Assessor-AI-App

Assessor AI App is a web application developed using the Java Spring Boot framework, strategically incorporating advanced artificial intelligence technologies. The application leverages OpenAI's GPT-4 and Whisper models to automate the summarization and evaluation of video interviews based on predefined criteria.

Features

• Automatic summarization of video interviews.
• Evaluation of video interviews according to specified criteria.
• Integration of OpenAI's GPT-4 and Whisper models for advanced AI capabilities.
• Containerized using Docker for easy deployment and portability.

Prerequisites

Make sure you have the following prerequisites installed:

• Java 17
• Maven
• FFMPEG
• Docker

Configuration

Modify the application.properties file to customize the application settings.

OpenAI API Configuration

Before running the application, make sure to update the OpenAI API key in the application.properties file.

openai.api.v1.completions.model=gpt-4
openai.api.v1.completions.url=https://api.openai.com/v1/chat/completions
openai.api.v1.whisper.url=https://api.openai.com/v1/audio/transcriptions
openai.api.v1.key=your-api-key

Server Configuration

The application runs on port 9090 with the context path /api/v1.

server.port=9090
server.servlet.context-path=/api/v1

Multipart Limit

spring.servlet.multipart.max-file-size=200MB
spring.servlet.multipart.max-request-size=200MB

Getting Started

1. Clone the repository:

git clone https://github.com/your-username/assessorai.git

2. Update the OpenAI API key in application.properties.
3. Build and run the application:

cd assessorai
mvn clean install
docker build -t assessorai-app .
docker run -p 9090:9090 assessorai-app

Usage

1. The application will be accessible at http://localhost:9090/api/v1.
2. Check endpoints on http://localhost:9090/api/v1/swagger-ui/index.html
3. Upload video interviews for automatic summarization and evaluation.
4. Customize evaluation criteria as needed.