This repository contains a Machine Learning FastAPI project that implements sentiment analysis and rating prediction based on user input. The project provides an API with two endpoints: /predict_review for sentiment analysis and /new_ratings for predicting new ratings based on sentiment analysis results.

Before running the project, make sure you have the following:

• Python 3.x installed
• Required dependencies installed. You can install them by running the following command:

  pip install -r requirements.txt
  

To start the server and use the API, follow these steps:

1. Ensure all the prerequisites are met.
2. Open a terminal or command prompt in the project directory.
3. Run the following command to start the server:

   python main.py 
   

   The server will start running at http://localhost:8080.

• Endpoint: /
• Method: GET
• Description: A test endpoint to check if the server is running.
• Response: Returns a simple "Hello world from ML endpoint!" message.

• Endpoint: /predict_review
• Method: POST
• Description: Predicts sentiment analysis based on the given text.
• Request Body:
  • text (str): The text to analyze.
• Response: Returns the predicted sentiment as a string. Possible values are "Positive", "Neutral", or "Negative". If an error occurs during prediction, it returns an "Internal Server Error" message.

• Endpoint: /new_ratings
• Method: POST
• Description: Predicts new ratings for a user based on a list of user ratings and a sentiment analysis result.
• Request Body:
  • predicted (str): The sentiment analysis result (e.g., "Positive", "Neutral", "Negative").
  • user_rating (list): A list of user ratings.
• Response:
  • total_rating (float): The updated overall rating.
  • new_ratings (list): The list of user ratings with the new rating appended.
• Response Model: ResponseRating
• Response Model Fields:
  • total_rating (float): The updated overall rating.
  • new_ratings (list): The list of user ratings with the new rating appended.

• If this is your first time running the project, make sure to install the required dependencies by running pip install -r requirements.txt.
• You can access the API documentation easily by visiting http://localhost:8080/docs in your browser after starting the server.
• The model used for sentiment analysis can be either an h5 model (nlp_model.h5) or a saved model (my_model_folder). Make sure to uncomment the appropriate line in main.py based on the model type you are using.

Contributions are welcome! If you find any issues or want to add new features, feel free to open an issue or submit a pull request.

This project is licensed under the MIT License.