NLP Quickbook: Text Classification with Flask

A Python NLP project for building, saving, reusing, and serving a small text classification model through a Flask API.

📌 Overview

NLP Quickbook demonstrates a compact machine learning workflow for natural language processing in Python. The project focuses on a text classification example using the Scikit-Learn stack and exposes the trained model through a Flask API.

The workflow described by the project includes:

• Building a small text classification model
• Writing the trained model to disk
• Loading and reusing the saved model for prediction
• Serving predictions through a Flask-based API
• Providing a simple web page for classifying new movie reviews

The original project description is: NLP in Python with Deep Learning.

🧰 Technology Stack

Technology	Purpose
Python 3	Runtime environment
Scikit-Learn	Machine learning stack for the text classification model
Flask	API and simple web interface
pip	Python dependency installation

✨ Features

• Text classification workflow for movie review-style input
• Model training step that saves the trained model to disk
• Prediction step that reuses the saved model
• Flask API for serving model predictions
• Simple local web page for entering and classifying new reviews
• Lightweight structure suitable for learning deployment basics

🚀 Running Locally

To run the project locally, use the workflow described in the project documentation:

1. Install Python 3 and pip.
2. Clone the repository.
3. Navigate to the working directory.
4. Install dependencies:

pip install -r requirements.txt

5. Run the Flask API:

python api.py

6. Open a browser and visit:

http://localhost:8000

🧪 Project Workflow

The project is organized around a simple machine learning deployment flow:

1. model_train.py builds a small text classification model and writes it to disk.
2. model_predict.py reloads the saved model and uses it for prediction.
3. api.py exposes the prediction workflow through Flask.

This makes the project useful as a practical example of moving from a trained NLP model to a locally served API.

🎯 Use Cases

NLP Quickbook is relevant for learning and demonstration scenarios such as:

• Understanding how text classification models can be trained in Python
• Practicing model persistence and reuse
• Learning how Flask can expose machine learning predictions
• Creating a small local web interface for NLP classification
• Demonstrating a basic end-to-end machine learning deployment workflow

❓ FAQ

What does this project classify?

The README describes a small text classification model used to classify new movie reviews.

Is this a Flask API project?

Yes. The model is exposed using Flask through api.py.

Does the project include model training?

Yes. The workflow includes training a text classification model in model_train.py.

Can the trained model be reused?

Yes. The trained model is written to disk and reused by model_predict.py.

What URL is used locally?

When the API is running locally, the documented browser URL is http://localhost:8000.

📄 License

No license information was provided in the available project description.