Feat/user-history-persistence

README.md

@@ -6,160 +6,121 @@ ApplyAI is a cloud-native, AI-powered assistant that helps streamline the job se
 
 - [Overview & Goals](#overview--goals)
 - [Architecture & Tech Stack](#architecture--tech-stack)
-- [Core features](#core-features)
-- [Data model (Firestore)](#data-model-firestore)
-- [Getting started (local development)](#getting-started-local-development)
-- [Deployment notes](#deployment-notes)
-- [Next steps](#next-steps)
+- [Core Features](#core-features)
+- [Data Model (Firestore)](#data-model-firestore)
+- [Getting Started (Local Development)](#getting-started-local-development)
+- [Testing Suite](#testing-suite)
+- [Roadmap](#roadmap)
 - [License](#license)
 
 ## Overview & Goals
 
 ApplyAI helps job seekers automate and improve their job applications. The MVP aims to:
 
-- Reduce application time by automating tailored materials.
-- Increase application throughput by making it easy to generate high-quality applications.
-- Maximize job-fit relevance by emphasizing the most relevant skills in tailored resumes.
+- **Reduce application time** by automating tailored materials.
+- **Increase application throughput** by making it easy to generate high-quality applications.
+- **Maximize job-fit relevance** by emphasizing the most relevant skills in tailored resumes using state-of-the-art LLMs.
 
 ## Architecture & Tech Stack
 
-This project is a full-stack, decoupled application.
-```
-[User Browser (localhost)] <--> [Next.js (React) Frontend] <--> [Firebase Auth]
-          |
-          v
-[FastAPI Backend (Local/Cloud)] <--> [Gemini API]
-          |
-          v
-[Firestore Database]
-```
-
-### Stack & Rationale
+This project is a full-stack, decoupled application following a modern serverless-first architecture.
 
-- **Frontend:** **React (Next.js)** + **TypeScript** + **Tailwind CSS**
-  - *Why:* A modern, production-grade React framework for fast, type-safe, and beautifully styled component-based UI.
-- **Auth:** **Firebase Authentication** (Google OAuth)
-  - *Why:* Secure, easy-to-implement identity management that integrates natively with Firestore.
-- **Backend:** **Python** + **FastAPI**
-  - *Why:* High-performance, asynchronous-first framework that's perfect for I/O-bound tasks like calling external AI APIs.
-- **AI Model:** **Google Gemini API**
-  - *Why:* High-quality generation for conversational chat and complex text-rewriting tasks.
-- **Database:** **Google Firestore (NoSQL)**
-  - *Why:* A serverless, scalable document database for storing unstructured data like chat history.
-- **DevOps:** **Docker** + **Google Cloud Run**
-  - *Why:* A containerized, serverless-first deployment that scales to zero.
-
-## Core features
+```mermaid
+graph TD;
+    User((User)) -->|Next.js/React| FE[Frontend Client];
+    FE -->|Auth| FB_Auth[Firebase Auth];
+    FE -->|JSON/REST| BE[FastAPI Backend];
+    BE -->|Query/Write| FS[(Cloud Firestore)];
+    BE -->|Prompt/Text| Gemini[Google Gemini AI];
+```
 
-1.  **Authentication (Frontend Implemented)**
-    - Secure Google Sign-In via Firebase.
-    - Global session management using React Context.
+To ensure VS Code recognizes the formatting immediately, I have placed the remaining content inside a single block. Copy everything from the box below and paste it directly after your Mermaid diagram.
 
