move debug messages to logging.debug instead of info

app.py

@@ -79,15 +79,15 @@ def damage_classification(SEM_image,image_threshold, model1_threshold, model2_th
     # inclusions = np.where(inclusions > model1_threshold)
 
     batch_model1 = np.array(images_model1, dtype=np.float32)
-    logging.info(f"Model 1 input shape: {batch_model1.shape}")
+    logging.debug(f"Model 1 input shape: {batch_model1.shape}")
     # Get predictions from model 1
     y1_pred_raw = model1(batch_model1)
-    logging.info(f"Model 1 raw output type: {type(y1_pred_raw)}")
+    logging.debug(f"Model 1 raw output type: {type(y1_pred_raw)}")
     
     # Extract actual predictions from the model output
     y1_pred = utils.extract_predictions_from_tfsm(y1_pred_raw)
-    logging.info(f"Model 1 predictions shape: {y1_pred.shape}")
-    logging.info(f"Model 1 predictions sample: {y1_pred[:3] if len(y1_pred) > 0 else 'Empty'}")
+    logging.debug(f"Model 1 predictions shape: {y1_pred.shape}")
+    logging.debug(f"Model 1 predictions sample: {y1_pred[:3] if len(y1_pred) > 0 else 'Empty'}")
     
     logging.info('---------------: model1 threshold :=====================')
     # Handle predictions based on their shape
@@ -137,15 +137,15 @@ def damage_classification(SEM_image,image_threshold, model1_threshold, model2_th
     #y2_pred = model2.predict(np.asarray(images_model2, float))
     #y2_pred = model2(np.asarray(images_model2, float))
     batch_model2 = np.array(images_model2, dtype=np.float32)
-    logging.info(f"Model 2 input shape: {batch_model2.shape}")
+    logging.debug(f"Model 2 input shape: {batch_model2.shape}")
     # Get predictions from model 2
     y2_pred_raw = model2(batch_model2)
-    logging.info(f"Model 2 raw output type: {type(y2_pred_raw)}")
+    logging.debug(f"Model 2 raw output type: {type(y2_pred_raw)}")
     # Extract actual predictions from the model output
     y2_pred = utils.extract_predictions_from_tfsm(y2_pred_raw)
-    logging.info(f"Model 2 predictions shape: {y2_pred.shape}")
-    logging.info(f"Model 2 predictions sample: {y2_pred[:3] if len(y2_pred) > 0 else 'Empty'}")
-    logging.info(y2_pred)
+    logging.debug(f"Model 2 predictions shape: {y2_pred.shape}")
+    logging.debug(f"Model 2 predictions sample: {y2_pred[:3] if len(y2_pred) > 0 else 'Empty'}")
+    logging.debug(y2_pred)
 
     logging.debug('---------------: model2 threshold :=====================')