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https://github.com/donmaruko/sentiment-analysis-api

Flask-based API for sentiment analysis using deep learning models and includes endpoints for text and file input, database storage, and integrated Swagger documentation.
https://github.com/donmaruko/sentiment-analysis-api

api deep-learning deep-neural-networks flask keras lstm machine-learning neural-network rnn scikit-learn scikitlearn-machine-learning sklearn sqlite3 swagger swagger-ui tensorflow

Last synced: 5 days ago
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Flask-based API for sentiment analysis using deep learning models and includes endpoints for text and file input, database storage, and integrated Swagger documentation.

Host: GitHub
URL: https://github.com/donmaruko/sentiment-analysis-api
Owner: donmaruko
Created: 2023-09-10T16:00:50.000Z (over 1 year ago)
Default Branch: main
Last Pushed: 2023-10-04T15:40:24.000Z (over 1 year ago)
Last Synced: 2024-11-09T14:30:58.310Z (2 months ago)
Topics: api, deep-learning, deep-neural-networks, flask, keras, lstm, machine-learning, neural-network, rnn, scikit-learn, scikitlearn-machine-learning, sklearn, sqlite3, swagger, swagger-ui, tensorflow
Language: Python
Homepage:
Size: 5.29 MB
Stars: 0
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

README

# Sentiment Analysis API with Flask and Swagger

This is a Flask-based API for performing sentiment analysis using pre-trained deep-learning models. It includes endpoints for both text and file input, and it stores the analyzed data in a SQLite database. Swagger is integrated for easy API documentation.

This repo contains:
- the flask app .py and .ymls
- .py files for the LSTM and RNN models
- .csv files used for training
- neural network calculations report
- overall presentation

## Table of Contents

- [Installation](#installation)
- [Usage](#usage)
- [Endpoints](#endpoints)
- [Database](#database)
- [Swagger Documentation](#swagger-documentation)
- [Contributing](#contributing)

## Installation

1. Clone this repository to your local machine:

```shell
git clone https://github.com/donmaruko/Sentiment-Analysis-API.git
```

2. Install the required Python packages by running the following command in your project directory:

```shell
pip install -r requirements.txt
```

## Usage

1. Make sure you have completed the installation steps. Before running app.py, please run all the model.py files to generate the pickle files

2. Start the Flask application:

```shell
python app.py
```

This will start the Flask development server, and your API will be accessible at `http://localhost:8000/docs`.

3. You can use the API endpoints to perform sentiment analysis on text input or file input.

## Endpoints

### Text Input Endpoints

- `/lstm` (POST): Perform sentiment analysis using an LSTM model with text input.
- `/rnn` (POST): Perform sentiment analysis using an RNN model with text input.
- `/nn` (POST): Perform sentiment analysis using an MLPClassifier model with text input.

### File Input Endpoints

- `/lstmfile` (POST): Perform sentiment analysis using a LSTM model with file input.
- `/rnnfile` (POST): Perform sentiment analysis using a RNN model with file input.
- `/nnfile` (POST): Perform sentiment analysis using a MLPClassifier model with file input.

### View and Clear Database

- `/view_database` (GET): View the data stored in the SQLite database.
- `/clear_database` (POST): Clear the data stored in the SQLite database.

## Database

The API uses an SQLite database (`data.db`) to store the analyzed data. You can view the stored data using the `/view_database` endpoint and clear the database using the `/clear_database` endpoint.

## Swagger Documentation

Swagger is integrated to provide API documentation. You can access the Swagger UI by visiting `http://localhost:8000/docs/` in your web browser. The Swagger documentation provides detailed information about each API endpoint, including request and response examples.

## Contributing

Contributions are welcome! If you have any suggestions, bug reports, or feature requests, please create an issue or submit a pull request.