-2.  **Resume Tailoring (Full-Stack Implemented)**
-    - `POST /resumes` endpoint that accepts a base resume and job description.
-    - Generates a tailored resume using Gemini and renders it as formatted Markdown.
-    - Saves the result to Firestore linked to the user.
+Markdown
+### Stack & Rationale
 
-3.  **AI Chat Agent (Backend Implemented)**
-    - `POST /chat` endpoint that takes a user message and returns a Gemini-powered response.
+* **Frontend:** **React (Next.js 15)** + **TypeScript** + **Tailwind CSS**
+  * Modern, type-safe UI with efficient client-side routing and global state management via React Context.
+* **Auth:** **Firebase Authentication** (Google OAuth)
+  * Secure identity management that provides unique UIDs to link data across the stack.
+* **Backend:** **Python 3.12** + **FastAPI**
+  * High-performance, asynchronous framework optimized for I/O-bound tasks like AI model inference.
+* **AI Model:** **Google Gemini 2.5 Flash**
+  * Utilized for both conversational "Career Coach" interactions and complex "Resume Tailoring" logic.
+* **Database:** **Google Firestore (NoSQL)**
+  * A serverless document database used to persist user-specific chat history and resume generation sessions.
+* **DevOps:** **GitHub Actions** + **Pytest**
+  * Automated CI/CD pipeline ensuring code quality and 100% mocked backend testing.
+
+## Core Features
+
+1.  **Authentication & Security**
+    * Secure Google Sign-In with persistent session management.
+    * Environment-driven configuration for API keys and Cloud credentials.
+2.  **Persistent AI Chat History**
+    * `POST /chat` and `GET /chats/{user_id}` endpoints.
+    * Career guidance that persists across sessions, allowing users to pick up where they left off.
+3.  **Resume Tailoring Engine**
+    * `POST /resumes` and `GET /resumes/{user_id}` endpoints.
+    * Intelligent rewriting of resumes based on job descriptions, saved to the cloud for future reference.
+
+## Data Model (Firestore)
+
+* **chats** (collection)
+  * `doc_id` (auto-generated)
+    * `user_id` (string)
+    * `messages` (array of `{ role: 'user'|'ai', content: string }`)
+    * `timestamp` (serverTimestamp)
+
+* **tailored_resumes** (collection)
+  * `doc_id` (auto-generated)
+    * `user_id` (string)
+    * `jobDescription` (string)
+    * `originalResume` (string)
+    * `tailoredResume` (string - Markdown)
+    * `createdAt` (timestamp)
+
+## Getting Started (Local Development)
 
-## Data model (Firestore)
+### Prerequisites
+* Python 3.12+ / Node.js 18+
+* Google Cloud Service Account with Firestore and Gemini API access.
 
-- `chats` (collection)
-  - `chatId` (document)
-    - `userId` (string)
-    - `messages` (array of `{ role: 'user'|'ai', content: string }`)
+### 1) Backend (`server/`)
+1. `cd server && python3 -m venv .venv && source .venv/bin/activate`
+2. `pip install -r requirements.txt`
+3. Create `.env` with `GEMINI_API_KEY` and `GOOGLE_APPLICATION_CREDENTIALS`.
+4. Run: `PYTHONPATH=. uvicorn main:app --reload --port 8000`
 
-- `tailored_resumes` (collection)
-  - `resumeId` (document)
-    - `userId` (string)
-    - `originalResume` (string)
-    - `jobDescription` (string)
-    - `tailoredResume` (string - Markdown)
-    - `createdAt` (timestamp)
+### 2) Frontend (`client/`)
+1. `cd client && npm install`
+2. Create `.env.local` with your Firebase config and `NEXT_PUBLIC_API_URL`.
+3. Run: `npm run dev`
 
-## Getting started (local development)
+## Testing Suite
 
-This repository is a monorepo containing both the `client` and `server`.
+The backend includes a robust unit testing suite using `Pytest` and `mocker`. To maintain speed and zero-cost CI, all external calls to Gemini and Firestore are fully mocked to prevent network dependency and unnecessary API costs.
 
-### Prerequisites
+**To run tests:**
+```bash
+cd server
+pytest
+```
 
-- Python 3.12+
-- Node.js 18+ and `npm`
-- A Google Cloud project with Firestore, Gemini API, and Firebase Auth enabled.
+## Deployment Notes
 
-### 1) Backend (`server/`)
+The backend is deployed to **Google Cloud Run**. The frontend is currently local but configured for deployment to Vercel or Firebase Hosting.
 
