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| import gradio as gr | |
| import tensorflow as tf | |
| import tensorflow_hub as hub | |
| import numpy as np | |
| # Load your trained model | |
| model = tf.keras.models.load_model("model.keras", custom_objects={"KerasLayer": hub.KerasLayer}) | |
| # Define your waste class labels | |
| class_names = ['batteries', 'clothes', 'e-waste', 'glass', 'light blubs', | |
| 'metal', 'organic', 'paper', 'plastic'] | |
| IMG_SIZE = 224 | |
| # Image preprocessing function | |
| def preprocess_image(image): | |
| image = tf.convert_to_tensor(image, dtype=tf.float32) | |
| image = tf.image.resize(image, (IMG_SIZE, IMG_SIZE)) / 255.0 | |
| return tf.expand_dims(image, axis=0) | |
| # Prediction function | |
| def predict_waste(image): | |
| processed = preprocess_image(image) | |
| preds = model.predict(processed) | |
| label = class_names[np.argmax(preds)] | |
| confidence = float(np.max(preds)) | |
| return f"{label} ({confidence:.2f})" | |
| # Launch Gradio Interface | |
| import gradio as gr | |
| def predict_waste(image): | |
| # Your prediction logic here | |
| return "Example category: Plastic" | |
| examples = [ | |
| ["examples/download (1).jpg"], | |
| ["examples/download (13) (1).jpg"], | |
| ["examples/download (1) (1).jpg"], | |
| ["examples/download (14).jpg"] | |
| ] | |
| with gr.Blocks(title="Waste Classification Model 🌱") as demo: | |
| gr.Markdown(""" | |
| # ♻️ Waste Classification with AI | |
| Upload or drag an image of a waste item below. Our model will classify it into one of 9 categories such as plastic, glass, paper, or metal. | |
| - ✅ Powered by MobileNetV2 | |
| - 🌍 Helping smart cities manage waste efficiently | |
| - 📷 Works with real-world waste images | |
| """) | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| image_input = gr.Image(type="numpy", label="📤 Upload a Waste Image") | |
| submit_btn = gr.Button("🔍 Classify Waste") | |
| with gr.Column(scale=1): | |
| prediction_output = gr.Textbox(label="🧠 Predicted Category", interactive=False) | |
| submit_btn.click(fn=predict_waste, inputs=image_input, outputs=prediction_output) | |
| gr.Examples( | |
| examples=examples, | |
| inputs=image_input, | |
| label="🖼 Example Waste Images", | |
| examples_per_page=4 | |
| ) | |
| gr.Markdown(""" | |
| --- | |
| 🔬 *Model accuracy may vary based on image clarity and background noise. For best results, upload clear, well-lit images.* | |
| """) | |
| demo.launch() | |