Update app.py

app.py

@@ -5,11 +5,16 @@ from model import create_effnetb2_model
 from timeit import default_timer as timer
 from typing import Tuple, Dict
 import pkg_resources
+import logging
+
+# Set up logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
 
 # Check Gradio version
 try:
     gradio_version = pkg_resources.get_distribution("gradio").version
-    print(f"Using Gradio version: {gradio_version}")
+    logger.info(f"Using Gradio version: {gradio_version}")
 except pkg_resources.DistributionNotFound:
     raise ImportError("Gradio is not installed. Please install it using 'pip install gradio'.")
 
@@ -17,13 +22,17 @@ except pkg_resources.DistributionNotFound:
 try:
     with open("class_names.txt", "r") as f:
         class_names = [food_name.strip() for food_name in f.readlines()]
+    logger.info("Class names loaded successfully")
 except FileNotFoundError:
+    logger.error("class_names.txt not found")
     raise FileNotFoundError("class_names.txt not found.")
 
 # Model and transforms preparation
 try:
     effnetb2, effnetb2_transforms = create_effnetb2_model(num_classes=101)
+    logger.info("EfficientNetB2 model created successfully")
 except Exception as e:
+    logger.error(f"Error creating model: {str(e)}")
     raise Exception(f"Error creating model: {str(e)}")
 
 # Load weights
@@ -34,9 +43,12 @@ try:
             map_location=torch.device("cpu"),
         )
     )
+    logger.info("Model weights loaded successfully")
 except FileNotFoundError:
+    logger.error("Model weights file not found")
     raise FileNotFoundError("Model weights file not found.")
 except Exception as e:
+    logger.error(f"Error loading weights: {str(e)}")
     raise Exception(f"Error loading weights: {str(e)}")
 
 # Predict function
@@ -51,8 +63,10 @@ def predict(img) -> Tuple[Dict, float]:
             pred_probs = torch.softmax(effnetb2(img), dim=1)
         pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
         pred_time = round(timer() - start_time, 5)
+        logger.info(f"Prediction completed: {pred_labels_and_probs}, Time: {pred_time}")
         return pred_labels_and_probs, pred_time
     except Exception as e:
+        logger.error(f"Prediction failed: {str(e)}")
         return {"error": f"Prediction failed: {str(e)}"}, 0.0
 
 # Gradio app
@@ -61,9 +75,10 @@ description = "An EfficientNetB2 feature extractor to classify 101 food classes.
 
 try:
     example_list = [["examples/" + example] for example in os.listdir("examples")]
+    logger.info("Examples loaded successfully")
 except FileNotFoundError:
     example_list = []
-    print("Warning: 'examples/' directory not found.")
+    logger.warning("'examples/' directory not found")
 
 # Simplified Gradio interface
 demo = gr.Interface(
@@ -76,10 +91,14 @@ demo = gr.Interface(
     examples=example_list,
     title=title,
     description=description,
+    allow_flagging="never",  # Disable flagging to simplify API
+    api_mode=False,          # Disable API mode to avoid schema generation
 )
 
 # Launch with share=True for Hugging Face Spaces
 try:
     demo.launch(share=True)
+    logger.info("Gradio app launched successfully")
 except Exception as e:
+    logger.error(f"Failed to launch Gradio app: {str(e)}")
     raise Exception(f"Failed to launch Gradio app: {str(e)}")