-1.  **Navigate to the server directory:**
-    ```bash
-    cd server
-    ```
-2.  **Create and activate a virtual environment:**
-    ```bash
-    python3 -m venv .venv
-    source .venv/bin/activate
-    ```
-3.  **Install dependencies:**
-    ```bash
-    pip install -r requirements.txt
-    ```
-4.  **Configure environment variables:**
-    Create a `.env` file in `server/` with:
-    ```
-    GEMINI_API_KEY="your-gemini-api-key"
-    GOOGLE_APPLICATION_CREDENTIALS="path/to/service-account.json"
-    ```
-5.  **Run the backend locally:**
-    (Run from root folder)
-    ```bash
-    PYTHONPATH=$PYTHONPATH:$(pwd)/server uvicorn server.main:app --reload --port 8000
-    ```
-    API docs: `http://127.0.0.1:8000/docs`
+* **CORS:** The FastAPI app allows requests from `http://localhost:3000`. For production, update `server/main.py` with the deployed frontend domain.
 
-### 2) Frontend (`client/`)
+## Roadmap
 
-1.  **Navigate to the client directory:**
-    ```bash
-    cd client
-    ```
-2.  **Install dependencies:**
-    ```bash
-    npm install
-    ```
-3.  **Configure environment variables:**
-    Create a `.env.local` file in `client/` with:
-    ```bash
-    # Point to your local Python backend
-    NEXT_PUBLIC_API_URL=[http://127.0.0.1:8000](http://127.0.0.1:8000)
-
-    # Firebase Configuration (Get these from Firebase Console)
-    NEXT_PUBLIC_FIREBASE_API_KEY=...
-    NEXT_PUBLIC_FIREBASE_AUTH_DOMAIN=...
-    NEXT_PUBLIC_FIREBASE_PROJECT_ID=...
-    NEXT_PUBLIC_FIREBASE_STORAGE_BUCKET=...
-    NEXT_PUBLIC_FIREBASE_MESSAGING_SENDER_ID=...
-    NEXT_PUBLIC_FIREBASE_APP_ID=...
-    NEXT_PUBLIC_FIREBASE_MEASUREMENT_ID=...
-    ```
-4.  **Run the frontend locally:**
-    ```bash
-    npm run dev
-    ```
-    App: `http://localhost:3000`
-
-## Deployment notes
-
-The backend is deployed to **Google Cloud Run**. The frontend is currently local but can be deployed to Vercel or Firebase Hosting.
-
-- **CORS:** The FastAPI app allows requests from `http://localhost:3000`. For production, update `server/main.py` with the deployed frontend domain.
-
-## Next steps
-
-- [x] Render the tailored resume as Markdown.
-- [x] Implement full user authentication (Firebase Auth).
-- [ ] Connect Frontend User ID to Backend (Save real user data).
-- [ ] Implement the frontend UI for the `/chat` endpoint.
-- [ ] Add a full CI/CD pipeline with GitHub Actions.
+- [x] **Phase 1:** Core AI Tailoring Logic & Form UI.
+- [x] **Phase 2:** Firebase Authentication & Global Context.
+- [x] **Phase 3:** Backend Persistence (Firestore) & Naming Convention Alignment.
+- [x] **Phase 4:** Mocked Test Infrastructure & History API Endpoints.
+- [ ] **Phase 5:** Frontend Hydration (Displaying historical chats/resumes in the UI).
+- [ ] **Phase 6:** Production Deployment (Cloud Run & Vercel).
 
