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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)
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