import openai
import numpy as np
import pandas as pd
from flask import Flask, request, jsonify
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split

# Initialize Flask app
app = Flask(__name__)

# Set up OpenAI API Key
openai.api_key = "YOUR_API_KEY_HERE"

# Mock data for user profiles, questions, and learning materials
learners_data = {}
materials = {
    "beginner": ["Intro to AI", "Basic Math", "Learning Techniques"],
    "intermediate": ["Data Structures", "Algorithms", "Intro to Machine Learning"],
    "advanced": ["Deep Learning", "Neural Networks", "Optimization Techniques"]
}
questions_data = {
    "beginner": ["What is AI?", "Define machine learning.", "Explain a basic algorithm."],
    "intermediate": ["Describe a data structure.", "Explain sorting algorithms.", "What is a neural network?"],
    "advanced": ["Explain backpropagation.", "Describe the optimization process in deep learning.", "What is reinforcement learning?"]
}

# Initialize an ML model for performance prediction
def initialize_prediction_model():
    data = pd.read_csv('sample_user_data.csv')  # Sample historical data
    X = data[['progress', 'test_scores', 'interactions']].values
    y = data['final_score'].values
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
    model = RandomForestRegressor()
    model.fit(X_train, y_train)
    return model

performance_model = initialize_prediction_model()

# Helper function for chatbot responses using OpenAI
def generate_response(prompt):
    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt=prompt,
        max_tokens=100
    )
    return response.choices[0].text.strip()

# Function to curate content based on user progress and level
def curate_content(user_id):
    progress = learners_data.get(user_id, {}).get("progress", 0)
    if progress < 30:
        return materials["beginner"]
    elif 30 <= progress < 70:
        return materials["intermediate"]
    else:
        return materials["advanced"]

# Function to generate adaptive questions based on user level
def generate_adaptive_question(user_id):
    progress = learners_data.get(user_id, {}).get("progress", 0)
    if progress < 30:
        question = np.random.choice(questions_data["beginner"])
    elif 30 <= progress < 70:
        question = np.random.choice(questions_data["intermediate"])
    else:
        question = np.random.choice(questions_data["advanced"])
    return question

# Function to provide personalized feedback based on user performance
def generate_feedback(user_id):
    performance = learners_data.get(user_id, {}).get("test_scores", [])
    if not performance:
        return "Please complete some assessments to receive feedback."
    
    avg_score = np.mean(performance)
    if avg_score > 80:
        return "Excellent! You are mastering the content well."
    elif 50 <= avg_score <= 80:
        return "Good job! Keep going and review the areas where you scored lower."
    else:
        return "It looks like you're struggling in some areas. Try reviewing the basics and work on practice problems."

# Function to predict future performance based on interactions
def predict_performance(user_id):
    user_data = learners_data.get(user_id, {})
    progress = user_data.get("progress", 0)
    test_scores = user_data.get("test_scores", [0])
    avg_score = np.mean(test_scores)
    interactions = user_data.get("interactions", 0)
    
    prediction = performance_model.predict([[progress, avg_score, interactions]])
    return prediction[0]

# Routes
@app.route('/register', methods=['POST'])
def register_user():
    user_id = request.json['user_id']
    learners_data[user_id] = {"progress": 0, "test_scores": [], "interactions": 0}
    return jsonify({"status": "success", "message": f"User {user_id} registered."})

@app.route('/content', methods=['GET'])
def get_content():
    user_id = request.args.get('user_id')
    if user_id in learners_data:
        content = curate_content(user_id)
        return jsonify({"content": content})
    else:
        return jsonify({"status": "error", "message": "User not found."})

@app.route('/question', methods=['GET'])
def get_question():
    user_id = request.args.get('user_id')
    if user_id in learners_data:
        question = generate_adaptive_question(user_id)
        learners_data[user_id]["interactions"] += 1
        return jsonify({"question": question})
    else:
        return jsonify({"status": "error", "message": "User not found."})

@app.route('/submit_answer', methods=['POST'])
def submit_answer():
    user_id = request.json['user_id']
    score = request.json['score']
    
    if user_id in learners_data:
        learners_data[user_id]["test_scores"].append(score)
        learners_data[user_id]["progress"] += 10
        return jsonify({"status": "success", "message": "Answer submitted."})
    else:
        return jsonify({"status": "error", "message": "User not found."})

@app.route('/feedback', methods=['GET'])
def get_feedback():
    user_id = request.args.get('user_id')
    if user_id in learners_data:
        feedback = generate_feedback(user_id)
        return jsonify({"feedback": feedback})
    else:
        return jsonify({"status": "error", "message": "User not found."})

@app.route('/predict_performance', methods=['GET'])
def performance_prediction():
    user_id = request.args.get('user_id')
    if user_id in learners_data:
        prediction = predict_performance(user_id)
        return jsonify({"predicted_performance": prediction})
    else:
        return jsonify({"status": "error", "message": "User not found."})

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