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

• Installation
• Usage
• Endpoints
• Database
• Swagger Documentation
• Contributing

1. Clone this repository to your local machine:

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:

pip install -r requirements.txt

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:

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.

• /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.

• /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_database (GET): View the data stored in the SQLite database.
• /clear_database (POST): Clear the data stored in the SQLite 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 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.

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