Update app.py

app.py

@@ -15,19 +15,26 @@ labels = {
     'amphibia': ['American Bullfrog', 'American Toad', 'Green Frog', 'Northern Leopard Frog']
 }
 
-
-def predict(image):
+def preprocess_image(img_path):
+    img = image.resize((224, 224))  # MobileNet requires 224x224 input size
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+    img_array = preprocess_input(img_array)  # Preprocess the image
     
-    img = image.resize((224, 224))
-    img_array = tf.keras.utils.img_to_array(img)
-    img_array = tf.expand_dims(img_array, 0) # Create a batch
+    return img_array
+
+# Function to perform inference
+def predict(model, img_path):
+    img = preprocess_image(img_path)
     model = load_model(r"inceptionv3_classs.h5")
-    predictions = model.predict(img_array)
-    score = np.argmax(predictions)
-    class_label = labels['class'][score]
-    print(score, class_label )
+    preds = model.predict(img)  # Get the model predictions
+    decoded_preds = np.argmax(preds) 
+    print(decoded_preds, labels['class'][decoded_preds])
+    return decoded_preds
+    
+
     
-    return class_label
+   
     # results = {}
     # image_array = load_and_preprocess_image(image)
     # print(image_array)