import gradio as gr
import os
import requests
from huggingface_hub import InferenceClient

# Initialize the Hugging Face Inference client for DeepSeek
def get_inference_client():
    api_token = os.environ.get("HUGGINGFACE_TOKEN")
    if not api_token:
        return None
    
    client = InferenceClient(token=api_token)
    return client

def generate_math_questions(chapter, num_questions=5, difficulty="medium"):
    """Generate mathematics questions based on the provided chapter/topic"""
    client = get_inference_client()
    
    if not client:
        return "Error: Hugging Face token not configured. Please set the HUGGINGFACE_TOKEN in your space settings."
    
    prompt = f"""Generate {num_questions} {difficulty}-level mathematics questions about {chapter}.
    
    For each question:
    1. Write a clear, well-defined question
    2. Provide a step-by-step solution
    3. Include the final answer
    
    Format your response as:
    
    ## Question 1
    [Question text]
    
    ### Solution
    [Step-by-step solution]
    
    ### Answer
    [Final answer]
    
    ## Question 2
    ...and so on
    """
    
    try:
        response = client.text_generation(
            prompt=prompt,
            model="deepseek-ai/deepseek-math-7b-instruct",
            max_new_tokens=2048,
            temperature=0.7,
            top_p=0.95,
        )
        return response
    except Exception as e:
        return f"Error generating questions: {str(e)}\n\nPlease ensure your Hugging Face token has the necessary permissions."

# Create the Gradio interface
with gr.Blocks(title="Mathematics Question Generator") as demo:
    gr.Markdown("# 🧮 Mathematics Question Generator")
    gr.Markdown("Enter a mathematics chapter or topic to generate practice questions with solutions.")
    
    with gr.Row():
        with gr.Column(scale=3):
            chapter_input = gr.Textbox(
                label="Mathematics Topic/Chapter",
                placeholder="e.g., Calculus: Integration by Parts",
                info="Be specific for better results"
            )
            
            with gr.Row():
                num_questions = gr.Slider(
                    minimum=1, 
                    maximum=10, 
                    value=5, 
                    step=1, 
                    label="Number of Questions"
                )
                
                difficulty = gr.Dropdown(
                    choices=["elementary", "easy", "medium", "hard", "advanced"],
                    value="medium",
                    label="Difficulty Level"
                )
            
            generate_button = gr.Button("Generate Questions", variant="primary")
    
    with gr.Row():
        output = gr.Markdown(label="Generated Questions")
    
    generate_button.click(
        fn=generate_math_questions,
        inputs=[chapter_input, num_questions, difficulty],
        outputs=output
    )
    
    gr.Markdown("---")
    gr.Markdown("Created by Kamagelo Mosia | Powered by DeepSeek and Hugging Face")

# Launch the app
demo.launch()