ResuMate - Your AI Career Companion 🤖

AI-powered resume analysis and interview preparation tool to help students and job seekers land their dream jobs.

🌟 Features

• 🔐 Simple Login - Form-based authentication (email + password)
• Resume Analysis - AI-powered analysis with ATS scoring
• Smart Chatbot - Ask questions about your resume with context memory
• Interview Preparation - Generate personalized interview questions
• Resume Improvement - Get AI-driven suggestions and enhanced resume
• Cover Letter Generator - Create tailored cover letters
• Job Search - Search multiple job boards at once
• Multi-user Support - Personal data storage with Google account integration

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🚀 Quick Start

Prerequisites

1. Groq API Key (Required for AI features)

   • Get free API key from Groq Console
   • Watch: How to Get Groq API Key

2. MySQL Database (Required)

   • Local: MySQL 8.0+
   • Heroku: ClearDB or JawsDB add-on

Local Setup

1. Clone the repository:

   git clone https://github.com/vivek34561/Resume-Tracking-And-AI-Based-Mock-Interview.git
   cd Resume-Tracking-And-AI-Based-Mock-Interview

2. Install dependencies:

   pip install -r requirements.txt

3. Set up environment variables (copy .env.example to .env):

   cp .env.example .env

4. Configure your .env file with:

   • Groq API key
   • MySQL database credentials

5. Set up the database (first run initializes tables automatically).

6. Run the app:

   streamlit run app.py

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Deploy to Heroku

This app is a Streamlit-based web app. The repo now includes everything needed to run on Heroku: Procfile, runtime.txt, and Aptfile (for ffmpeg). Follow these steps from Windows PowerShell.

1) Prerequisites

• Git installed
• Heroku account and Heroku CLI installed
• Python not required on Heroku, but helpful locally

2) One-time setup in this repo

The repo contains:

• Procfile — tells Heroku how to start Streamlit
• runtime.txt — pins Python 3.11.9
• Aptfile — installs ffmpeg for streamlit-webrtc/av
• requirements.txt — Python deps

Commit any local changes before deploying.

3) Login and create the app

heroku login
heroku create your-app-name

4) Add buildpacks (Python + Apt)

Order matters — Python first, then Apt to install ffmpeg.

heroku buildpacks:add -i 1 heroku/python
heroku buildpacks:add -i 2 heroku-community/apt

5) Set config vars

heroku config:set STREAMLIT_SERVER_HEADLESS=true
heroku config:set PYTHONUNBUFFERED=true

# Optional but recommended if you want a default for API usage
# (the UI also lets you paste the key at runtime)
heroku config:set OPENAI_API_KEY=sk-... 
heroku config:set JOOBLE_API_KEY=your_jooble_key  # if you use Jooble

6) Deploy

If your current branch is main:

git push heroku main

If your working branch is frontend_deploy (as in this repo):

git push heroku frontend_deploy:main

Heroku will detect Python, install dependencies from requirements.txt, install ffmpeg from Aptfile, and launch the web dyno using the Procfile:

web: streamlit run app.py --server.port=$PORT --server.address=0.0.0.0

7) Open and monitor

heroku open
heroku logs --tail

Notes & Troubleshooting

• API Keys: The app UI asks for your Groq API key in the sidebar. You can also set GROQ_API_KEY in Heroku config vars; the UI supports pasting at runtime.
• FFmpeg: Aptfile installs ffmpeg required by streamlit-webrtc/av. If you don’t use voice/video, you can remove Aptfile and the apt buildpack.
• Build errors on faiss-cpu/av: Ensure Heroku-22/24 stack and Python 3.11. If issues persist, try pinning versions in requirements.txt.
• Memory/timeouts: Streamlit apps are single-process; keep models lightweight. Prefer hosted APIs (OpenAI, Pinecone) to heavy local compute.
• Region: If you need EU region, create the app with --region eu.

Local run (optional)

pip install -r requirements.txt
streamlit run app.py

Then open the printed local URL in your browser.

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Backend Removal

This project now runs purely as a Streamlit frontend. The previous FastAPI backend has been removed. All features (resume analysis, Q&A, interview questions, improvements, cover letter, job search) run locally via agents.

To run locally:

pip install -r requirements.txt
streamlit run app.py

If you previously set BACKEND_URL or configured CORS, those are no longer needed.