PlacementPilot AI

An AI-powered career readiness and technical interview preparation platform. Practice adaptive, non-repeating scenario-based mock interviews, generate multi-template ATS-optimized resumes, and scan resume compatibility against job descriptions.

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Key Features

• ** Anti-Repetition AI Mock Interviews**:

  • Stateful session memory preventing repeated questions across turns.
  • Deep scenario-based, open-ended questions spanning Python, Java, DSA, Backend, DBMS, Frontend, and HR.
  • Adaptive follow-up probes based on previous candidate answers.
  • Voice Input: Integrated browser Web Speech recognition for speaking answers naturally.
  • Detailed evaluation cards with numerical scoring (X/10), Strengths, Weaknesses, and Actionable Recommendations.
  • Comprehensive final session scorecard modal with performance tier classification.

• ** Multi-Template ATS Resume Builder**:

  • 3 professional PDF design layouts (Modern Tech Pro, Executive Classic, Minimalist ATS Clean).
  • Built with ReportLab ensuring clean PDF rendering and strict ATS parseability.
  • AI-assisted 3-sentence professional summary generator.
  • Action-verb and keyword optimization for experience and project bullet points.

• ** Resume ATS Analyzer**:

  • Upload PDF resumes to compare against any target Job Description.
  • Computes ATS Match Score (0–100%) and categorizes critical missing technical keywords.
  • Highlights resume strengths, vulnerabilities, and tailored improvement advice.

• ** Placement Analytics & History**:

  • Live dashboard tracking total interviews practiced, average performance scores, and readiness rating.
  • Domain practice distribution meters and difficulty level breakdowns.
  • Full transcript history storing all past questions, answers, and interviewer evaluations.

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Tech Stack

• Backend: Python 3.13, FastAPI, Starlette, Uvicorn
• AI & LLM: Groq Cloud API (openai/gpt-oss-120b), Prompt Engineering & Blacklist Session Memory
• Database: MySQL, SQLAlchemy ORM, PyMySQL, Cryptography
• PDF Generation & Parsing: ReportLab (Platypus & Canvas), PyPDF2
• Frontend: HTML5, Modern CSS3 (Dark Theme, CSS Variables, Glassmorphism), Vanilla JavaScript (ES6+, Web Speech API)
• Security: Passlib (Bcrypt password hashing), Session-based authentication cookies

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Quick Start Guide

1. Prerequisites

• Python 3.10+ installed
• MySQL Server running locally or remotely
• A free Groq API Key

2. Clone the Repository

git clone https://github.com/your-username/placement-pilot-ai.git
cd placement-pilot-ai

3. Set Up Virtual Environment

# Create virtual environment
python -m venv venv

# Activate on Windows
.\venv\Scripts\activate

# Activate on macOS/Linux
source venv/bin/activate

4. Install Dependencies

pip install -r requirements.txt

5. Configure Environment Variables

Create a .env file in the root directory (or copy from .env.example):

GROQ_API_KEY=your_groq_api_key_here

6. Database Setup

Ensure MySQL is running and create the database:

CREATE DATABASE placement_platform;

Configure your database credentials in app/database/connection.py if different from default.

7. Run the Application

uvicorn app.main:app --reload

Open your browser and navigate to:

http://localhost:8000

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Project Structure

├── app/
│   ├── database/          # Database models & connection configuration
│   ├── routes/            # FastAPI modular routers (auth, dashboard, interview, resume)
│   ├── services/          # Core business logic (AI interview engine, ReportLab builder, ATS analyzer)
│   ├── static/            # CSS stylesheets, client-side JS, and SVG icons
│   ├── templates/         # Jinja2 server-rendered HTML templates
│   └── main.py            # FastAPI main entrypoint & middleware setup
├── generated_resumes/     # Temporary storage for compiled PDF resumes
├── uploads/               # Temporary storage for uploaded resumes
├── .env.example           # Template environment configuration
├── .gitignore             # Git ignore rules for secrets and virtualenv
├── requirements.txt       # Python package dependencies
└── README.md              # Project documentation

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License

This project is open-source and available under the MIT License.