Front end

flask_app/app.py

@@ -1,7 +1,13 @@
 from flask import Flask, request, jsonify
+from flask_cors import CORS
 from flask_app.chatbot import vectorize_and_store, get_response
+import datetime
+
+from flask_app.add_sample_users import insert_sample_users
 
 app = Flask(__name__)
+CORS(app, origins="*", methods=["GET", "POST", "OPTIONS"], allow_headers=["Content-Type", "Authorization"])
+
 
 FILE_TYPES = [
     "application/pdf",  # PDF
@@ -17,40 +23,86 @@ def root():
     })
 
 
-@app.route("/upload", methods=["POST"])
+@app.route("/api/upload", methods=["POST"])
 def upload_file():
     """Handles user file uploads."""
     file = request.files.get("file")
-    # course = request.json.get("course")  # could be form or json dependng on how frontend sends request
-    # TODO: add other useful file metadata
+    course = request.form.get("course")
 
     if not file:
         return jsonify({"error": "No file provided"}), 400
+
+    if not course:
+        return jsonify({"error": "No course provided"}), 400
 
     if file.content_type not in FILE_TYPES:
         return jsonify({"error": "Unsupported file type"}), 400
 
     # Process the file and store in ChromaDB (for chatbot)
-    vectorize_and_store(file)
+    vectorize_and_store(file, course)
 
     # TODO: insert to database
 
 
     return jsonify({"message": "File uploaded & processed successfully."})
 
 
-@app.route("/chat", methods=["POST"])
+@app.route("/api/chat", methods=["POST"])
 def generate_response():
     """Generate a response to user queries."""
     query = request.json.get("query")
+    course = request.json.get("course")
 
     if not query:
         return jsonify({"error": "No query provided"}), 400
 
-    response = get_response(query)
+    response = get_response(query, course)
 
     return jsonify({'response': response})
+
+
+@app.route('/api/users', methods=['POST'])
+def create_user():
+    # Get the JSON data from request
+    user_data = request.get_json()
 
+    # Validate required fields
+    required_fields = ['googleUid', 'email']
+    for field in required_fields:
+        if not user_data.get(field):
+            return jsonify({
+                "error": f"Missing required field: {field}"
+            }), 400
+
+    try:
+        # Format the user data for MongoDB
+        user_to_insert = [{
+            "googleUid": user_data.get('googleUid'),
+            "email": user_data.get('email'),
+            "displayName": user_data.get('displayName'),
+            "photoURL": user_data.get('photoURL'),
+            "createdAt": datetime.datetime.now(datetime.timezone.utc),
+            "lastLoginAt": datetime.datetime.now(datetime.timezone.utc)
+        }]
+
+        # Insert the user into MongoDB
+        inserted_ids = insert_sample_users(user_to_insert, "User")
+
+        if not inserted_ids:
+            return jsonify({
+                "error": "Failed to create user in database"
+            }), 500
+
+        return jsonify({
+            "message": "User created successfully",
+            "userId": str(inserted_ids[0])
+        }), 201
+
+    except Exception as e:
+        return jsonify({
+            "error": f"Server error: {str(e)}"
+        }), 500
+
 
 if __name__ == '__main__':
     app.run(debug=True)

flask_app/chatbot.py

@@ -16,22 +16,22 @@
 HISTORY = []  # Store conversation history in-memory for the purpose of hackathon
 
 
-def vectorize_and_store(file):
+def vectorize_and_store(file, course):
     text = extract_text(file)
     embedding = get_embedding(text)
-    documents.add(ids=str(uuid.uuid4()), documents=text, embeddings=embedding)
+    documents.add(ids=str(uuid.uuid4()), documents=text, embeddings=embedding, metadatas={"course": course})
 
 
 def get_embedding(text):
-    return embedding_model.encode(text)
+    return embedding_model.encode(text).tolist()
 
 
-def query_documents(query_embedding, top_k=3):
-    results = documents.query(query_embeddings=query_embedding, n_results=top_k)
+def query_documents(query_embedding, course, top_k=3):
+    results = documents.query(query_embeddings=query_embedding, n_results=top_k, where={"course": course})
     return results["documents"], results["metadatas"]
 
 
-def get_prompt(query, context):
+def get_prompt(query, course, context):
     prompt = f"""
     Conversation History:
     {"\n".join([f"User: {exchange['user']}\nBot: {exchange['bot']}" for exchange in HISTORY])}
@@ -42,7 +42,7 @@ def get_prompt(query, context):
     {context}
 
     Instructions:
-    You are an AI assistant designed to answer user questions based on the provided context. Focus on providing a clear and relevant answer based on the context and the conversation history if applicable. Do not mention the process of retrieving or accessing data, and keep the conversation natural and focused on the user's needs. If a piece of information is missing, simply state that you don't know it, but avoid referencing how or why you lack that information.
+    You are an AI assistant designed to answer user questions about their {course} course, based on the provided context. Focus on providing a clear and relevant answer based on the context and the conversation history if applicable. Do not mention the process of retrieving or accessing data, and keep the conversation natural and focused on the user's needs. If a piece of information is missing, simply state that you don't know it, but avoid referencing how or why you lack that information.
 
