README.md

🚗 Car Info Finder & Version Comparator

This Streamlit-based project allows users to search for car information using the Wikipedia API and compare different versions or models of cars if disambiguation is detected. It includes two modules: a main search and comparison interface (SearchPage.py) and a dedicated comparison view (campare.py).

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📦 Features

🔍 Car Info Finder (SearchPage.py)

• Search cars by name using the Wikipedia API.
• Automatically filters out non-automobile-related results.
• Supports three categories: Vintage, Classic, and Trending.
• Maintains a search history for quick access.
• Includes a Compare Mode for viewing multiple car models side-by-side.
• Handles Wikipedia disambiguation by sorting versions using extracted years.
• Displays car summaries, images, and links to full Wikipedia articles.

📊 Version Comparator (campare.py)

• Displays multiple car models if the selected name has a disambiguation page.
• Allows users to select and compare specific versions in parallel.
• Uses session state to persist disambiguation options across navigation.
• Shows detailed Wikipedia summaries with images and links.

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🚀 How to Run

1. Clone this repository or copy both files to your local directory.
2. Make sure you have Python and the required libraries:

   pip install streamlit wikipedia
   

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# Link

Link: https://huggingface.co/spaces/tsairohith/car_info_finder

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🧠 How It Works

1. Uses the wikipedia Python package to fetch summaries and images.

2. Filters non-car-related content using automotive keywords.

3. Handles ambiguous results (e.g., "Ford Mustang" with many models) via a secondary comparison tool.

4. Session state ensures seamless switching between search and compare modes.

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🛠️ To-Do (Optional Enhancements)

1. Add sentiment analysis or specs comparison.

2. Cache Wikipedia API responses for performance.

3. Expand to other vehicle types (bikes, trucks, etc.).

4. Include user ratings or historical sales data.

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🧑‍💻 Author

Built with ❤️ using Streamlit and Wikipedia by [Team 43].

Team Members

1. Rajesh Parikapalli
2. T.Sai Rohith
3. K.Sharath Chandra
4. Nihal Shariff