Update app.py

app.py

@@ -1,53 +1,57 @@
 import gradio as gr
 import tensorflow as tf
-import numpy as np
-from tensorflow.keras.applications.resnet152 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"  # Path to your model file
+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 preprocess_image(image):
-    """
-    Preprocesses the uploaded image for prediction.
-    """
-    image = image.resize((224, 224))  # Resize to match the model input size (224x224 for ResNet152)
-    image = img_to_array(image)  # Convert PIL image to NumPy array
-    image = preprocess_input(image)  # Normalize for ResNet152
-    image = np.expand_dims(image, axis=0)  # Add batch dimension
+def decode_image_from_base64(base64_str):
+    # Decode the base64 string to bytes
+    image_data = base64.b64decode(base64_str)
+    # Convert the bytes into a PIL image
+    image = Image.open(BytesIO(image_data))
     return image
-
-def predict(image):
+    
+def predict_image(image):
     """
-    Accepts an image, preprocesses it, and returns the top 3 predictions.
+    Process the uploaded image and return the top 3 predictions.
     """
-    processed_image = preprocess_image(image)
-    predictions = model.predict(processed_image)
-    decoded_predictions = decode_predictions(predictions, top=3)[0]
+    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]
+
+        # Format predictions as a list of tuples (label, confidence)
+        results = [(label, float(confidence)) for _, label, confidence in decoded_predictions]
+        return dict(results)
 
-    # Return predictions as a list of dictionaries
-    results = [{"label": label, "confidence": float(confidence)} for _, label, confidence in decoded_predictions]
-    return {"predictions": results}
+    except Exception as e:
+        return {"Error": str(e)}
 
-# Create a Gradio interface
+# Create the Gradio interface
 interface = gr.Interface(
-    fn=predict,  # The prediction function
-    inputs=gr.Image(type="pil", label="Upload an Image"),  # Input: Image
-    outputs=gr.JSON(label="Predictions"),  # Output: JSON (Top 3 Predictions)
+    fn=predict_image,
+    inputs=gr.Image(type="pil"),  # Accepts an image input
+    outputs=gr.Label(num_top_classes=3),  # Shows top 3 predictions with confidence
     title="ResNet152 Image Classifier",
     description="Upload an image, and the model will predict what's in the image.",
-    examples=[
-        ["example_images/example1.jpg"],  # Add paths to example images
-        ["example_images/example2.jpg"]
-    ],
+    examples=["dog.jpg", "cat.jpg"],  # Example images for users to test
 )
 
-# Launch the app
+# Launch the Gradio app
 if __name__ == "__main__":
-    interface.launch(share=True)
+    interface.launch()