app.py

from flask import Flask, request, jsonify
from flask_cors import CORS
from transformers import pipeline
import nltk

# The SSL and nltk.download sections have been removed.
# The 'punkt' tokenizer is now pre-installed via the Dockerfile.

# --- Model Loading ---
# We use a T5 (Transformer) model fine-tuned for Question Generation.
print("Loading question generation model...")
# The invalid 'revision' parameter has been removed.
# The pipeline will now load the latest version of the model.
question_generator = pipeline("text2text-generation", model="valhalla/t5-base-qg-hl")
print("Model loaded successfully!")


# --- Flask App Setup ---
app = Flask(__name__)
# Enable Cross-Origin Resource Sharing (CORS) to allow your frontend
# to communicate with this API.
CORS(app)

# --- API Endpoint ---
@app.route('/generate-quiz', methods=['POST'])
def generate_quiz():
    """
    This function handles the API request to generate a quiz.
    It expects a JSON payload with a 'context' key.
    """
    # Get the JSON data from the request
    data = request.get_json()
    if not data or 'context' not in data:
        return jsonify({'error': 'No context provided in the request.'}), 400

    context = data['context']
    
    # 1. Split the context into individual sentences using the pre-downloaded tokenizer.
    sentences = nltk.sent_tokenize(context)
    
    # We'll limit the number of questions to avoid long processing times.
    max_questions = 10
    sentences = sentences[:max_questions]

    generated_questions = []
    
    print(f"Generating questions for {len(sentences)} sentences...")

    # 2. Generate a question for each sentence.
    for sentence in sentences:
        try:
            # The model generates a string which might contain multiple questions
            # separated by <sep>. We take the first one.
            output = question_generator(sentence, max_length=64, num_beams=4)
            question = output[0]['generated_text'].split('<sep>')[0].strip()
            # Basic validation to ensure the question is meaningful
            if question.endswith('?') and len(question.split()) > 3:
                 generated_questions.append({'question': question, 'context': sentence})
        except Exception as e:
            print(f"Error generating question for sentence: '{sentence}'. Error: {e}")


    print(f"Successfully generated {len(generated_questions)} questions.")

    # 3. Return the list of questions as a JSON response.
    return jsonify({'quiz': generated_questions})