AI Interviewer Model

Introduction

AI Interviewer Model is an interview analysis system that leverages machine learning techniques for evaluating video and audio responses. It uses OpenCV-based preprocessing along with Support Vector Regression (SVR) and Random Forest Regression (RFR) for video analysis. For audio processing, it employs a Convolutional Neural Network (CNN). The model is deployed using Gradio for an interactive UI and Docker for containerization.

File Structure

Below is the file structure visualization of the project:

📂 AI-interviewer-model
│── 📂 src                # Trained models and checkpoints
│── 📂 flagged            # Temp Files
│── 📜 requirements.txt   # Dependencies list
│── 📜 README.md          # Project documentation
│── 📜 main.py            # Entry point of the application
│── 📜 Dockerfile         # Docker container setup

Getting Started

Follow these steps to set up and run the project.

Prerequisites

Ensure you have the following installed:

• Python 3.x
• OpenCV
• Scikit-learn
• PyTorch
• TensorFlow/Keras (for CNN model)
• NumPy
• Pandas
• Gradio
• Docker (for deployment)

Installation

1. Clone the repository:

   git clone https://github.com/hridaya14/AI-interviewer-model.git
   cd AI-interviewer-model

2. Create a virtual environment and activate it:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install dependencies:

   pip install -r requirements.txt

Running the Project

To launch gradio app:

python main.py

To build and run the Docker container:

docker build -t ai-interviewer .
docker run -p 7860:7860 ai-interviewer

Contributing

Contributions are welcome! Please fork the repository and submit a pull request with your improvements.