app.py

import gradio as gr
import torch

def generate_text(prompt, max_length, temperature, top_p, top_k, repetition_penalty):
    # This is a placeholder function
    # In a real implementation, you would:
    # 1. Preprocess the input prompt
    # 2. Load your ML model
    # 3. Generate text using the specified parameters
    # For now, we'll just return a simple generated text
    
    if not prompt:
        return "Please enter a prompt."
    
    return f"Another important factor in promoting healthy aging is social interaction. Social isolation and loneliness have been linked to an increased risk of cognitive decline and dementia. Maintaining strong social connections with family, friends, and community members can help to keep the brain active and engaged.\n\nIn addition to these lifestyle factors, there are also medical interventions that can help to promote healthy aging. For example, certain medications can help to slow down the progression of Alzheimer's disease and other forms of dementia. Other treatments, such as cognitive training programs and brain stimulation therapies, may also be effective in improving cognitive function in older adults.\n\nDespite these promising developments, there is still much work to be done in understanding the"

# Create the Gradio interface
with gr.Blocks() as demo:
    with gr.Row():
        # Left column - Input
        with gr.Column():
            prompt = gr.Textbox(
                label="Enter your prompt", 
                placeholder="Recent advances in neuroimaging suggest that",
                lines=3
            )
            
            with gr.Row():
                generate_btn = gr.Button("Generate", variant="primary")
                clear_btn = gr.Button("Clear")
            
            with gr.Accordion("Advanced Options", open=False):
                max_length = gr.Slider(
                    label="Maximum Length", 
                    minimum=64, maximum=1024, value=512, step=64
                )
                
                temperature = gr.Slider(
                    label="Temperature (0 = deterministic, 0.7 = creative, 1.5 = random)", 
                    minimum=0, maximum=1.5, value=0.7, step=0.1
                )
                
                top_p = gr.Slider(
                    label="Top-p (nucleus sampling)", 
                    minimum=0.1, maximum=1.0, value=0.9, step=0.1
                )
                
                top_k = gr.Slider(
                    label="Top-k", 
                    minimum=1, maximum=100, value=40, step=1
                )
                
                repetition_penalty = gr.Slider(
                    label="Repetition Penalty", 
                    minimum=1.0, maximum=2.0, value=1.1, step=0.1
                )
        
        # Right column - Output
        with gr.Column():
            output = gr.Textbox(
                label="Generated Text", 
                lines=20
            )
    
    # Example prompts
    example_prompts = [
        "Recent advances in neuroimaging suggest that",
        "The role of dopamine in learning and memory involves",
        "Explain the concept of neuroplasticity in simple terms",
        "What are the key differences between neurons and glial cells?"
    ]
    
    # Add examples
    gr.Examples(
        examples=example_prompts,
        inputs=prompt
    )
    
    # Event handlers
    generate_btn.click(
        fn=generate_text, 
        inputs=[prompt, max_length, temperature, top_p, top_k, repetition_penalty], 
        outputs=output
    )
    
    clear_btn.click(
        fn=lambda: None, 
        inputs=None, 
        outputs=[prompt, output]
    )

# Launch the app
demo.launch()