Update app.py

app.py

@@ -327,28 +327,22 @@ async def predict_tomato(file: UploadFile = File(...)):
             "message": "Please upload a clear image of a PLANT leaf."
         })
 
-        #Fix Added because Model was trained on leaf images with black background.
-        # 1. Turn the image into a matrix of numbers
-        #image_array = np.array(image)
-        
-        # 2. Find all pixels where Red, Green, and Blue are all very high (white)
-        #white_mask = (image_array[:, :, 0] > 240) & \
-                     #(image_array[:, :, 1] > 240) & \
-                     #(image_array[:, :, 2] > 240)
-        
-        # 3. Change all those specific pixels to [0, 0, 0] (Pure Black)
-        #image_array[white_mask] = [0, 0, 0]
-        
-        # 4. Turn the matrix back into an image so the rest of the code can use it
-        #image = Image.fromarray(image_array)
-        
+         image_array = np.array(image)
+        white_mask = (
+            (image_array[:, :, 0] > 240) &
+            (image_array[:, :, 1] > 240) &
+            (image_array[:, :, 2] > 240)
+        )
+        image_array[white_mask] = [0, 0, 0]
+        image = Image.fromarray(image_array)
+
         
         # 1. Standard Resize
         image = image.resize((224, 224))
         
         # 2. Custom Normalization: Converting [0, 255] to [-1.0, 1.0]
         input_data = np.array(image).astype(np.float32)
-        input_data = (input_data / 127.5) - 1.0
+        input_data = efficientnet_preprocess(input_data)
         input_data = np.expand_dims(input_data, axis=0)
 
         # --- RUN INFERENCE ---