Post-recommendation-system
This project is an NLP-based intelligent post recommendation system built with Python, Streamlit, spaCy, TF-IDF, cosine similarity, and SQLite database.
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README
🧐 Intelligent Post Recommendation System
This project is an NLP-based intelligent post recommendation system built with Python, Streamlit, spaCy, TF-IDF, cosine similarity, and SQLite database.
It allows users to add posts, fetch similar posts based on a query, and view all posts in the database.
🚀 Features
- Intelligent recommendation of similar posts based on user query.
- Add new posts through a simple web UI.
- View all posts stored in a database.
- Preprocessing using spaCy (lemmatization, stopword removal).
- TF-IDF vectorization and cosine similarity for recommendations.
- SQLite database for storage.
- Easy-to-run with Streamlit app interface.
🛠️ Technologies Used
- Python 3.x
- Streamlit
- spaCy (
en_core_web_sm) - scikit-learn (TF-IDF + cosine similarity)
- SQLite (built-in DB)
- Pandas
📆 Installation
First, clone the repository or copy the code files:
bash
git clone https://github.com/nabeel03103n/Post-recommendation-system.git
cd post-recommendation-system
Install the required dependencies:
bash
pip install streamlit spacy scikit-learn pandas
python -m spacy download en_core_web_sm
📂 Project Structure
post-recommendation-system/
├── app.py # Main Streamlit app
├── seed_posts.py # (Optional) Script to populate database with 1000+ posts
├── posts.db # SQLite database file (auto-created)
└── README.md # Project documentation
⚡ Running the Application
- Start the Streamlit server:
bash
streamlit run app.py
- It will open automatically in your browser at:
http://localhost:8501
📝 Usage
- Recommend: Enter a query/post idea, and get top similar posts.
- Add Post: Add new posts manually through the UI.
- View All Posts: See all stored posts in a table view.
If you want to preload 1000+ sample posts:
bash
python seed_posts.py
Then refresh your app.
🧀 Example Queries
- "Deploy an AI chatbot using Python."
- "Learn computer vision with OpenCV."
- "Introduction to blockchain technology."
- "Master deep learning best practices."
🌟 Future Improvements (Ideas)
- Add categories/tags for better filtering.
- Enable user authentication (login/signup).
- Migrate database to PostgreSQL/MySQL for production.
- Improve UI using Streamlit Components or Tailwind + Streamlit.
🤝 Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
📄 License
This project is licensed under the MIT License.
Built with ❤️ using Python and Streamlit.