reading.py

from flask import Flask, Blueprint, request, jsonify, current_app
import openai
import random
import os
from flask_cors import CORS

# --- Blueprint ---
reading_bp = Blueprint("reading", __name__)

# app = Flask(__name__)

app = Flask(__name__)
CORS(app)  

_OPENAI_API_KEY_FALLBACK = os.getenv("OPENAI_API_KEY", "")
# Set up your OpenAI API key (replace this with your own API key)
# openai.api_key = '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'  # Replace with your actual OpenAI API key

def _ensure_openai_key():
    """Set openai.api_key from app config or env before each API call."""
    api_key = (current_app.config.get("OPENAI_API_KEY")
               if current_app else None) or _OPENAI_API_KEY_FALLBACK
    if api_key:
        openai.api_key = api_key

# Function to generate content dynamically based on the topic and difficulty level
def generate_content(topic, difficulty):
    _ensure_openai_key()
    try:
        # Define instructions based on difficulty level
        if difficulty == "easy":
            instruction = f"Write a very simple and basic explanation about {topic} for children aged 6-8. Use very simple words and short sentences."
        elif difficulty == "medium":
            instruction = f"Write a detailed and engaging explanation about {topic} for children aged 9-12. Use simple words but include more details."
        else:  # Hard difficulty
            instruction = f"Write an in-depth explanation about {topic} for children aged 13-16. Use more complex words and provide deeper insights into the topic."

        # Call OpenAI API to generate the content
        response = openai.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=[
                {"role": "system", "content": "You are a friendly teacher explaining concepts to students."},
                {"role": "user", "content": instruction}
            ],
            max_tokens=700,
            temperature=0.7
        )

        content = response.choices[0].message.content.strip()
        return content

    except Exception as e:
        return f"Error generating content: {str(e)}"

# Function to generate multiple-choice questions from content based on difficulty level
def generate_questions(content, difficulty):
    _ensure_openai_key()
    try:

        # Split the content into sentences or key points and shuffle them
        content_sentences = content.split(".")  # Assuming content is in sentence form. If not, modify accordingly.
        random.shuffle(content_sentences)

        # Adjust question complexity based on difficulty
        if difficulty == "easy":
            question_instruction = "Generate 3 very simple multiple-choice questions based on the content. The questions should be very easy to understand."
        elif difficulty == "medium":
            question_instruction = "Generate 3 multiple-choice questions with moderate difficulty based on the content."
        else:  # Hard difficulty
            question_instruction = "Generate 3 challenging multiple-choice questions that require deep understanding of the content."

        # prompt = f"{question_instruction}\nContent:\n{content}\n\nFormat the output like this:\n\n1. Question: What is XYZ?\nOptions: [Option 1, Option 2, Option 3, Option 4]\nCorrect Answer: Option 1\n\n2. Question: Why does XYZ happen?\nOptions: [Option 1, Option 2, Option 3, Option 4]\nCorrect Answer: Option 2"


        prompt = f"{question_instruction}\nContent:\n{'. '.join(content_sentences[:3])}\n\nFormat the output like this:\n\n1. Question: What is XYZ?\nOptions: [Option 1, Option 2, Option 3, Option 4]\nCorrect Answer: Option 1\n\n2. Question: Why does XYZ happen?\nOptions: [Option 1, Option 2, Option 3, Option 4]\nCorrect Answer: Option 2"

        response = openai.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=[
                {"role": "system", "content": "You are a helpful assistant who generates educational multiple-choice questions."},
                {"role": "user", "content": prompt}
            ],
            max_tokens=700,
            temperature=0.7
        )

        questions = response.choices[0].message.content.strip()
        return questions

    except Exception as e:
        return f"Error generating questions: {str(e)}"

@reading_bp.route('/generate_content', methods=['POST'])
# @app.route('/generate_content', methods=['POST'])
def generate_content_route():
    data = request.json
    topic = data.get('topic')
    difficulty = data.get('difficulty', 'medium')  # Default to medium if not provided

    if not topic:
        return jsonify({"error": "Topic is required"}), 400

    if difficulty not in ["easy", "medium", "hard"]:
        return jsonify({"error": "Invalid difficulty level. Choose 'easy', 'medium', or 'hard'."}), 400

    content = generate_content(topic, difficulty)
    return jsonify({"content": content})

@reading_bp.route('/generate_questions', methods=['POST'])
# @app.route('/generate_questions', methods=['POST'])
def generate_questions_route():
    data = request.json
    content = data.get('content')
    difficulty = data.get('difficulty', 'medium')  # Default to medium if not provided

    if not content:
        return jsonify({"error": "Content is required"}), 400

    if difficulty not in ["easy", "medium", "hard"]:
        return jsonify({"error": "Invalid difficulty level. Choose 'easy', 'medium', or 'hard'."}), 400

    questions = generate_questions(content, difficulty)
    return jsonify({"questions": questions})

@reading_bp.route('/validate_answer', methods=['POST'])
# @app.route('/validate_answer', methods=['POST'])
def validate_answer():
    question = request.json.get('question')
    selected_answer = request.json.get('selected_answer')

    if not question or not selected_answer:
        return jsonify({"error": "Question and answer are required"}), 400

    # Ensure both answers are stripped of leading/trailing spaces before comparison
    correct_answer = question["correct_answer"].strip()
    selected_answer = selected_answer.strip()

    # Print the correct answer to the backend console for debugging
    print(f"Correct Answer: {correct_answer}")

    is_correct = selected_answer == correct_answer

    return jsonify({"is_correct": is_correct, "correct_answer": correct_answer})

# if __name__ == '__main__':
#     app.run(debug=True)

# if __name__ == '__main__':
#     app.run(host='0.0.0.0', port=5001)

# --- Optional: allow this file to run standalone locally while still using the blueprint ---
if __name__ == '__main__':
    app = Flask(__name__)
    CORS(app)
    # For local runs, pull key from env; no hard-coding
    app.config["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY", "")
    app.register_blueprint(reading_bp, url_prefix='')
    app.run(host='0.0.0.0', port=5001, debug=True)