{"id":30284952,"url":"https://github.com/byteb8/filmflow","last_synced_at":"2026-04-14T00:02:26.362Z","repository":{"id":231465843,"uuid":"781802415","full_name":"byteB8/FilmFlow","owner":"byteB8","description":"This project is a movie recommendation system that suggests similar movies based on user input. 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The system allows users to search for movies, view movie details, get similar movie recommendations, and explore cast details.\n\n## Features\n\n- **Search Movies:** Users can search for movies by title.\n- **Autocompletion:** Auto suggest films title based on user input.\n- **View Movie Details:** Detailed information about each movie, including posters, overview, ratings, genres, release date, and runtime, is displayed.\n- **Similar Movie Recommendations:** Based on the selected movie, users can get recommendations for similar movies.\n- **Explore Cast Details:** Users can explore details about the cast of a movie, including their birthdays, biographies, and place of birth.\n\n## Architecture\n\n\n![architecture](assets/filmflow-architecture.png)\n\n\n## Technologies Used\n\n- **Frontend:** HTML, CSS, JavaScript, jQuery, Bootstrap\n- **Backend:** Python, Flask framework\n- **API:** The Movie Database (TMDb) API\n\n## Setup Instructions\n\n1. Clone the repository: `git clone https://github.com/KumaarBalbir/FilmFlow.git`\n2. Create a virtual environment using Conda: `conda create --name filmflow-venv python=3.8`\n3. Activate the virtual environment: `conda activate filmflow-venv`\n4. Install the required dependencies: `pip install -r requirements.txt`\n5. Run the Flask application: `python main.py`\n6. Open the browser and navigate to `http://localhost:5000` to access the application.\n\n## Project Structure\n\n- `main.py`: Main Flask application file containing route definitions and API integrations.\n- `static/`: Contains static files such as CSS stylesheets and JavaScript scripts (`recommend.js`: JavaScript file for frontend functionality such as **AJAX requests** and event handling and `autocomplete.js` is for **autosuggestion** while user enters title name).\n- `templates/`: Contains HTML templates for pages of the application.\n- `artifact`: `transform.pkl` contains a serialized version of the **TF-IDF vectorizer** or text transformer used for  text preprocessing and `sentiment-model.pkl` is serialized trained model for sentiment analysis, specifically a **Multinomial Naive Bayes** classifier.\n- `preprocess`: Contains python scripts for data extraction and preprocessing of the movies details used in this project.\n- `sentiment-model`: Contains script for training multinomial naive bayes model used for viewers sentiments.\n- `assets`: Some project related resource.\n- `requirements.txt`: List of Python dependencies required for the project.\n\n## Usage\n\n1. Enter the title of a movie in the search box and click on search icon.\n2. Select a movie from the search results to view its details.\n3. Explore similar movie recommendations and cast details.\n4. Enjoy exploring and discovering new movies!\n\n🙂 Feel free to contribute, provide feedback, or suggest improvements to the project!\n\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbyteb8%2Ffilmflow","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbyteb8%2Ffilmflow","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbyteb8%2Ffilmflow/lists"}