Update app.py

app.py

@@ -11,8 +11,8 @@ from io import BytesIO
 import numpy as np
 from scipy.spatial.distance import mahalanobis
 
-# Setup logging
-logging.basicConfig(level=logging.INFO)
+# Setup logging (reduced to WARNING level)
+logging.basicConfig(level=logging.WARNING)
 logger = logging.getLogger(__name__)
 
 # Define the number of classes
@@ -92,7 +92,7 @@ def is_in_distribution(features):
         distance = compute_mahalanobis_distance(features, mean, cov)
         distances.append(distance)
     min_distance = min(distances)
-    logger.info(f"Minimum Mahalanobis distance: {min_distance:.4f}")
+    logger.info(f"Minimum Mahalanobis distance: {min_distance:.4f}")  # Keep this log for calibration
     return min_distance < MAHALANOBIS_THRESHOLD
 
 # Prediction function for an uploaded image
@@ -105,7 +105,6 @@ def predict_from_image_url(image_url):
 
         # Apply transformations
         image_tensor = transform(image).unsqueeze(0)  # Shape: [1, 3, 224, 224]
-        logger.info(f"Input image tensor shape: {image_tensor.shape}")
 
         # Extract features from the penultimate layer
         with torch.no_grad():
@@ -129,10 +128,7 @@ def predict_from_image_url(image_url):
         # Stage 2: Main Model Prediction
         with torch.no_grad():
             outputs = main_model(image_tensor)  # Shape: [1, 3]
-            logger.info(f"Model output shape: {outputs.shape}")
-            logger.info(f"Raw logits: {outputs[0].numpy()}")
             probabilities = torch.softmax(outputs, dim=1)[0]  # Convert to probabilities
-            logger.info(f"Softmax probabilities: {probabilities.numpy()}")
             predicted_class = torch.argmax(outputs, dim=1).item()
 
         # Define class information