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from PIL import Image |
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import numpy as np |
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import gradio as gr |
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from tensorflow.keras.models import load_model |
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model = load_model('mio_modello.h5') |
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def preprocess_image(image): |
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image = Image.fromarray(image) |
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image = image.convert("RGB") |
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image = image.resize((64, 64)) |
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image_array = np.array(image) / 255.0 |
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image_array = np.expand_dims(image_array, axis=0) |
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return image_array |
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def classify_image(image): |
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image_array = preprocess_image(image) |
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prediction = model.predict(image_array)[0] |
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print(f"Raw model predictions: {prediction}") |
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predicted_class_idx = np.argmax(prediction) |
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class_labels = ['Chihuahua', 'Muffin'] |
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print(f"Predicted class: {class_labels[predicted_class_idx]} with confidence {prediction[predicted_class_idx]}") |
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confidence_scores = {class_labels[i]: float(prediction[i]) for i in range(len(class_labels))} |
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return confidence_scores |
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examples = ['chihuahua1.jpg', 'chihuahua2.jpg', 'chihuahua3.jpg', 'muffin1.jpg', 'muffin2.jpg'] |
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gr.Interface( |
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fn=classify_image, |
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inputs=gr.Image(type="numpy"), |
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outputs=gr.Label(num_top_classes=2), |
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examples=examples, |
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title="Chihuahua vs Muffin Classifier", |
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description="don't know if you should cuddle it or eat it? find out uploading a picture. PS: no muffin and no chihuahua got hurt in this project" |
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).launch() |
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