AI Interview Training Coach

AI Interview Training Coach is an interactive web application designed to help users practice technical interviews with real-time feedback. Using LLMs (Large Language Models), the platform analyzes user responses, generates follow-up questions, provides detailed content and delivery analysis, and offers overall summaries and personalized coaching comments.

Thanks to the sponsor of hackathon Boson AI and their amazing API for Higgs Audio V2.

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Demo Video

Check out the demo video on YouTube: Watch Demo Check out my Technical Report here: Report

Features

• Interactive Interview Practice
• Real-Time Analysis
• Overall Summary
• Session History
• Data-Driven Insights
• Resume-Based Question Generation

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Tech Stack

• Frontend: HTML, CSS, JavaScript, Bootstrap 5, Chart.js
• Backend: Python, Flask
• AI Models: OpenAI GPT-4o-mini, langchain, RAG, Higgs Audio V2
• Audio Processing: Higgs Audio V2 for real-time speech-to-text analysis

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Screenshot

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Installation

0. Make sure you have FFMPEG and Python installed

1. Clone the repository

git clone https://github.com/AlezHibali/Interactive-Interview-Practice-with-Higgs.git
cd Interactive-Interview-Practice-with-Higgs

2. Create a virtual environment

python -m venv env
source env/bin/activate  # Linux/Mac
env\Scripts\activate     # Windows

3. Install dependencies

cd backend
pip install -r requirements.txt

4. Set API Keys

export OPENAI_API_KEY="your_openai_api_key"
export BOSON_API_KEY="your_boson_api_key"

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Usage

1. Start the Flask backend

cd backend
python app.py

2. Open the frontend

• Open localhost:5000/index.html in a browser, or deploy using a web server.

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Project Structure

ai-interview-coach/
├─ backend/
│  ├─ app.py                 # Main Flask app
│  ├─ config.py              # Configuration
│  ├─ higgs_client.py        # Higgs Audio V2 integration
│  ├─ llm_client.py          # LLM interface (GPT-4o-mini)
│  ├─ model_prompts.py       # Prompt templates
│  ├─ rag_question.py        # RAG question generation
│  ├─ requirements.txt       # Python dependencies
│  ├─ tmp/                   # Temporary files
│  ├─ uploaded_resumes/      # Uploaded resumes
│  └─ __pycache__/           # Python cache
├─ frontend/
│  ├─ index.html             # Landing page
│  ├─ new_session.html       # Start session page
│  ├─ practice.html          # Active practice page
│  ├─ history.html           # Session history page
│  ├─ app.js                 # Main JS
│  ├─ dashboard.js           # Dashboard JS
│  ├─ practice.js            # Practice session JS
│  └─ static/
│     ├─ style/              # CSS files
│     └─ src/img/            # Screenshots
└─ README.md

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Contact

• Developer: Daixin Tian
• Email: ali.daixin.tian@gmail.com
• GitHub: https://github.com/AlezHibali