     Answer:
     """
@@ -51,13 +51,13 @@ def get_prompt(query, context):
 
 
 
-def get_response(query):
+def get_response(query, course):
     query_embedding = get_embedding(query)
-    documents, metadatas = query_documents(query_embedding)
+    documents, _ = query_documents(query_embedding, course)
     context = "\n".join([doc for doc in documents[0]])
 
     model = genai.GenerativeModel(model_name='gemini-2.0-flash')
-    response = model.generate_content(get_prompt(query, context))
+    response = model.generate_content(get_prompt(query, course, context))
 
     HISTORY.append({"user": query, "bot": response})
 

flask_app/requirements.txt

@@ -9,4 +9,6 @@ google-generativeai
 chromadb
 sentence-transformers
 uuid
-pdfplumber
+pdfplumber
+flask-cors
+pymongo

front_end/.gitignore

@@ -11,5 +11,6 @@ docs/_build/
 .env
 .DS_Store
 privateinfobutidc.json
+node_modules/
 
 chroma_db

front_end/src/api/api.tsx

@@ -0,0 +1,36 @@
+export const fetchAssistantResponse = async (userMessage: string) => {
+    const response = await fetch('http://127.0.0.1:5000/api/chat', {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json',
+      },
+      body: JSON.stringify({ query: userMessage, course: "course2" }),
+    });
+
+    if (!response.ok) {
+      throw new Error('Failed to fetch AI response');
+    }
+
+    const data = await response.json();
+    return data;
+  };
+
+
+export const insertUser = async (userData: object) => {
+    const response = await fetch('http://127.0.0.1:5000/api/users', {
+        method: 'post',
+        headers: {
+            'Content-Type': 'application/json',
+            'Accept': 'application/json'
+        },
+        body: JSON.stringify(userData)
+    });
+
+    if (!response.ok) {
+        throw new Error('Failed to insert user to database');
+      }
+
+      const data = await response.json();
+      return data;
+
+};

front_end/src/components/AssessmentSection.tsx

@@ -1,4 +1,3 @@
-
 import React, { useState } from 'react';
 import { Card, CardContent } from "@/components/ui/card";
 import { Tabs, TabsContent, TabsList, TabsTrigger } from "@/components/ui/tabs";
@@ -61,8 +60,7 @@ const AssessmentSection: React.FC = () => {
 
   const handleUploadFile = (file: File) => {
     console.log("Assessment file uploaded:", file.name);
-    toast({
-      title: "File Uploaded",
+    toast.success("File Uploaded", {
       description: `${file.name} has been uploaded successfully.`,
       duration: 3000,
     });

front_end/src/components/ChatSection.tsx

@@ -4,6 +4,7 @@ import { Button } from "@/components/ui/button";
 import { Textarea } from "@/components/ui/textarea";
 import { ScrollArea } from "@/components/ui/scroll-area";
 import { Send } from 'lucide-react';
+import { fetchAssistantResponse } from '@/api/api';
 
 interface Message {
   id: string;
@@ -43,7 +44,7 @@ const ChatSection: React.FC = () => {
     }
   };
 
-  const handleSendMessage = () => {
+  const handleSendMessage = async () => {
     if (inputValue.trim() === '') return;
 
     // Add user message
@@ -57,17 +58,14 @@ const ChatSection: React.FC = () => {
     setMessages(prev => [...prev, userMessage]);
     setInputValue('');
 
-    // Simulate assistant response
-    setTimeout(() => {
-      const assistantMessage: Message = {
-        id: (Date.now() + 1).toString(),
-        text: "I've received your message. In a real implementation, this would connect to an AI assistant to provide relevant responses about your course materials.",
-        sender: 'assistant',
-        timestamp: new Date()
-      };
-
-      setMessages(prev => [...prev, assistantMessage]);
-    }, 1000);
+    const { response } = await fetchAssistantResponse(inputValue);
+    const assistantMessage: Message = {
+      id: (Date.now() + 1).toString(),
+      text: response,
+      sender: 'assistant',
+      timestamp: new Date()
+    };
+    setMessages(prev => [...prev, assistantMessage]);
   };
 
   const formatTime = (date: Date) => {

front_end/src/components/CourseSettingsModal.tsx

@@ -1,4 +1,3 @@
-
 import React, { useState, useEffect } from 'react';
 import { Button } from "@/components/ui/button";
 import { Input } from "@/components/ui/input";
@@ -36,6 +35,7 @@ const CourseSettingsModal: React.FC<CourseSettingsModalProps> = ({
   const [courseName, setCourseName] = useState('');
   const [instructor, setInstructor] = useState('');
   const [syllabus, setSyllabus] = useState<File | null>(null);
+  const [showRemoveConfirmation, setShowRemoveConfirmation] = useState(false);
 
   // Parse current schedule and initialize form values
   useEffect(() => {
@@ -121,6 +121,12 @@ const CourseSettingsModal: React.FC<CourseSettingsModalProps> = ({
     onClose();
   };
 
+  const handleConfirmRemove = () => {
+    onRemoveCourse();
+    setShowRemoveConfirmation(false);
+    onClose();
+  };
+
   return (
     <Dialog open={isOpen} onOpenChange={onClose}>
       <DialogContent className="sm:max-w-[800px] sm:max-h-[90vh]">
@@ -182,7 +188,7 @@ const CourseSettingsModal: React.FC<CourseSettingsModalProps> = ({
                 <h3 className="text-sm font-medium text-destructive mb-2">Danger Zone</h3>
                 <Button 
                   variant="destructive" 
-                  onClick={onRemoveCourse}
+                  onClick={() => setShowRemoveConfirmation(true)}
                   className="w-full justify-center"
                 >
                   <Trash2 className="h-4 w-4 mr-2" />
@@ -211,6 +217,25 @@ const CourseSettingsModal: React.FC<CourseSettingsModalProps> = ({
           <Button onClick={handleSave}>Save Changes</Button>
         </DialogFooter>
       </DialogContent>
+
+      {/* Confirmation Dialog */}
+      <Dialog open={showRemoveConfirmation} onOpenChange={setShowRemoveConfirmation}>
+        <DialogContent className="sm:max-w-[425px]">
+          <DialogHeader>
+            <DialogTitle>Confirm Removal</DialogTitle>
+          </DialogHeader>
+          <div className="py-4">
+            <p>Are you sure you want to remove "{courseName}"? This action cannot be undone.</p>
+          </div>
+          <DialogFooter>
+            <Button variant="outline" onClick={() => setShowRemoveConfirmation(false)}>Cancel</Button>
+            <Button variant="destructive" onClick={handleConfirmRemove}>
+              <Trash2 className="h-4 w-4 mr-2" />
+              Remove Course
+            </Button>
+          </DialogFooter>
+        </DialogContent>
+      </Dialog>
     </Dialog>
   );
 };

front_end/src/lib/auth-context.tsx

@@ -6,6 +6,7 @@ import {
   onAuthStateChanged,
   User
 } from 'firebase/auth';
+import { insertUser } from '@/api/api'
 import { auth } from './firebase';
 
 interface AuthContextType {
@@ -30,10 +31,37 @@ export function AuthProvider({ children }: { children: React.ReactNode }) {
     return () => unsubscribe();
   }, []);
 
-  const signInWithGoogle = async () => {
+const signInWithGoogle = async () => {
     const provider = new GoogleAuthProvider();
     try {
-      await signInWithPopup(auth, provider);
+      const result = await signInWithPopup(auth, provider);
+      const user = result.user;
+
+      // Create the user data object
+      const userData = {
+        googleUid: user.uid,
+        email: user.email,
+        displayName: user.displayName,
+        photoURL: user.photoURL,
+      };
+
+      // Send POST request to Flask backend API
+      try {
+        const data = await insertUser(userData);
+
+        if (data.userId) {
+          console.log('User created successfully:', data);
+          // You can store the userId in local storage or state if needed
+          localStorage.setItem('userId', data.userId);
+        } else {
+          throw new Error('Invalid response from server');
+        }
+
+      } catch (error: any) {
+        console.error('Error creating user:', error.data?.error || error.message);
+        throw new Error('Failed to create user in database');
+      }
+
     } catch (error) {
       console.error('Error signing in with Google:', error);
       throw error;
@@ -69,4 +97,4 @@ export function useAuth() {
     throw new Error('useAuth must be used within an AuthProvider');
   }
   return context;
-} 
+}