Update app.py

app.py

@@ -1,31 +1,39 @@
 import gradio as gr
 import tensorflow as tf
-from tensorflow.keras.applications import ResNet152, preprocess_input, decode_predictions
+from tensorflow.keras.applications.resnet import ResNet152, preprocess_input, decode_predictions
 from tensorflow.keras.preprocessing.image import img_to_array
 from PIL import Image
 import numpy as np
 
 # Load the pre-trained ResNet152 model
-MODEL_PATH = "resnet152-image-classifier.h5"  # Directory where the model is saved
-model = tf.keras.models.load_model(MODEL_PATH)
+MODEL_PATH = "resnet152-image-classifier.h5"  # Path to the saved model
+try:
+    model = tf.keras.models.load_model(MODEL_PATH)
+except Exception as e:
+    print(f"Error loading model: {e}")
+    exit()
 
 def predict_image(image):
     """
-    This function processes the uploaded image and returns the top 3 predictions.
+    Process the uploaded image and return the top 3 predictions.
     """
-    # Preprocess the image
-    image = image.resize((224, 224))  # ResNet152 expects 224x224 input
-    image_array = img_to_array(image)
-    image_array = preprocess_input(image_array)  # Normalize the image
-    image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension
+    try:
+        # Preprocess the image
+        image = image.resize((224, 224))  # ResNet152 expects 224x224 input
+        image_array = img_to_array(image)
+        image_array = preprocess_input(image_array)  # Normalize the image
+        image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension
 
-    # Get predictions
-    predictions = model.predict(image_array)
-    decoded_predictions = decode_predictions(predictions, top=3)[0]
+        # Get predictions
+        predictions = model.predict(image_array)
+        decoded_predictions = decode_predictions(predictions, top=3)[0]
 
-    # Format predictions as a dictionary
-    results = {label: f"{confidence * 100:.2f}%" for _, label, confidence in decoded_predictions}
-    return results
+        # Format predictions as a dictionary
+        results = {label: f"{confidence * 100:.2f}%" for _, label, confidence in decoded_predictions}
+        return results
+
+    except Exception as e:
+        return {"Error": str(e)}
 
 # Create the Gradio interface
 interface = gr.Interface(