Update app.py

app.py

@@ -16,15 +16,11 @@ def predict_input_image(img):
     crop_start_x = (w - 224) // 2
     crop_start_y = (h - 224) // 2
     img = img.crop((crop_start_x, crop_start_y, crop_start_x + 224, crop_start_y + 224))
-    img = img.resize(224, 224)
-    img = np.array(img).reshape(1, 224, 224, 3)
-
-    # Convert the image to a format suitable for model prediction
-    #img_array = np.array(img) / 255.0  # Normalize pixel values to [0, 1]
-    img_array = np.expand_dims(img, axis=0)
+    img = tf.image.resize(img, [224,224])
+    img = np.expand_dims(img, axis = 0)
 
     # Make predictions using your model
-    predictions = my_model.predict(img_array)
+    predictions = my_model.predict(img)
 
     # Convert predictions to binary (0 or 1) based on the threshold
     binary_prediction = 'Tumor Detected' if predictions[0][0] > threshold else 'No Tumor Detected'