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Update app.py
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app.py
CHANGED
@@ -16,15 +16,11 @@ def predict_input_image(img):
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crop_start_x = (w - 224) // 2
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crop_start_y = (h - 224) // 2
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img = img.crop((crop_start_x, crop_start_y, crop_start_x + 224, crop_start_y + 224))
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img =
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img = np.
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# Convert the image to a format suitable for model prediction
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#img_array = np.array(img) / 255.0 # Normalize pixel values to [0, 1]
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img_array = np.expand_dims(img, axis=0)
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# Make predictions using your model
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predictions = my_model.predict(
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# Convert predictions to binary (0 or 1) based on the threshold
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binary_prediction = 'Tumor Detected' if predictions[0][0] > threshold else 'No Tumor Detected'
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crop_start_x = (w - 224) // 2
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crop_start_y = (h - 224) // 2
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img = img.crop((crop_start_x, crop_start_y, crop_start_x + 224, crop_start_y + 224))
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img = tf.image.resize(img, [224,224])
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img = np.expand_dims(img, axis = 0)
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# Make predictions using your model
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predictions = my_model.predict(img)
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# Convert predictions to binary (0 or 1) based on the threshold
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binary_prediction = 'Tumor Detected' if predictions[0][0] > threshold else 'No Tumor Detected'
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