generateQuestion.py

# from flask import Flask, Blueprint, jsonify, send_file, abort, make_response, request
from flask import Flask, Blueprint, jsonify, send_file, abort, make_response, request, current_app

from flask_cors import CORS
import os
print(f"GOOGLE_APPLICATION_CREDENTIALS: {os.getenv('GOOGLE_APPLICATION_CREDENTIALS')}")
import io
import uuid
import requests
import re 

questions_bp = Blueprint("questions", __name__)

app = Flask(__name__)
CORS(app)

@app.route('/')
def home():
    return "Welcome to the Flask app! The server is running."

# read from env as a fallback (works for local .env and HF Secrets)
COHERE_API_KEY = os.getenv("COHERE_API_KEY", "")

def _cohere_headers():
    """
    Prefer a key set on the Flask app (e.g., in verification.py: app.config["COHERE_API_KEY"]),
    otherwise fall back to the environment variable COHERE_API_KEY.
    """
    api_key = current_app.config.get("COHERE_API_KEY") or COHERE_API_KEY
    if not api_key:
        return None
    return {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json",
    }

# (1) UPDATED: Cohere v2 Chat endpoint
COHERE_API_URL = 'https://api.cohere.com/v2/chat'

def _extract_text_v2(resp_json: dict) -> str:
    """
    v2 /chat returns:
    { "message": { "content": [ { "type": "text", "text": "..." } ] } }
    """
    msg = resp_json.get("message", {})
    content = msg.get("content", [])
    if isinstance(content, list) and content:
        block = content[0]
        if isinstance(block, dict):
            return (block.get("text") or "").strip()
    return ""

def validate_topic(topic):
    validation_prompt = f"""
   ou are a highly knowledgeable AI grammar expert. Your task is to evaluate whether the given topic relates to **English grammar** or not.

    **Input Topic:** "{topic}"

    ### **Instructions:**
    - If the input **exactly refers to** grammar concepts (such as **parts of speech**, **verb tenses**, **sentence structure**, **grammar rules**, etc.), respond with `"Grammar"`.
    - If the input **seems to be a general question or concept** that is **not directly related to grammar**, such as general knowledge, science, history, or unrelated fields, respond with `"Not Grammar"`.
    - If the input is in the form of a **question** (e.g., "What is subject-verb agreement?"), respond with `"ask grammar topics"`.
    - If the topic refers to a **specific grammar concept** (e.g., **noun**, **verb**, **preposition**, **past tense**, etc.), always classify it as `"Grammar"`.
    - **Do not include any explanations or examples**. Your answer must only be `"Grammar"`, `"Not Grammar"`, or `"ask grammar topics"`, depending on whether the topic is relevant to grammar.
    - If the input is **unclear**, err on the side of classifying it as `"Not Grammar"` rather than `"Grammar"`.

    Your response must only be one of these three options:
    - `"Grammar"`
    - `"Not Grammar"`
    - `"ask grammar topics"`
    No extra text or explanation.
    """

    headers = _cohere_headers()
    if not headers:
        return "Error: COHERE_API_KEY not set"

    # (2) UPDATED: messages payload
    payload = {
        'model': 'command-r-08-2024',
        'messages': [
            {'role': 'user', 'content': validation_prompt}
        ],
        'max_tokens': 5
    }

    try:
        response = requests.post(COHERE_API_URL, json=payload, headers=headers)
        # (3) UPDATED: v2 parsing
        validation_result = _extract_text_v2(response.json())

        if validation_result not in ["Grammar", "Not Grammar", "ask grammar topics"]:
            return "Not Grammar"
        return validation_result

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

@questions_bp.post('/generate-questions')
def generate_questions_test():
    try:
        data = request.get_json()
        topic = data.get('topic', '').strip()
        validation_result = validate_topic(topic)

        if validation_result != "Grammar":
            return jsonify({"message": "Please enter a valid **grammar topic**, not a general word or unrelated question."}), 400

        difficulty = data.get('difficulty', 'basic')
        print(f"Generating {difficulty} questions for topic: {topic}")

        if difficulty == 'basic':
            prompt = f"""
            Generate five **completely new and unique** very basic-level fill-in-the-blank grammar questions **every time** on the topic '{topic}'. 

