feat: spaces deployement

Readme.md

@@ -1,258 +1,5 @@
-# PrepAI – Intelligent Interview & Notes Assistant
-
-<img src='img\1.png' alt="Devecho" width="350">
-
-PrepAI is a comprehensive AI-powered platform designed to help users prepare for interviews, generate personalized quizzes, manage study notes, and even participate in real-time mock interviews using conversational AI. It blends multiple modern technologies such as FastAPI, React, ChromaDB, PostgreSQL, Vapi, Groq, and LlamaIndex into a cohesive, production-ready system.
-
 ---
-
-## 🚀 Overview
-
-PrepAI enables users to:
-
-- Conduct **AI-powered mock interviews** with real-time voice interaction.
-- Upload PDFs and **chat with notes** using vector search + RAG.
-- Generate **MCQ quizzes** from resumes or study material.
-- Maintain persistent **chat sessions**, interview transcripts, and summaries.
-- Work inside a fully containerized architecture with isolated services.
-
-This system is ideal for interview preparation platforms, ed-tech tools, or personal study automation.
-
+title: PrepAI
+sdk: docker
+app_port: 7860
 ---
-
-## 🧩 Architecture
-
-PrepAI is built with a modular full‑stack architecture:
-
-### **Backend (FastAPI)**
-
-- JWT Authentication
-- Upload & process PDFs
-- ChromaDB vector store integration
-- Streaming LLM responses
-- Vapi-powered live interview assistant
-- Quiz generation using Groq/OpenAI
-
-### **Frontend (React + TypeScript + Vite)**
-
-- User onboarding (Sign In / Sign Up)
-- PDF upload, preview, rename, deletion
-- Interactive note‑chat interface
-- Dashboard with metrics
-- Seamless Vapi interview client
-
-### **Database Layer**
-
-- **PostgreSQL** stores:
-
-  - Users
-  - PDFs
-  - Chat sessions
-  - Messages
-  - Metadata
-
-### **Vector Search Layer**
-
-- **ChromaDB** stores:
-
-  - Chunked PDF embeddings
-  - User-ingested knowledge
-
-### **AI Services**
-
-- **Groq/OpenAI** for LLM responses
-- **SentenceTransformers** for embeddings
-- **Vapi** for real-time voice conversations
-- **LlamaIndex** for PDF parsing & chunking
-
----
-
-## 📦 Directory Structure
-
-```
-aki-008-prepai/
-├── Backend/
-│   ├── app/
-│   ├── Dockerfile
-│   └── requirements.txt
-│
-├── Frontend/
-│   ├── src/
-│   ├── Dockerfile
-│   └── vite.config.ts
-│
-├── docker-compose.yml
-└── RUN.md
-```
-
----
-
-## ⚙️ Installation & Setup
-
-### **1. Clone the repository**
-
-```
-git clone <repo-url>
-cd aki-008-prepai
-```
-
-### **2. Create your `.env` file**
-
-Provide:
-
-- PostgreSQL credentials
-- Groq API key
-- OpenAI key (if needed)
-- VAPI_PRIVATE_KEY
-- VAPI_PUBLIC_KEY
-- VAPI_ASSISTANT_ID
-
-Example:
-
-```
-DATABASE_URL=postgresql+asyncpg://postgres:password@dblocalhost:5432/studentdb
-GROQ_API_KEY=your-key
-VAPI_PRIVATE_KEY=your-key
-VAPI_PUBLIC_KEY=your-key
-VAPI_ASSISTANT_ID=your-assistant-id
-```
-
-### **3. Run the system (Docker)**
-
-```
-docker-compose up --build
-```
-
-Services started:
-
-- Frontend → [http://localhost:5173](http://localhost:5173)
-- Backend → [http://localhost:8000](http://localhost:8000)
-- ChromaDB → [http://localhost:8080](http://localhost:8080)
-- PostgreSQL → [http://localhost:5347](http://localhost:5347)
-
-### **4. Dev mode (manual)**
-
-Backend:
-
-```
-cd Backend
-python run.py
-```
-
-Frontend:
-
-```
-cd Frontend
-npm install
-npm run dev
-```
-
-ChromaDB:
-
-```
-chroma run --host 0.0.0.0 --port 8080 --path ./chroma_store
-```
-
----
-
-## 🧪 Key Features in Detail
-
-### **1. PDF Upload & Notes Chat**
-
-- PDFs are chunked using PyMuPDF + LlamaIndex.
-- Embeddings generated via MiniLM.
-- Chunks stored in ChromaDB.
-- Users can open chats tied to each PDF with full chat history.
-
-### **2. AI Interview System**
-
-- Dynamic prompt generation based on job-role, experience, difficulty.
-- Real-time Vapi-based interview with:
-
-  - Emotion recognition
-  - Adjustable voice
-  - Adaptive follow-ups
-  - Strict 5-minute flow
-
-- Transcripts saved automatically.
-
-### **3. Quiz Generation**
-
-From resumes or notes:
-
-- Strict rules enforced by the SYSTEM_PROMPT
-- Always 10 MCQs with 4 options
-- JSON‑structured output
-- Options + explanations
-
-### **4. Authentication**
-
-- Secure hashing using Argon2
-- JWT tokens
-- Protected routes for all user-specific actions
-
----
-
-## 🛠️ Development Notes
-
-### **Backend**
-
-- Powered by **FastAPI** with async SQLAlchemy.
-- Auto-table creation on startup.
-- Organized into clear routers: Auth, Notes, Interview, Quiz.
-- Streaming responses for chat.
-
-### **Frontend**
-
-- Built on **React + TypeScript**.
-- Modern UI with Tailwind.
-- Routes include: Home, Dashboard, Notes, Interview.
-- ProtectedRoute ensures authentication.
-
-### **Docker Setup**
-
-`docker-compose.yml` orchestrates:
-
-- PostgreSQL database
-- ChromaDB vector server
-- Backend (Python)
-- Frontend (Nginx)
-
----
-
-## 📄 Interview Transcript Storage
-
-During every interview:
-
-- All real-time transcripts are appended in `Backend/transcripts/<call_id>.txt`.
-- Summary is appended at end of call.
-
----
-
-## 🚧 Roadmap
-
-Future improvements:
-
-- User analytics dashboard
-- Multi-file knowledge merging
-- Advanced scoring for interview responses
-- Multi-voice model selector
-- Mobile-friendly front-end layout
-
----
-
-## 🧑‍💻 Contributing
-
-Feel free to open issues or submit pull requests. Contributions are welcome for both frontend and backend.
-
----
-
-## 📜 License
-
-This project is licensed under your chosen license (MIT recommended).
-
----
-
-### ❤️ Thank you for using PrepAI!
-
-If you'd like additional documentation (API reference, UML diagrams, onboarding guide), just ask!