AI-Powered Survey Insights

Project Description

AI-Powered Survey Insights is a web application that leverages advanced AI models to process and analyze user queries related to two survey datasets using Retrieval-Augmented Generation (RAG). This tool provides actionable insights from:

• Dataset 1: Sustainability Research Results
• Dataset 2: Christmas Research Results

The project consists of:

• Backend: A FastAPI-based service that processes user queries and interacts with the OpenAI API.
• Frontend: A simple HTML/CSS/JavaScript interface served using Nginx.
• Dockerized Deployment: Both backend and frontend are containerized for easy deployment using Docker and Docker Compose.

Table of Contents

• Features
• Prerequisites
• Installation and Setup
• Usage Instructions
• API Documentation
• Docker Instructions
• Deployment
• License
• Acknowledgements

Features

• Interactive Query Interface: Users can ask questions about the survey datasets in natural language.
• AI-Generated Insights: Retrieves precise answers using OpenAI’s models.
• Dataset Comparison: Compare insights between Sustainability and Christmas survey datasets.
• User-Friendly Interface: Clean, intuitive UI for easy interaction.
• Dockerized Setup: Simple container-based deployment with Docker Compose.

Prerequisites

• Docker: Install Docker.
• Docker Compose: Comes bundled with Docker Desktop.
• OpenAI API Key: Obtain an API key from OpenAI.

Installation and Setup

1. Clone the Repository:

   git clone https://github.com/elcaiseri/Survey-Analysis-RAG-System.git
   cd Survey-Analysis-RAG-System

2. Set Up Environment Variables:

   Create a .env file in the project root:

   OPENAI_API_KEY=your-openai-api-key
   APP_TOKEN=your-app-token
   BACKEND_URL=http://localhost:8000
   FRONTEND_URL=http://localhost:5500
   

   Note: Replace the placeholders with actual values. Do not commit this file to version control.

3. Prepare the Data:

   Run the exploratory data analysis script to prepare the datasets:

   python eda.py

4. Build and Run the Containers:

   Use Docker Compose to build and start the services:

   docker-compose up --build

   This will build both the frontend and backend containers and start them.

Usage Instructions

1. Access the Application:

   Open your browser and go to http://localhost:5500.

2. Interact with the Application:

   • Enter a query in the input field (e.g., “How important is sustainability to consumers?”).
   • Select the relevant dataset from the dropdown (Sustainability or Christmas).
   • Click Get Insights to submit your query.

3. View Results:

   • The AI-generated response will be displayed below the form.
   • If there’s an error, it will be shown in the error message section.

API Documentation

The backend exposes a POST endpoint for querying datasets.

• Endpoint: /query
• Method: POST
• Description: Processes a user query and returns AI-generated insights.

Request Body Example:

{
  "query": "How important is sustainability to consumers?",
  "dataset": "sustainability"
}

Response Example:

{
  "answer": "Sustainability is highly important to consumers, with 75% preferring eco-friendly products."
}

Error Handling:

• If the request fails, the API returns an appropriate HTTP status code and an error message.

Docker Instructions

Building and Running the Containers

1. Build the Docker Containers:

   docker-compose build

2. Start the Containers:

   docker-compose up

3. Run in Detached Mode (Optional):

   docker-compose up -d

Stopping the Containers

docker-compose down

Rebuilding After Code Changes

If you make changes to the code, rebuild the containers:

docker-compose up --build

Deployment

To deploy the application to a production environment, consider using one of these services:

• AWS Elastic Beanstalk
• Google Cloud Run
• Azure App Service
• Heroku

Deployment Checklist:

• Securely manage environment variables (e.g., using AWS Secrets Manager).
• Configure CORS policies to allow frontend-backend communication.
• Use HTTPS in production to ensure secure data transmission.

License

This project is licensed under the MIT License. See the LICENSE file for more information.

Acknowledgements

• OpenAI: For providing the API and language models.
• FastAPI: For the robust and lightweight web framework.
• Docker: For containerization and simplified deployment.
• Nginx: For serving static files efficiently.
• Community Resources: Tutorials and documentation that helped shape this project.

Feel free to contribute to this project by opening issues or submitting pull requests. If you have any questions, contact us at [email protected].