WasteVision - AI-Based Waste Management System ♻️

Overview 📜

WasteVision is a deep learning-based image classification web app that helps users identify whether waste items are Organic or Recyclable. It leverages a Convolutional Neural Network (CNN) trained on a custom waste dataset and is built with Streamlit for quick deployment and ease of use.

Whether you're managing waste in smart cities, educational campaigns, or personal sustainability efforts, WasteVision offers an intuitive and interactive tool to support effective waste segregation.

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Features 🚀

• Image Upload: Upload a waste item image directly via the interface. 🖼️
• Waste Type Prediction: Instantly identify waste as Recyclable or Organic using a trained CNN model. 🧠
• Streamlit Interface: Clean, minimal, and user-friendly UI built using Streamlit. 🖥️
• Live Web App: Accessible without any local installation. 🌐
• Efficient & Lightweight: Fast predictions using optimized Keras models. ⚡

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Live Demo 🌍

Try the app now: WasteVision Streamlit App

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Installation 🛠️

To run WasteVision locally, follow these steps:

1. Clone the Repository:

git clone https://github.com/Prathyusha-215/waste_vision.git
cd waste_vision

2. Create a Virtual Environment (Optional but Recommended):

python -m venv venv
source venv/bin/activate  # On Windows, use venv\Scripts\activate

3. Install Required Libraries:

pip install -r requirements.txt

4. Run the App:

streamlit run waste.py

5. Open in Browser:

Visit http://localhost:8501 to use the application locally.

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Usage 🎮

1. Launch the App locally or open the Live Demo.
2. Upload an Image of waste (e.g., bottle, banana peel, etc.).
3. Click on "Predict Waste Type" to see the model’s prediction.
4. View Output: The app displays the uploaded image, predicted category, and corresponding emoji.

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Technical Stack 💻

• Web App Framework: Streamlit
• Deep Learning: TensorFlow / Keras
• Image Processing: Pillow
• Visualization: Streamlit Widgets & Matplotlib
• Dataset: Custom dataset for Organic & Recyclable waste

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Project Structure 🗂️

waste_vision/
├── waste.py                # Main Streamlit app script
├── waste_model.h5          # Pre-trained CNN model
├── waste1.jpg              # Sample image
├── streamlit_app.PNG       # App UI screenshot
├── requirements.txt        # Required Python packages
└── README.md               # Project documentation

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Contributing 🤝

We welcome contributions! To contribute:

1. Fork the repository 🍴
2. Create a new branch 🌿
3. Make your changes 💻
4. Submit a pull request 🔄

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License 📜

This project is licensed under the MIT License. See the LICENSE file for more details.

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Acknowledgments 🙏

• TensorFlow/Keras for the model training framework
• Streamlit for simplifying app deployment
• Pillow for handling image uploads

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Contact 📧

Developer: Prathyusha Vanama

• 📬 Email: prathyushavanama215@gmail.com
• 🐙 GitHub: Prathyusha-215

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♻️ WasteVision – Smart Segregation Starts with You! 🌱