app.py

import os
import gradio as gr
from huggingface_hub import InferenceClient

# Initialize the InferenceClient
client = InferenceClient(api_key=os.getenv("HF_TOKEN"))

# Define the function for generating the response
def generate_response(question, prompt):
    messages = [
        {"role": "user", "content": question},
        {"role": "system", "content": prompt}
    ]
    
    # Get the completion from the model
    completion = client.chat.completions.create(
        model="meta-llama/Meta-Llama-3-8B-Instruct", 
        messages=messages, 
        max_tokens=1024
    )
    
    # Return the generated response
    return completion.choices[0].message['content']

# Design the Gradio interface
def create_ui():
    with gr.Blocks() as demo:
        gr.HTML("<h1 style='color: #4CAF50; text-align: center;'>Welcome to PromptMate!</h1>")
        gr.HTML("<p style='color: #2c3e50; text-align: center;'>Ask a question and get a detailed response with the power of AI!</p>")
        
        # Add a horizontal rule for visual separation
        gr.HTML("<hr style='border-top: 2px solid #4CAF50;'>")

        gr.HTML("<h2 style='color: #34495e; text-align: center;'>Example Usage:</h2>")
        gr.HTML("<p style='color: #2c3e50; text-align: center;'>Question: <em>What is the capital of France?</em><br>Prompt: <em>Provide a detailed answer using geographical and cultural context.</em></p>")

        # Add another horizontal rule for visual separation
        gr.HTML("<hr style='border-top: 2px solid #4CAF50;'>")

        with gr.Row():
            question_input = gr.Textbox(
                label="Your Question",
                placeholder="Type your question here...",
                lines=2,
                max_lines=4,
                elem_id="question_input",
                value="What is the capital of France?"  # Example for user
            )
        
        with gr.Row():
            prompt_input = gr.Textbox(
                label="Prompt",
                placeholder="Provide a prompt to guide the response...",
                lines=4,
                max_lines=6,
                elem_id="prompt_input",
                value="Provide a detailed answer using geographical and cultural context."  # Example for user
            )
        
        with gr.Row():
            submit_button = gr.Button("Generate Response", elem_id="submit_button")
        
        output = gr.Textbox(
            label="Generated Response",
            placeholder="Your AI-generated response will appear here...",
            lines=6,
            max_lines=8,
            interactive=False,
            elem_id="output"
        )
        
        # Define the button action
        submit_button.click(
            fn=generate_response, 
            inputs=[question_input, prompt_input], 
            outputs=output
        )
        
    return demo

# Run the app
if __name__ == "__main__":
    app = create_ui()
    app.launch()