Update README.md

README.md

@@ -1,69 +1,74 @@
 # Waste Classification Model
 
-This is a Keras model for classifying waste into different categories. This model was trained to identify various types of waste materials to aid in recycling and waste management efforts.
+A Convolutional Neural Network (CNN) built with TensorFlow/Keras for automated waste classification. This model identifies and categorizes different types of waste materials to support recycling and waste management efforts.
 
 ## Model Details
 
-This model is a Convolutional Neural Network (CNN) built with TensorFlow/Keras.
+- **Architecture**: Convolutional Neural Network (CNN)
+- **Framework**: TensorFlow/Keras
+- **Input Size**: 128×128 pixels, RGB (3 channels)
+- **Categories**: 6 waste types
 
-*   **Input:** Images of waste materials. The expected input size is (128, 128) pixels with 3 color channels (RGB).
-*   **Output:** The model outputs a prediction probability for each waste category.
-*   **Categories:** The model is trained to classify waste into the following categories (assuming these are your categories - please adjust if different):
-    *   Cardboard
-    *   Glass
-    *   Metal
-    *   Paper
-    *   Plastic
-    *   Trash
+### Classification Categories
 
-## How to Use
+The model classifies waste into these categories:
+- **Cardboard**
+- **Glass** 
+- **Metal**
+- **Paper**
+- **Plastic**
+- **Trash**
 
-You can load and use this model for inference using the `tensorflow` and `huggingface_hub` libraries in Python.
-
-First, make sure you have the necessary libraries installed: 
+## Installation
 
+```bash
+pip install tensorflow huggingface-hub numpy pillow
+```
 
+## Usage
 
+```python
 from huggingface_hub import hf_hub_download
 import tensorflow as tf
 import numpy as np
 from tensorflow.keras.preprocessing import image
 
-# Replace with your model repository and filename
+# Download and load model
 repo_id = "MOHAMMED7M7/waste-classification-model"
-filename = "waste_classification_model.keras" # Make sure this is the correct filename you uploaded
-
-# Download the model file
+filename = "waste_classification_model.keras"
 model_path = hf_hub_download(repo_id=repo_id, filename=filename)
-
-# Load the model
 model = tf.keras.models.load_model(model_path)
 
-print("Model loaded successfully!")
-
-# Example of making a prediction
-# Replace 'path/to/your/image.jpg' with the actual path to your image
+# Preprocess image
 def preprocess_image(img_path):
-    img = image.load_img(img_path, target_size=(128, 128)) # Ensure target_size matches your model
+    img = image.load_img(img_path, target_size=(128, 128))
     img_array = image.img_to_array(img)
     img_array = np.expand_dims(img_array, axis=0)
-    img_array /= 255.0  # Normalize the image if your model expects it
+    img_array /= 255.0
     return img_array
 
-# Replace with the path to your image
+# Make prediction
 image_path = 'path/to/your/image.jpg'
-
-# Preprocess the image
 processed_image = preprocess_image(image_path)
-
-# Make a prediction
 predictions = model.predict(processed_image)
 
-# Get the predicted class (assuming a classification model)
+# Get result
+class_names = ['cardboard', 'glass', 'metal', 'paper', 'plastic', 'trash']
 predicted_class_index = np.argmax(predictions)
+predicted_class = class_names[predicted_class_index]
+confidence = predictions[0][predicted_class_index]
+
+print(f"Predicted class: {predicted_class}")
+print(f"Confidence: {confidence:.2%}")
+```
+
+## Requirements
+
+- Python 3.7+
+- TensorFlow 2.8+
+- NumPy
+- Pillow
 
-# You'll need a list of your class names to map the index to a label
-class_names = ['cardboard', 'glass', 'metal', 'paper', 'plastic', 'trash'] # Replace with your actual class names
-predicted_class_label = class_names[predicted_class_index]
+## License
 
-print(f"The predicted class is: {predicted_class_label}")
+MIT License