 ## License
 

client/components/Chat.tsx

@@ -1,15 +1,17 @@
 "use client";
 import { useState } from "react";
 import ReactMarkdown from "react-markdown";
+import { useAuth } from "@/context/AuthContext";
 
 interface Message {
   role: "user" | "ai";
   content: string;
 }
 
-export default function Chat(){
+export default function Chat() {
+  const { user } = useAuth();
   const [input, setInput] = useState("");
-  const [messages, setMessages] = useState<Message[]>([])
+  const [messages, setMessages] = useState<Message[]>([]);
   const [isLoading, setIsLoading] = useState(false);
 
   const handleSend = async () => {
@@ -19,49 +21,69 @@ export default function Chat(){
     setMessages((prev) => [...prev, userMessage]);
     setInput("");
     setIsLoading(true);
-
+    let res;
     try {
-      const res = await fetch(`${process.env.NEXT_PUBLIC_API_URL}/chat`, {
-        method: "POST",
-        headers: { "Content-Type": "application/json" },
-        body: JSON.stringify({ message: userMessage.content }),
-      });
+      if (!user) {
+        res = await fetch(`${process.env.NEXT_PUBLIC_API_URL}/chat`, {
+          method: "POST",
+          headers: { "Content-Type": "application/json" },
+          body: JSON.stringify({
+            message: userMessage.content,
+            userId: "None",
+          }),
+        });
+      } else {
+        res = await fetch(`${process.env.NEXT_PUBLIC_API_URL}/chat`, {
+          method: "POST",
+          headers: { "Content-Type": "application/json" },
+          body: JSON.stringify({
+            message: userMessage.content,
+            userId: user?.uid,
+          }),
+        });
+      }
 
       if (!res.ok) throw new Error("Failed to fetch response");
 
       const data = await res.json();
-      const aiMessage: Message = { role: "ai", content: data.reply || data.response };
+      const aiMessage: Message = {
+        role: "ai",
+        content: data.reply || data.response,
+      };
       setMessages((prev) => [...prev, aiMessage]);
     } catch (error) {
       console.error(error);
       setMessages((prev) => [
         ...prev,
-        { role: "ai", content: "Sorry, I encountered an error. Please try again." },
+        {
+          role: "ai",
+          content: "Sorry, I encountered an error. Please try again.",
+        },
       ]);
     } finally {
       setIsLoading(false);
     }
   };
 
-
-  return(
- <div className="flex flex-col h-[600px] w-full max-w-2xl border border-brand-gray/30 bg-surface rounded-xl overflow-hidden shadow-2xl shadow-primary/10">
-
+  return (
+    <div className="flex flex-col h-[600px] w-full max-w-2xl border border-brand-gray/30 bg-surface rounded-xl overflow-hidden shadow-2xl shadow-primary/10">
       {/* Messages Area */}
       <div className="flex-1 overflow-y-auto p-4 flex flex-col gap-4">
         {messages.length === 0 && (
           <div className="text-center text-brand-gray mt-20">
             <p className="text-xl font-semibold text-white">Hi! I'm ApplyAI.</p>
-            <p className="text-sm">Ask me how to improve your resume or prepare for an interview.</p>
+            <p className="text-sm">
+              Ask me how to improve your resume or prepare for an interview.
+            </p>
           </div>
         )}
-        
+
         {messages.map((msg, idx) => (
           <div
             key={idx}
             className={`p-3 rounded-lg max-w-[80%] ${
               msg.role === "user"
-                ? "bg-primary text-white self-end ml-auto shadow-md" 
+                ? "bg-primary text-white self-end ml-auto shadow-md"
                 : "bg-surface border border-brand-gray/30 text-brand-gray self-start"
             }`}
           >
@@ -74,7 +96,7 @@ export default function Chat(){
             )}
           </div>
         ))}
-        
+
         {isLoading && (
           <div className="self-start bg-surface border border-brand-gray/20 p-3 rounded-lg text-brand-gray text-sm animate-pulse">
             Thinking...
@@ -102,4 +124,4 @@ export default function Chat(){
       </div>
     </div>
   );
-}
+}