Create app.py

app.py

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+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()