Update app.py

app.py

@@ -5,9 +5,11 @@ import numpy as np
 import cv2
 
 # Load the trained model
-model_path = './model_checkpoint_manual_effnet.h5'
+model_path = 'C:/Users/kamel/Documents/Image Classification/model_checkpoint_manual_effnet.h5'
 model = load_model(model_path)
 
+class_names = ['ADONIS', 'AFRICAN GIANT SWALLOWTAIL', 'AMERICAN SNOOT', 'AN 88', 'APPOLLO', 'ARCIGERA FLOWER MOTH', 'ATALA', 'ATLAS MOTH', 'BANDED ORANGE HELICONIAN', 'BANDED PEACOCK']
+
 # Define a function to preprocess the input image
 def preprocess_image(img):
     # Check if img is a file path or an image object
@@ -38,7 +40,10 @@ def classify_image(img):
     # Get the predicted class label
     predicted_class = np.argmax(predictions)
     
-    return f"Predicted Class: {predicted_class}"
+    # Get the predicted class name
+    predicted_class_name = class_names[predicted_class]
+    
+    return f"Predicted Class: {predicted_class_name}"
 
 # Create a Gradio interface
 iface = gr.Interface(fn=classify_image, 
@@ -47,11 +52,4 @@ iface = gr.Interface(fn=classify_image,
                      live=True)
 
 # Launch the Gradio app
-iface.launch()
-
-
-# In[ ]:
-
-
-
-
+iface.launch()