Update README.md

README.md

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-license: apache-2.0
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+# 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.
+
+## Model Details
+
+This model is a Convolutional Neural Network (CNN) built with TensorFlow/Keras.
+
+*   **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
+
+## How to Use
+
+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: 
+
+
+
+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
+repo_id = "MOHAMMED7M7/waste-classification-model"
+filename = "waste_classification_model.keras" # Make sure this is the correct filename you uploaded
+
+# Download the model file
+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
+def preprocess_image(img_path):
+    img = image.load_img(img_path, target_size=(128, 128)) # Ensure target_size matches your model
+    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
+    return img_array
+
+# Replace with the path to your image
+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)
+predicted_class_index = np.argmax(predictions)
+
+# 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]
+
+print(f"The predicted class is: {predicted_class_label}")