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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() |