Update app.py

app.py

@@ -13,18 +13,21 @@ classes = ['Colon Adenocarcinoma', 'Colon Benign Tissue', 'Lung Adenocarcinoma',
 
 # Function to preprocess the uploaded image and make predictions
 def predict(img):
-    img = img.resize((224, 224))
-    img_array = tf.keras.preprocessing.image.img_to_array(img)
-    img_array = tf.expand_dims(img_array, 0)
+    try:
+        img = img.resize((224, 224))
+        img_array = tf.keras.preprocessing.image.img_to_array(img)
+        img_array = tf.expand_dims(img_array, 0)
+        
+        predictions = model.predict(img_array)
+        class_labels = classes
+        # predictions = model.predict(img_array)
+        # predicted_class = classes[np.argmax(predictions[0])]
+        score = tf.nn.softmax(predictions[0])
+        # print(f"{class_labels[tf.argmax(score)]}") 
     
-    predictions = model.predict(img_array)
-    class_labels = classes
-    # predictions = model.predict(img_array)
-    # predicted_class = classes[np.argmax(predictions[0])]
-    score = tf.nn.softmax(predictions[0])
-    print(f"{class_labels[tf.argmax(score)]}") 
-    
-    return f"{class_labels[tf.argmax(score)]}
+        return f"Prediction: {class_labels[tf.argmax(score)]}"
+    except Exception as e:
+        return str(e)
 
 # Create a Gradio interface
 iface = gr.Interface(