            ### Rules:
            - Generate five unique fill-in-the-blank grammar questions based on the topic '{topic}'.
            - Each question must have exactly one blank represented by '_______' (not two blanks or underscores inside the sentence).
            - Each question must have a different theme for variety.
            - Use different sentence structures; avoid predictable patterns.
            - Avoid long words or abstract concepts.
            - Focus on the topic '{topic}', and ensure the blank is the key part of speech.
            - Each question must include the correct answer in parentheses at the end.
            - Do not include any explanations or instructions—only the five questions.
            """
        elif difficulty == 'intermediate':
            prompt = f"""
            Generate five **completely new and unique** intermediate-level fill-in-the-blank grammar questions **every time** on the topic '{topic}'.

            ### Rules:
            - Generate five unique fill-in-the-blank grammar questions based on the topic '{topic}'.
            - Each question must have exactly one blank represented by '_______'.
            - Slightly more challenging than basic-level; use a wider range of sentence structures and vocabulary.
            - Each question must have a different theme.
            - Sentences should be longer and include more detail.
            - Focus on the topic '{topic}', and ensure the blank is the key part of speech.
            - Each question must include the correct answer in parentheses at the end.
            - Do not include any explanations or instructions—only the five questions.
            """
        elif difficulty == 'expert':
            prompt = f"""
            Generate five **completely new and unique** advanced-level (C1) fill-in-the-blank grammar questions **every time** on the topic '{topic}'.

            ### Rules:
            - Generate five unique fill-in-the-blank grammar questions based on the topic '{topic}'.
            - Each question must have exactly one blank represented by '_______'.
            - More challenging than intermediate (C1); require expert-level mastery of grammar and context.
            - Ensure varied and sophisticated vocabulary; avoid basic words.
            - Each question should require nuanced comprehension; test advanced grammar patterns.
            - The blank must be the key part of the sentence (not an obvious answer).
            - Each question must include the correct answer in parentheses at the end.
            - Do not include any explanations or instructions—only the five questions.
            """
        else:
            return jsonify({"error": "Invalid difficulty level"}), 400

        headers = _cohere_headers()
        if not headers:
            return jsonify({"error": "COHERE_API_KEY not set on the server"}), 500

        # (2) UPDATED: messages payload
        payload = {
            'model': 'command-r-08-2024',
            'messages': [
                {'role': 'user', 'content': prompt}
            ],
            'max_tokens': 1000
        }

        response = requests.post(COHERE_API_URL, json=payload, headers=headers)
        print("Response status code:", response.status_code)
        print("Response content:", response.text)

        if response.status_code == 200:
            # (3) UPDATED: v2 parsing
            text = _extract_text_v2(response.json())
            # Return same style as before but adapted (text content)
            return jsonify({"text": text})
        else:
            return jsonify({"error": "Failed to fetch questions", "details": response.text}), 500
    except Exception as e:
        return jsonify({"error": str(e)}), 500

@questions_bp.post('/validate-answer')
def validate_answer():
    try:
        data = request.get_json()
        topic = data.get('topic', '')
        question = data.get('question', '')
        user_answer = data.get('user_answer', '')

        if not question or not user_answer or not topic:
            return jsonify({'error': 'Topic, question, and user answer are required'}), 400

        prompt = f"""
        You are a highly knowledgeable grammar assistant. Validate whether the user's answer to the following question is correct or not based on {topic}. If the answer is incorrect, provide a helpful hint.
        
