# app.pyfrom flask import Flask, request, jsonifyfrom pymongo import MongoClientimport google.generativeai as genaifrom agents import MasterAgent, DiscoverAgent, TeacherAgent, LearningTrackerAgent, RoadmapAgentapp = Flask(__name__)# MongoDB setupclient = MongoClient('mongodb://localhost:27017/')db = client['ai_tutoring_system']students_collection = db['students']# Gemini model setupgenai.configure(api_key='YOUR_GEMINI_API_KEY')model = genai.GenerativeModel('gemini-1.5-pro')# Initialize agentsmaster_agent = MasterAgent(model)discover_agent = DiscoverAgent(model)teacher_agent = TeacherAgent(model)learning_tracker_agent = LearningTrackerAgent(model)roadmap_agent = RoadmapAgent(model)@app.route('/initialize_student', methods=['POST'])def initialize_student(): student_data = request.json student_id = students_collection.insert_one(student_data).inserted_id return jsonify({"student_id": str(student_id)}), 201@app.route('/interact', methods=['POST'])def interact(): data = request.json student_id = data['student_id'] query = data['query'] # Use Master Agent to decide which agent should handle the query agent_decision = master_agent.decide(query) if agent_decision == 'discover': response = discover_agent.process(student_id, query) elif agent_decision == 'teacher': response = teacher_agent.process(query) elif agent_decision == 'learning_tracker': response = learning_tracker_agent.process(student_id, query) elif agent_decision == 'roadmap': response = roadmap_agent.process(student_id, query) else: response = "I'm not sure how to handle that request."return jsonify({"response": response})@app.route('/learning_summary/', methods=['GET'])def get_learning_summary(student_id): student = students_collection.find_one({"_id": student_id}) if not student: return jsonify({"error": "Student not found"}), 404 summary = learning_tracker_agent.generate_summary(student) return jsonify({"summary": summary})if __name__ == '__main__': app.run(debug=True)# agents.pyclass MasterAgent: def __init__(self, model): self.model = model def decide(self, query): # Use the Gemini model to decide which agent should handle the query response = self.model.generate_content(f"Decide which agent should handle this query: {query}") return response.text.strip().lower()class DiscoverAgent: def __init__(self, model): self.model = model def process(self, student_id, query): # Implement logic to gather and summarize student information passclass TeacherAgent: def __init__(self, model): self.model = modeldef process(self, query): # Implement logic to provide explanations and answer questions passclass LearningTrackerAgent: def __init__(self, model): self.model = model def process(self, student_id, query): # Implement logic to evaluate student knowledge and track progress passdef generate_summary(self, student): # Implement logic to generate a learning summary for the student passclass RoadmapAgent: def __init__(self, model): self.model = model def process(self, student_id, query): # Implement logic to create personalized learning paths pass