comment debug codes

handler.py

@@ -165,11 +165,11 @@ def cluster_windows(predicted_patterns , probability_threshold, window_size,eps
 
     for pattern, group in df.groupby('Chart Pattern'):
         centers = group['Center'].values.reshape(-1, 1)
-        print("centers: ", centers)
+        # print("centers: ", centers)
 
         if min_center < max_center:
             norm_centers = (centers - min_center) / (max_center - min_center)
-            print("Norm Center: ",norm_centers)
+            # print("Norm Center: ",norm_centers)
         else:
             norm_centers = np.ones_like(centers)
 
@@ -177,7 +177,7 @@ def cluster_windows(predicted_patterns , probability_threshold, window_size,eps
         print("DBSCAN \n", db)
         group['Cluster'] = db.labels_
         cluster_labled_windows.append(group)
-        print(cluster_labled_windows)
+        # print(cluster_labled_windows)
 
         for cluster_id, cluster_group in group[group['Cluster'] != -1].groupby('Cluster'):
             expanded_dates = []
@@ -328,10 +328,10 @@ class EndpointHandler:
                 # For now, if the model *needs* clean data, this implicitly is a "bad input" if NaNs appear.
                 # If your model handles NaNs gracefully, then this is just a warning.
 
-            # Print head after all processing to see the final DataFrame state
-            print("\n--- HANDLER: OHLC Data after all input processing ---")
-            print(ohlc_data.head().to_string())
-            print("--- END HANDLER DEBUG ---")
+            # # Print head after all processing to see the final DataFrame state
+            # print("\n--- HANDLER: OHLC Data after all input processing ---")
+            # print(ohlc_data.head().to_string())
+            # print("--- END HANDLER DEBUG ---")
 
         except Exception as e:
             print(f"Error processing input data: {e}")
@@ -389,8 +389,8 @@ class EndpointHandler:
                 print("Predicted patterns dataframe is empty")
                 continue
 
-            print("Predicted Patterns intermediate")
-            print(predicted_patterns)
+            # print("Predicted Patterns intermediate")
+            # print(predicted_patterns)
 
             # Pass eps and min_samples from handler's state
             cluster_labled_windows_df , interseced_clusters_df = cluster_windows(