app.py

import os
import gradio as gr
import openai
from sentence_transformers import SentenceTransformer, util

# Load similarity model
similarity_model = SentenceTransformer('all-MiniLM-L6-v2')

# Get OpenAI API key from Hugging Face secret
openai.api_key = "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"

def generate_unique_questions(topic, difficulty, num_questions, constraints):
    base_prompt = f"""
    You are an AI for generating lab experiment questions.
    Topic: {topic}
    Difficulty: {difficulty}
    Constraints: {constraints if constraints else "None"}.
    
    Task:
    - Generate {num_questions} unique but equivalent lab questions
    - Ensure same difficulty level for all
    - Avoid repeating exact wording
    - Keep questions answerable in similar time
    - Return them in a numbered list.
    """

    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=[{"role": "system", "content": "You are an expert question generator for labs."},
                  {"role": "user", "content": base_prompt}],
        temperature=0.9
    )

    # Extract questions
    raw_questions = response.choices[0].message['content'].strip().split("\n")
    questions = [q.strip() for q in raw_questions if q.strip()]

    # Ensure uniqueness (semantic check)
    final_questions = []
    embeddings = []

    for q in questions:
        emb = similarity_model.encode(q, convert_to_tensor=True)
        if not any(util.cos_sim(emb, e) > 0.85 for e in embeddings):
            embeddings.append(emb)
            final_questions.append(q)

    return "\n".join(final_questions)


# Gradio UI
with gr.Blocks() as demo:
    gr.Markdown("# 🔬 AI Lab Question Generator")
    gr.Markdown("Generate **unique but equivalent** lab questions for each student.")
    
    topic = gr.Textbox(label="Lab Topic", placeholder="e.g., Ohm's Law Experiment")
    difficulty = gr.Dropdown(["Easy", "Medium", "Hard"], label="Difficulty", value="Medium")
    num_questions = gr.Slider(1, 20, value=5, step=1, label="Number of Questions")
    constraints = gr.Textbox(label="Extra Constraints (Optional)", placeholder="e.g., include diagram-based question")
    
    generate_btn = gr.Button("Generate Questions")
    output = gr.Textbox(label="Generated Questions", lines=10)
    
    generate_btn.click(generate_unique_questions, 
                       inputs=[topic, difficulty, num_questions, constraints], 
                       outputs=output)

demo.launch()