Update app.py

app.py

@@ -2,31 +2,38 @@ import gradio as gr
 import tensorflow as tf
 from tensorflow.keras.applications import EfficientNetV2L
 from tensorflow.keras.applications.efficientnet_v2 import preprocess_input, decode_predictions
-from tensorflow.keras.preprocessing.image import img_to_array
-from PIL import Image
 import numpy as np
+from PIL import Image
+
+# Lazy loading to optimize memory usage
+model = None
+
+def load_model():
+    """Load the EfficientNetV2L model only when needed."""
+    global model
+    if model is None:
+        model = EfficientNetV2L(weights="imagenet")
 
-# Load the stronger pre-trained model (EfficientNetV2L)
-model = EfficientNetV2L(weights="imagenet")
+def preprocess_image(image):
+    """Preprocess the image for EfficientNetV2L model inference."""
+    image = image.resize((480, 480))  # Resize for EfficientNetV2L
+    image_array = np.array(image)  # Convert to NumPy array
+    image_array = preprocess_input(image_array)  # Normalize input
+    image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension
+    return image_array
 
 def predict_image(image):
     """
-    Process the uploaded image and return the top 3 predictions as a dictionary.
+    Process the uploaded image and return the top 3 predictions.
     """
     try:
-        # Preprocess the image
-        image = image.resize((480, 480))  # EfficientNetV2L expects 480x480 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)
+        load_model()  # Ensure the model is loaded
+        image_array = preprocess_image(image)  # Preprocess image
+        predictions = model.predict(image_array)  # Get predictions
         decoded_predictions = decode_predictions(predictions, top=3)[0]
 
         # Format predictions as a dictionary (label -> confidence)
-        results = {label: float(confidence) for _, label, confidence in decoded_predictions}
-        return results
+        return {label: float(confidence) for _, label, confidence in decoded_predictions}
 
     except Exception as e:
         return {"Error": str(e)}
@@ -35,9 +42,10 @@ def predict_image(image):
 interface = gr.Interface(
     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
+    outputs=gr.Label(num_top_classes=3),  # Shows top 3 predictions
     title="EfficientNetV2L Image Classifier",
-    description="Upload an image, and the model will predict what's in the image with higher accuracy."
+    description="Upload an image, and the model will predict its content with high accuracy.",
+    allow_flagging="never"  # Disable flagging to avoid unnecessary logs
 )
 
 # Launch the Gradio app