feat: add confidence score feature to model predictions

.gitattributes

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README.md

@@ -1,76 +1,25 @@
-# Waste Classification Application
-
-This project is a web-based application that classifies waste materials from user-uploaded images. It identifies the type of waste (e.g., cardboard, glass, metal) and provides information on how to properly dispose of it.
-
-## 🚀 Live Demo
-
-Try the application live on Hugging Face Spaces\!
-
-**➡️ [Waste Classification Demo](https://huggingface.co/spaces/HMWCS/Gemma3n-challenge-demo)**
-
------
-
-## ✨ Features
-
-  * **Image-based classification:** Upload an image of a waste item to have it automatically classified.
-  * **Multiple waste categories:** The application can identify a variety of waste materials.
-  * **Disposal information:** After classification, the app provides guidance on how to dispose of the identified waste material.
-  * **Web interface:** A user-friendly web interface built with Gradio makes the application easy to use.
-
------
-
-## 💡 How it works
-
-The application uses a pre-trained Gemma3n (E2B) model to perform the image classification. The model has been fine-tuned on a dataset of waste images to accurately identify different materials. The disposal information is retrieved from a knowledge base within the application.
-
------
-
-## 📓 Kaggle Notebook
-
-Explore the model fine-tuning process and the underlying code in our detailed Kaggle Notebook.
-
-**➡️ [Gemma3n Challenge Notebook](https://www.kaggle.com/code/yichuanhuang/gemma3n-challenge)**
-
------
-
-## 🛠️ Getting Started
-
-### Prerequisites
-
-  * Python 3.9+
-  * Pip
-  * Cuda (optional)
-
-### Installation
-
-1.  Clone the repository:
-    ```bash
-    git clone https://github.com/yichuan-huang/gemma3n-challenge
-    ```
-2.  Navigate to the project directory:
-    ```bash
-    cd gemma3n-challenge
-    ```
-3.  Install the required dependencies:
-    ```bash
-    pip install -r requirements.txt
-    ```
-
-### Running the application
-
-To start the application, run the following command:
-
-```bash
-python app.py
-```
-
-This will launch a Gradio web server. You can access the application by opening the provided URL in your web browser.
-
-## 📂 Project Structure
-
-  * `app.py`: The main application file, containing the Gradio interface and the classification logic.
-  * `classifier.py`: Handles the image classification using the pre-trained model.
-  * `config.py`: Contains configuration settings for the application, such as the model name and labels.
-  * `knowledge_base.py`: A simple knowledge base containing disposal information for different waste materials.
-  * `requirements.txt`: A list of the Python dependencies required to run the application.
-  * `test_images/`: A directory containing sample images for testing the application.
+---
+title: Gemma3n Challenge
+emoji: 🌍
+colorFrom: green
+colorTo: green
+sdk: gradio
+sdk_version: 5.37.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+# Garbage Classification System
+
+A Gradio app for classifying garbage into 4 categories using Gemma-3n model.
+
+## Usage
+1. Upload an image.
+2. Click "Classify Garbage" or wait for auto-classification.
+
+## Categories
+- Recyclable Waste
+- Food/Kitchen Waste
+- Hazardous Waste
+- Other Waste

app.py

@@ -29,7 +29,7 @@ def classify_garbage_impl(image):
     Actual classification implementation
     """
     if image is None:
-        return "Please upload an image", "No image provided", "N/A"
+        return "Please upload an image", "No image provided"
 
     try:
         classification, full_response, confidence_score = classifier.classify_image(image)
@@ -117,4 +117,4 @@ with gr.Blocks(title="Garbage Classification System") as demo:
     )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()

requirements.txt

@@ -5,4 +5,4 @@ torchvision
 transformers >= 4.53
 accelerate
 timm
-gradio
+gradio