        Topic: {topic}
        Question: "{question}"
        User's Answer: "{user_answer}"

        Is the answer correct? If not, please explain why and give a hint.
        """

        headers = _cohere_headers()
        if not headers:
            return jsonify({"error": "COHERE_API_KEY not set on the server"}), 500

        # (2) UPDATED: messages payload
        payload = {
            'model': 'command-r-08-2024',
            'messages': [
                {'role': 'user', 'content': prompt}
            ],
            'max_tokens': 100,
            'temperature': 0.7
        }

        response = requests.post(COHERE_API_URL, headers=headers, json=payload)
        print(f"Status Code: {response.status_code}")
        print(f"Response Body: {response.text}")

        if response.status_code == 200:
            # (3) UPDATED: v2 parsing
            text = _extract_text_v2(response.json())
            return jsonify({"text": text})
        else:
            return jsonify({'error': 'Failed to fetch data from Cohere API'}), 500

    except Exception as e:
        return jsonify({'error': str(e)}), 500

# Function to validate an individual answer using Cohere API
def validate_answer_with_ai(topic, question, user_answer):
    try:
        prompt = f"""
        You are a highly knowledgeable grammar assistant. Validate whether the user's answer to the following question is correct or not based on {topic}. If the answer is incorrect, provide a helpful hint.

        Topic: {topic}
        Question: "{question}"
        User's Answer: "{user_answer}"

        Is the answer correct? If not, please explain why and give a hint.
        """

        headers = _cohere_headers()
        if not headers:
            return "Error: COHERE_API_KEY not set"

        # (2) UPDATED: messages payload
        payload = {
            'model': 'command-r-08-2024',
            'messages': [
                {'role': 'user', 'content': prompt}
            ],
            'max_tokens': 200,
            'temperature': 0.7
        }

        # (1) UPDATED URL used here too
        response = requests.post(COHERE_API_URL, headers=headers, json=payload)

        if response.status_code == 200:
            # (3) UPDATED: v2 parsing
            return _extract_text_v2(response.json())
        else:
            return f"Error: {response.status_code} - {response.text}"

    except Exception as e:
        return f"An error occurred: {str(e)}"

@questions_bp.post('/validate-all-answers')
def validate_all_answers():
    try:
        data = request.get_json()
        questions = data.get('questions', [])

        if not questions:
            return jsonify({'error': 'No questions provided'}), 400

        validation_results = []

        for item in questions:
            topic = item.get('topic', '')
            question = item.get('question', '')
            user_answer = item.get('user_answer', '')

            if not topic or not question or not user_answer:
                validation_results.append({
                    'question': question,
                    'error': 'Missing required fields (topic, question, or answer).'
                })
                continue

            validation_response = validate_answer_with_ai(topic, question, user_answer)

            hint = None
            if isinstance(validation_response, str) and (
                "incorrect" in validation_response.lower() or "not correct" in validation_response.lower()
            ):
                hint = generate_hint(topic, question, user_answer)

            validation_results.append({
                'question': question,
                'user_answer': user_answer,
                'validation_response': validation_response,
                'hint': hint
            })

        return jsonify({'results': validation_results})

    except Exception as e:
        return jsonify({'error': str(e)}), 500

def generate_hint(topic, question, user_answer):
    try:
        prompt = f"""
        You are a highly skilled grammar assistant. Your task is to generate a helpful hint for the user to improve their answer based on the following question.

        Topic: {topic}
        Question: "{question}"
        User's Answer: "{user_answer}"

        If the user's answer is incorrect, provide a specific, actionable hint to help the user correct their answer.
        The hint should include:
        - Explanation of the error made by the user.
        - A hint on the correct grammatical structure or word form.
        - A hint on how to structure the sentence correctly **without revealing the exact answer**.

        Please make sure the hint is **clear** and **helpful** for the user, **without revealing the correct answer**.
        """

        headers = _cohere_headers()
        if not headers:
            return "Error: COHERE_API_KEY not set"

        # (2) UPDATED: messages payload
        payload = {
            'model': 'command-r-08-2024',
            'messages': [
                {'role': 'user', 'content': prompt}
            ],
            'max_tokens': 250,
            'temperature': 0.7
        }

        response = requests.post(COHERE_API_URL, headers=headers, json=payload)

        if response.status_code == 200:
            # (3) UPDATED: v2 parsing
            return _extract_text_v2(response.json())
        else:
            return f"Error: {response.status_code} - {response.text}"

    except Exception as e:
        return f"An error occurred: {str(e)}"

if __name__ == '__main__':
    app.register_blueprint(questions_bp, url_prefix='')  # local exposure
    app.run(host='0.0.0.0', port=5012, debug=True)