GPT-3 Sentiment Analysis Project

Overview

This project allows users to interact with GPT-3 to generate text responses, perform sentiment analysis on these responses, and generate visual sentiment reports. It uses a MySQL database to store user interactions and a pie chart for visualizing sentiment results.

Prerequisites

• Python 3.9 or above
• MySQL database
• Hugging Face API Key

Setup

1. Clone the Project

Clone the repository to your local machine:

git clone <repository-url>
cd <repository-directory>

2. Install Dependencies

Install the required Python packages:

pip install -r requirements.txt

3. Set Up MySQL Database

Create the MySQL database and the necessary table:

CREATE DATABASE gpt3_project;

USE gpt3_project;

CREATE TABLE interactions (
    id INT AUTO_INCREMENT PRIMARY KEY,
    user_input TEXT NOT NULL,
    gpt_response TEXT NOT NULL,
    sentiment VARCHAR(50) NOT NULL,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

4. Configure Environment Variables

Add your Hugging Face API Key to the gpt3_integration.py file or set it as an environment variable. You may also want to create a .env file to store your MySQL password and other configuration settings.

5. Run the Application

To run the application locally:

python app.py

6. Docker Deployment

To deploy the application using Docker:

• Build the Docker image:

  docker build -t gpt3_sentiment_project .

• Run the Docker container (make sure your MySQL container is running):

  docker run -it --name gpt3_sentiment_app --link mysql-container2:mysql -p 5000:5000 gpt3_sentiment_project

Features

• Text interaction with GPT-3
• Sentiment analysis (Positive, Negative, Neutral)
• MySQL database integration to store interactions
• Pie chart visualization of sentiment analysis results
• User-friendly command-line interface

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• OpenAI for the GPT-3 model
• Hugging Face for the API integration
• MySQL for the database
• Matplotlib for data visualization