Update utils.py

utils.py

@@ -34,41 +34,12 @@ base_model = tf.keras.applications.ResNet50V2(include_top=False, input_shape=inp
 #Making the layers of the model trainable
 base_model.trainable = True
 
-def preprocess(image_or_path):
-    if isinstance(image_or_path, str):  # Check if the input is a string (image path)
-        img = Image.open(image_or_path)
-    elif isinstance(image_or_path, np.ndarray):  # Check if the input is a numpy array
-        # Convert the numpy array to a PIL Image
-        img = Image.fromarray((image_or_path * 255).astype(np.uint8))
-    else:
-        raise ValueError("Unsupported input type. Expected string path or numpy array.")
-    
-    # Print image dimensions for debugging
-    print(f"Original image dimensions: {img.size}")
-
-    # Check image mode
-    if img.mode != "RGB":
-        img = img.convert("RGB")
-        print("Converted image mode to RGB.")
-
-    try:
-        # Resize the image
-        resized_img = img.resize((256, 256), Image.LANCZOS)
-
-        # Convert the image to a numpy array
-        img_array = np.array(resized_img)
-
-        # Normalize if necessary
-        img_array = img_array / 255.0
-
-        # Add a batch dimension
-        img_array = np.expand_dims(img_array, axis=0)
-
-        return img_array
-
-    except Exception as e:
-        print(f"Error encountered: {e}")
-        return None
+def preprocess(img_path):
+    img = Image.open(img_path)
+    img = img.resize((256, 256))
+    img_array = np.array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    return img_array
 
 def model_arc():
     model = tf.keras.Sequential([