import gradio as gr
import google.generativeai as genai
from functools import lru_cache
import time

# Initialize Gemini API (replace with your actual API key)
genai.configure(api_key="AIzaSyBPQF0g5EfEPzEiGRzA3iNzJZK4jDukMvE")

# Initialize the model
model = genai.GenerativeModel('gemini-pro')


def get_coding_exercise(topic, difficulty):
    """Generate a coding exercise based on the given topic and difficulty."""
    
    prompt = f"""Create a {difficulty} Python coding exercise about {topic}. 
    Provide ONLY the problem statement and expected output , sample input and sample output. 
    Do NOT include any code or solution.
    Keep it under 100 words and make it clear and concise."""
    try:
        response = model.generate_content(prompt)
        return response.text
    except Exception as e:
        return f"Error generating exercise: {str(e)}"

def evaluate_code(exercise, user_code):
    """Evaluate the user's code submission."""
    prompt = f"""
    Exercise: {exercise}

    User's code:
    {user_code}

    Perform a concise code review addressing the following points:
    1. Correctness: Does the code solve the given problem? If not, what's missing?
    2. Efficiency: Is the solution efficient? Suggest any optimizations if applicable.
    3. Style and Best Practices: Comment on code style, readability, and adherence to Python best practices.
    4. Potential Improvements: Offer 1-2 specific suggestions for improving the code.

    Format your response as follows:
    Correctness: [Your evaluation]
    Efficiency: [Your evaluation]
    Style: [Your evaluation]
    Improvements: [Your suggestions]

    Keep the entire response under 200 words and be specific in your feedback.
    """
    try:
        response = model.generate_content(prompt)
        return response.text
    except Exception as e:
        return f"Error evaluating code: {str(e)}"

def tutor_interface(topic, difficulty):
    with gr.Row():
        gr.Markdown("Generating exercise...")
    time.sleep(0.1)  # Small delay to ensure loading message is shown
    exercise = get_coding_exercise(topic, difficulty)
    return exercise

def submit_solution(exercise, user_code):
    with gr.Row():
        gr.Markdown("Evaluating solution...")
    time.sleep(0.1)  # Small delay to ensure loading message is shown
    feedback = evaluate_code(exercise, user_code)
    return feedback

# Create the Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# Intelligent Code Tutor")
    
    with gr.Row():
        topic_input = gr.Textbox(label="Topic (e.g., 'loops', 'lists', 'functions')")
        difficulty_input = gr.Dropdown(["easy", "medium", "hard"], label="Difficulty")
    
    generate_btn = gr.Button("Generate Exercise")
    exercise_output = gr.Textbox(label="Coding Exercise", lines=10)
    
    generate_btn.click(tutor_interface, inputs=[topic_input, difficulty_input], outputs=exercise_output)
    
    code_input = gr.Code(language="python", label="Your Solution")
    submit_btn = gr.Button("Submit Solution")
    feedback_output = gr.Textbox(label="Feedback", lines=10)
    
    submit_btn.click(submit_solution, inputs=[exercise_output, code_input], outputs=feedback_output)

if __name__ == "__main__":
    demo.launch()