Update README.md

README.md

@@ -1,45 +1,21 @@
-# Waste Classification Model
-
-This is a deep learning model for classifying waste images into two categories: Dry Waste and Wet Waste. The model is built using PyTorch and can be used for automated waste sorting systems.
-
-## Model Details
-
-- **Model Type**: Convolutional Neural Network (CNN)
-- **Input**: RGB images
-- **Output**: Binary classification (Dry Waste / Wet Waste)
-- **Framework**: PyTorch
-
-## Usage
-
-```python
-import torch
-from model import WasteClassifier
-
-# Load the model
-model = WasteClassifier()
-model.load_state_dict(torch.load('waste_classifier.pth'))
-model.eval()
-
-# Make predictions
-def predict(image):
-    with torch.no_grad():
-        output = model(image)
-        prediction = torch.argmax(output, dim=1)
-        return "Dry Waste" if prediction.item() == 0 else "Wet Waste"
-```
-
-## Requirements
-
-The model requires the following dependencies:
-- PyTorch
-- torchvision
-- PIL
-- numpy
-
-## Training
-
-The model was trained on a custom dataset of waste images. The training notebook (`training.ipynb`) contains the complete training pipeline and data preprocessing steps.
-
-## License
-
-This model is released under the MIT License. 
+---
+title: Waste Classification AI
+emoji: ♻️
+colorFrom: green
+colorTo: blue
+sdk: docker
+python_version: '3.10'
+app_file: app.py
+app_port: 7860
+short_description: Classify waste images as dry or wet using a PyTorch model.
+tags:
+- image-classification
+- waste-management
+- pytorch
+- computer-vision
+- docker
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference