IMDB movie search and review sentiment analysis app

This project uses a Python library called FastAPI to serve a pretrained sentiment analysis model created using Tensorflow as a REST API service.

Available Scripts

In the project root directory you will need to pip install using the requirements.txt into a new Conda(or equivalent) environment:

pip install -r requirements.txt

In the project root directory, you can run:

python -m uvicorn main:app --host 0.0.0.0 --port 8000 --reload

This will run the server in development mode and will reload when changes are made.

or simply run:

python main.py

API endpoints

/train POST

Trains the model.

/predict POST

Make prediction with request body {"text": "This movie is not very good. I won't see it again"}.

/evaluate-model GET

Returns the model accuracy eg {"results": {"loss": 0.75, "accuracy": 0.62}}.

/save-model-weights GET

Saves the trained weights to a file called model_weights.h5.

/load-model-weights GET

Loads the model weights.

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React front end app

.env and OMDBb API key

You will need to visit OMDb API and get a freely available API key and then make a .env file in the client directory.

.env should contain REACT_APP_API_KEY=<OMDb API key>

Available Scripts

npm start

Runs the app in the development mode.
Open http://localhost:3000 to view it in the browser.

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TODO

React app

• Improve the styles of the app, at the moment it is a little more functionality over style.

API

• Improve how the model runs sentiment analysis on the provided text, at the moment it is really a bad at telling whether the review text is positive or negative.