Movie Database Project

A modern Python-based movie database application for managing, analyzing, and exploring movie collections.

The project combines:

• API integration
• SQLite storage
• JSON persistence
• website generation
• analytics
• fuzzy search
• and multi-user support

inside a clean and extensible Python architecture.

The application is designed both as:

• a practical learning project
• and a reusable Python project template for future applications.

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Screenshot

Generated Movie Website

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Features

• CRUD functionality (Create, Read, Update, Delete)
• Movie analytics:
  • top-rated movies
  • least-rated movies
  • statistics and rankings
• Nested movie data structures
• Persistent JSON storage
• SQLite database integration
• Automatic movie data fetching from external APIs
• Website generation with:
  • movie posters
  • IMDb links
  • hover effects
  • responsive layout
• Personalized multi-user movie collections
• Intelligent fuzzy movie search
• Detailed movie information pages
• Logging and configuration management
• Modular Python project structure
• Unit tests and smoke tests
• Environment variable support via .env
• Extensible architecture for future frameworks and features

And much more.

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Tech Stack

• Python
• SQLite
• SQLAlchemy
• Requests
• HTML / CSS
• Matplotlib
• OMDb API

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Architecture

The project is separated into multiple layers:

• API layer
• Storage layer
• Website generation layer
• Helper utilities
• Export layer

The application supports both:

• JSON-based persistence
• and SQLite database storage

to demonstrate different storage approaches.

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Screenshots

Generated Movie Website

Add screenshots here later:

docs/screenshots/movie_website.png

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Project Structure

project/
│
├── README.md
├── api
│   └── omdb_api.py
├── .env.example
├── data
│   ├── blockbusters.py
│   ├── exports
│   │   ├── index.html
│   │   ├── movie_histogram.png
│   │   └── style.css
│   ├── movies.db
│   └── movies.json
├── helpers
│   └── display_formats.py
├── movie_db.py
├── movie_storage
│   ├── movie_storage_json.py
│   └── movie_storage_sql.py
├── requirements.txt
├── templates
│   └── index_template.html
└── website.py

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Installation

Clone the repository and install the required dependencies.

Clone Repository

git clone <repository-url>
cd <project-folder>

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Install Dependencies

All required Python packages are listed in requirements.txt.

Install them with:

pip install -r requirements.txt

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Environment Variables

The OMDb API requires authentication.

Create a local .env file:

API_KEY=YOUR_API_KEY

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Run the Application

Start the project with:

python movie_db.py

or depending on your system:

python3 movie_db.py

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Output

After execution, the generated website will be available locally.

Open the following file in your browser:

./data/exports/index.html

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Example Workflow

After starting the application, the user selects or creates a personal movie collection profile.

Example:

Select user:
0. Create new user
1. Alice
2. Bob

> Alice selected

The selected user now works inside their own personalized movie environment.

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Example Session

1. List Movies

Choose option: 1

Example output:

Interstellar (2014) - Rating: 8.7
Inception (2010) - Rating: 8.8

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2. Add Movies

Choose option: 2
Movie title: Interstellar

The application automatically:

• fetches movie data from the API
• downloads ratings and poster URLs
• stores the movie in SQLite
• assigns the movie to the selected user

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3. Search Movies

Choose option: 7
Search: inter

The application supports intelligent fuzzy movie searching.

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4. Show Movie Details

Choose option: 12
Movie: Interstellar

Detailed information includes:

• Title
• Year
• Rating
• Poster
• IMDb link
• Additional metadata
• Awards

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5. Analytics and Statistics

Choose option: 5

Example statistics:

• highest-rated movie
• lowest-rated movie
• average rating
• total movie count

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6. Rating Histogram

Choose option: 9

A histogram visualization of movie ratings is generated.

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7. Generate Website

Choose option: 10

The application generates a complete movie website including:

• movie posters
• IMDb links
• hover effects
• responsive movie grid
• personalized user collections

Generated output:

exports/index.html

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8. Random Movie Recommendation

Choose option: 6

The application selects a random movie from the user's collection.

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9. Update or Delete Movies

Choose option: 4
Choose option: 3

Users can modify or remove movies from their collections.

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10. Fill Database with Blockbusters

Choose option: 11

The application automatically imports a predefined blockbuster movie collection via API requests.

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11. Exit Application

Choose option: 0

The application closes safely and all data remains persistently stored.

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Future Improvements

Possible future extensions:

• Flask or FastAPI integration
• User authentication
• Watchlists and favorites
• Movie recommendations
• Docker support
• CI/CD pipelines
• Cloud deployment
• Advanced analytics dashboard

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License

Released under the MIT License.

This project is intended for educational and learning purposes.