Movie Genre Classification With CI/CD Implementation in Heroku

This repository contains a movie genre classification project that predicts the genres of movies based on their overviews. It includes a trained model, a Flask web application, and instructions for local setup and usage.

Prerequisites

Python 3.8 or higher Docker (optional)

Table of Contents

1. Introduction
2. Setup
   • Local Setup
   • Docker Setup
3. Usage
   • Web Application
   • API Endpoint
4. Model Training
5. Model Evaluation
6. CI/CD
7. License

Introduction

The movie genre classification project uses natural language processing techniques to predict the genres of movies based on their overviews. It employs a machine learning model trained on a labeled dataset of movie overviews and corresponding genres.

Setup

To use the movie genre classification project, you can follow the instructions below to set it up in your local environment or as a Docker container.

Local Setup

1. Clone the repository to your local machine:

   git clone https://github.com/kndeepak/CI-CD-Pipeline-Implementation.git

2. Navigate to the project directory:

   cd Movie-Genere-CI-CD-Pipeline-Prediction

3. Install the required dependencies:

   pip install -r requirements.txt

4. Run the Flask web application:

   python app.py

Docker Setup

1. Install Docker on your machine. Refer to the official Docker documentation for instructions specific to your operating system.

2. Clone the repository to your local machine:

   git clone https://github.com/nursnaaz/Movie-Genere-CI-CD-Pipeline-Prediction.git

3. Navigate to the project directory:

   cd Movie-Genere-CI-CD-Pipeline-Prediction

4. Build the Docker image:

   docker build -t movie-genre-classification .

5. Run the Docker container:

   docker run -p 5555:5555 movie-genre-classification

Usage

Once you have set up the movie genre classification project, you can use it in two ways: through the web application or via the API endpoint.

Web Application

• Access the local web application by opening your web browser and visiting http://localhost:5555.

• Access the Heroku web application by opening your web browser and visiting https://movie-genere.herokuapp.com/

Enter a movie overview in the provided input field.

Click the "Predict" button to get the predicted genres for the movie.

Local API Endpoint

You can also make predictions using the API endpoint.

Endpoint: http://localhost:5555/predict_api Method: POST Request Payload: Parameter name: overview Parameter value: A vengeful New York transit cop decides to steal a trainload of subway fares; his foster brother, a fellow cop, tries to protect him.

Heroku Endpoint

Endpoint: https://movie-genere.herokuapp.com/predict_api Method: POST Request Payload: Parameter name: overview Parameter value: A vengeful New York transit cop decides to steal a trainload of subway fares; his foster brother, a fellow cop, tries to protect him.

Example using cURL:

curl -d "overview=A vengeful New York transit cop decides to steal a trainload of subway fares; his foster brother, a fellow cop, tries to protect him." -X POST https://movie-genere.herokuapp.com/predict_api

curl -d "overview=A vengeful New York transit cop decides to steal a trainload of subway fares; his foster brother, a fellow cop, tries to protect him." -X POST http:///localhost:5555/predict_api

Model Training

The model for movie genre classification was trained using the provided code. Here's a summary of the training process:

• The dataset used for training is the movies_metadata.csv file, which can be downloaded from Kaggle (34.45MB file within the 239MB zip file).
• The data was preprocessed and cleaned by extracting the 'overview' and 'genres' columns from the dataset.
• A TextPreprocessor class was implemented to clean the text data by removing special characters, converting to lowercase, and removing stopwords.
• The dataset was split into training and testing sets using a 80% ratio.
• The target labels were binarized using MultiLabelBinarizer.
• The movie genre classification model was built using a pipeline that includes text preprocessing, TF-IDF vectorization, and a logistic regression classifier.
• The model was trained using the training dataset.
• The trained model pipeline and the MultiLabelBinarizer were saved as model_pipeline.pkl and mlb.pkl, respectively.

Model Evaluation

After training the model, it was evaluated using the testing dataset. Here are the evaluation results:

• Accuracy: 0.16206766206766207
• F1-score: 0.5600685836094441 (micro-average F1-score for multi-label classification)

These metrics provide an indication of the model's performance in predicting the genres of movies based on their overviews.

CI/CD

This repository implements CI/CD (Continuous Integration/Continuous Deployment) using GitHub Actions and Heroku. The CI/CD pipeline ensures that whenever a code commit is made, the code is automatically built, tested, and deployed to Heroku.

The workflow in the .github/workflows/main.yml file defines the CI/CD pipeline. It includes steps for installing dependencies, running tests, and deploying the application to Heroku.

To set up CI/CD for your own repository, you can follow these steps:

• Create a Heroku account (if you don't have one already) and create a new app.

• Set up the Heroku CLI on your local machine and log in to your Heroku account.

• Add the necessary Heroku environment variables in your GitHub repository's secrets. These variables may include the Heroku API key, Heroku app name, etc.

• Push the code to the GitHub repository, and the CI/CD pipeline will automatically trigger. The workflow will build, test, and deploy the code to your Heroku app.

License

The movie genre classification project is released under the MIT License. You are free to use, modify, and distribute the code for personal and commercial purposes.