Update app.py

app.py

@@ -8,17 +8,10 @@ model_path = 'cnn_tumor.h5'
 model = tf.keras.models.load_model(model_path)
 
 def make_prediction(img, model):
-    img = img.resize((128, 128))  # Resize the image to 128x128
-    img = np.array(img) / 255.0    # Normalize the image
-    
-    # Check if the image has only one channel (grayscale)
-    if img.ndim == 2:  
-        img = np.stack((img,)*3, axis=-1)  # Convert grayscale to RGB
-    elif img.shape[2] == 1:  # If the image has a single channel
-        img = np.concatenate([img, img, img], axis=-1)  # Convert to RGB
-
-    input_img = np.expand_dims(img, axis=0)  # Add a batch dimension
-    res = model.predict(input_img)   # Make the prediction
+    img = img.resize((128, 128))
+    img = np.array(img) / 255.0  # Normalize the image
+    input_img = np.expand_dims(img, axis=0)
+    res = model.predict(input_img)
     return res
 
 # Function to load the CSS file and apply it