio(label="Media Type", choices=["video", "music"])
        generate_media_button = gr.Button("Generate Media")
        media_output = gr.Video(label="Generated Media") if media_type_input == "video" else gr.Audio(label="Generated Media")
    
    # Query Hugging Face Hub for language models
    query_button.click(fn=lambda x: [model.modelId for model in api.list_models(filter=x)], inputs=language_model_input, outputs=language_model_input)
    
    # Generate text with a language model
    generate_button.click(fn=generate_text, inputs=[language_model_input, chat_input], outputs=chat_output)
    
    # Generate an image with Stable Diffusion
    generate_image_button.click(fn=generate_image, inputs=[image_input, image_model_input], outputs=image_output)
    
    # Generate video or music with other diffusion models
    generate_media_button.click(fn=generate_media, inputs=[media_input, media_model_input, media_type_input], outputs=media_output)

demo.launch()