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README.md
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-----
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## π Kaggle Notebook
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Explore the model fine-tuning process and the underlying code in our detailed Kaggle Notebook.
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**β‘οΈ [Gemma3n Challenge Notebook](https://www.kaggle.com/code/yichuanhuang/gemma3n-garbage-classification)**
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-----
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## π οΈ Getting Started
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### Prerequisites
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* Python 3.9+
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* Pip
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* Cuda (optional)
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### Installation
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1. Clone the repository:
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```bash
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git clone https://github.com/yichuan-huang/gemma3n-challenge
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```
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2. Navigate to the project directory:
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```bash
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cd gemma3n-challenge
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```
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3. Install the required dependencies:
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```bash
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pip install -r requirements.txt
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```
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### Running the application
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To start the application, run the following command:
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```bash
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python app.py
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```
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This will launch a Gradio web server. You can access the application by opening the provided URL in your web browser.
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## π Project Structure
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* `app.py`: The main application file, containing the Gradio interface and the classification logic.
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* `classifier.py`: Handles the image classification using the pre-trained model.
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* `config.py`: Contains configuration settings for the application, such as the model name and labels.
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* `knowledge_base.py`: A simple knowledge base containing disposal information for different waste materials.
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* `requirements.txt`: A list of the Python dependencies required to run the application.
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* `test_images/`: A directory containing sample images for testing the application.
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---
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title: Gemma3n Challenge
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emoji: π
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colorFrom: green
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colorTo: green
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sdk: gradio
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sdk_version: 5.37.0
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app_file: app.py
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pinned: false
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license: mit
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---
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# Garbage Classification System
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A Gradio app for classifying garbage into 4 categories using Gemma-3n model.
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## Usage
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1. Upload an image.
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2. Click "Classify Garbage" or wait for auto-classification.
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## Categories
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- Recyclable Waste
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- Food/Kitchen Waste
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- Hazardous Waste
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- Other Waste
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