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| import gradio as gr | |
| import tensorflow as tf | |
| import numpy as np | |
| from PIL import Image | |
| # Load model | |
| model = tf.keras.models.load_model("model.h5") | |
| # Correct class names in training order | |
| class_names = ['batteries', 'clothes', 'e-waste', 'glass', 'light blubs', 'metal', 'organic', 'paper', 'plastic'] | |
| # Match training input size | |
| IMG_SIZE = (224, 224) | |
| def preprocess(image): | |
| image = image.resize(IMG_SIZE) | |
| image_array = tf.keras.preprocessing.image.img_to_array(image) | |
| image_array = image_array / 255.0 # Normalize if this was done in training | |
| return np.expand_dims(image_array, axis=0) | |
| def predict(image): | |
| input_tensor = preprocess(image) | |
| predictions = model.predict(input_tensor)[0] | |
| top_class = class_names[np.argmax(predictions)] | |
| confidence = np.max(predictions) * 100 | |
| return f"{top_class} ({confidence:.2f}%)" | |
| demo = gr.Interface( | |
| fn=predict, | |
| inputs=gr.Image(type="pil", label="Upload an image of a recyclable item to classify it."), | |
| outputs=gr.Textbox(label="Output"), | |
| title="Waste Image Classifier" | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |