Feat: Add message warning when reaching the final example

load_and_save.py

@@ -43,8 +43,8 @@ def load_first_example(annotations_df, file_list_df, id, completed, index):
 
     if os.path.exists(path_ann):
         annotations_df = pd.read_csv(path_ann, keep_default_na=False)
-        index = len(annotations_df)
-        completed = len(annotations_df) - 1 # update how many examples were completed
+        index = min(len(file_list_df) - 1, len(annotations_df))
+        completed = len(annotations_df) # update how many examples were completed
 
     else: 
         # Initialize an empty DataFrame to store annotations
@@ -74,21 +74,39 @@ def load_example(annotations_df, file_list_df, index):
     * duration: current sentence duration
     
     """
-
-    row = file_list_df.iloc[index]
-    audio_path = os.path.join(persistent_storage, 'files_to_annotate_2round', row["sample_id"].split('-')[0], row["sample_id"] + '.wav')
-    sentence = row["sentence"]
-
-    # If the user already made an annotation for this example, gradio will return said annotation
-    ann = (
-        annotations_df.iloc[index].to_dict() if index < len(annotations_df) else {"sample_id": row["sample_id"], "emotion": 'Blank', "confidence": 'Blank',
-                                                                            "comments": '', "n_clicks": 0}
-    )
-
-    start = row['start']
-    end = row['end']
-    duration = get_audio_duration(audio_path)
-    print(f'start/end/duration (load example) - {start} {end} {duration}')
+    if index < len(file_list_df):
+        row = file_list_df.iloc[index]
+        audio_path = os.path.join(persistent_storage, 'files_to_annotate_2round', row["sample_id"].split('-')[0], row["sample_id"] + '.wav')
+        sentence = row["sentence"]
+
+        # If the user already made an annotation for this example, gradio will return said annotation
+        ann = (
+            annotations_df.iloc[index].to_dict() if index < len(annotations_df) else {"sample_id": row["sample_id"], "emotion": 'Blank', "confidence": 'Blank',
+                                                                                "comments": '', "n_clicks": 0}
+        )
+
+        start = row['start']
+        end = row['end']
+        duration = get_audio_duration(audio_path)
+        print(f'start/end/duration (load example) - {start} {end} {duration}')
+    else:
+        index -= 1
+        row = file_list_df.iloc[index]
+        audio_path = os.path.join(persistent_storage, 'files_to_annotate_2round', row["sample_id"].split('-')[0], row["sample_id"] + '.wav')
+        sentence = row["sentence"]
+
+        # If the user already made an annotation for this example, gradio will return said annotation
+        ann = (
+            annotations_df.iloc[index].to_dict() if index < len(annotations_df) else {"sample_id": row["sample_id"], "emotion": 'Blank', "confidence": 'Blank',
+                                                                                "comments": '', "n_clicks": 0}
+        )
+
+        start = row['start']
+        end = row['end']
+        duration = get_audio_duration(audio_path)
+        print(f'start/end/duration (load example) - {start} {end} {duration}')
+
+        gr.Warning("This is the last example, well done!")
     return sentence, audio_path, ann['emotion'], ann['confidence'], ann["comments"], ann['n_clicks'], start, end, duration
 
 
@@ -162,7 +180,10 @@ def next_example(annotations_df, file_list_df, emotions, confidence, comments, n
         annotations_df, ann_completed = save_annotation(annotations_df, file_list_df, emotions, confidence, comments, n_clicks, participant_id, ann_completed, current_index)
         if current_index < len(file_list_df) - 1:
             current_index += 1
-
+        
+        else:
+           gr.Warning("This is the last example, well done!")
+    print(f'current_index {current_index}')
     sentence, audio_path, emotions, confidence, comments, clicks, start, end, duration = load_example(annotations_df, file_list_df, current_index)    
     return annotations_df, sentence, audio_path, emotions, confidence, comments, clicks, gr.State(start), gr.State(end), gr.State(duration), ann_completed, current_index