Update app.py

app.py

@@ -13,7 +13,7 @@ mineral_classification_model = tf.keras.models.load_model(mineral_classification
 # Load the mineral detection model
 mineral_detection_model = tf.keras.models.load_model("mineral_detection_model_Final_4_18_2024.h5")
 
-# Define the class labels
+#  the class labels
 class_labels = ['biotite', 'granite', 'olivine', 'plagioclase', 'staurolite']
 mineral_facts = {
     'biotite': "Hardness: 2.5-3\nMagnetism: None\nDensity: 2.7-3.3 g/cm³\nColors: Black, brown, green\nDescription: A phyllosilicate mineral of the mica group with a distinctive platy structure.",
@@ -23,7 +23,7 @@ mineral_facts = {
     'staurolite': "Hardness: 7-7.5\nMagnetism: None\nDensity: 3.6-3.8 g/cm³\nColors: Brown, reddish-brown, black\nDescription: A nesosilicate mineral with a distinctive cruciform twinning habit, commonly found in metamorphic rocks."
 }
 
-# Function to preprocess the image for mineral detection
+# function to preprocess the image for mineral detection
 def preprocess_image_detection(img_array):
     if img_array is None:
         return None
@@ -33,16 +33,17 @@ def preprocess_image_detection(img_array):
     img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
     return img_array
 
-# Function to preprocess the image for classification
+# function to preprocess the image for classification
 def preprocess_image_classification(img_array):
     if img_array is None:
         return None
     img = (img_array * 255).astype(np.uint8)  # Convert back to uint8
     img_array = cv2.resize(img, (224, 224))  # Resize to 224x224
-   # img_array is expected to be uint8 and with batch dimension
+    img_array = img_array.astype(np.uint8)
+    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
     return img_array
 
-# Define the function to detect if the input is a mineral
+#   function to detect if the input is a mineral
 def detect_mineral(image):
     if image is not None:
         image = Image.fromarray(np.array(image).astype(np.uint8), 'RGB')
@@ -57,7 +58,7 @@ def detect_mineral(image):
     else:
         return "No image provided."
 
-# Define the function to make predictions for mineral classification
+#  function to make predictions for mineral classification
 def classify_image(image):
     # Check if the input is a mineral
     is_mineral = detect_mineral(image)