Update app.py

app.py

@@ -363,7 +363,6 @@ def inference(in_video, stream_url, start_time, end_time, beep_detection_on, eve
                 period_length_overlaps[idx:idx+seq_len] += 1
                 event_type_logit_overlaps[idx:idx+seq_len] += 1
             del y1_out, y2_out, y3_out, y4_out  # free up memory
-            del y1_out, y2_out, y3_out, y4_out  # free up memory
             
     periodLength = np.divide(period_lengths, period_length_overlaps, where=period_length_overlaps!=0)[:length]
     periodicity = np.divide(periodicities, period_length_overlaps, where=period_length_overlaps!=0)[:length]
@@ -424,11 +423,6 @@ def inference(in_video, stream_url, start_time, end_time, beep_detection_on, eve
             confidence = 0
         else:
             confidence = (np.mean(periodicity[periodicity > miss_threshold]) - miss_threshold) / (1 - miss_threshold)
-        periodicity_mask = periodicity > miss_threshold
-        if np.sum(periodicity_mask) == 0:
-            confidence = 0
-        else:
-            confidence = (np.mean(periodicity[periodicity > miss_threshold]) - miss_threshold) / (1 - miss_threshold)
     except ZeroDivisionError:
         confidence = 0
     self_err = abs(count_pred - marks_count_pred)
@@ -442,16 +436,8 @@ def inference(in_video, stream_url, start_time, end_time, beep_detection_on, eve
         count_msg = f"## Reps Count (both feet): {count_pred:.1f}, Marks Count (both feet): {marks_count_pred:.1f}, Confidence: {total_confidence:.2f}"
     else:
         count_msg = f"## Reps Count (one foot): {count_pred:.1f}, Marks Count (one foot): {marks_count_pred:.1f}, Confidence: {total_confidence:.2f}"
-        count_msg = f"## Reps Count (one foot): {count_pred:.1f}, Marks Count (one foot): {marks_count_pred:.1f}, Confidence: {total_confidence:.2f}"
 
     if api_call:
-        if CACHE_API_CALLS:
-            # write outputs as row of csv
-            with open('api_calls.tsv', 'a') as f:
-                periodicity_str = np.array2string(periodicity, formatter={'float_kind':lambda x: "%.2f" % x}, threshold=np.inf).replace('\n', '')
-                periodLength_str = np.array2string(periodLength, formatter={'float_kind':lambda x: "%.2f" % x}, threshold=np.inf).replace('\n', '')
-                full_marks_str = np.array2string(full_marks, formatter={'float_kind':lambda x: "%.2f" % x}, threshold=np.inf).replace('\n', '')
-                f.write(f"{stream_url}\t{start_time}\t{end_time}\t{beep_detection_on}\t{event_length}\t{periodicity_str}\t{periodLength_str}\t{full_marks_str}\t{count_pred}\t{total_confidence}\n")
         if CACHE_API_CALLS:
             # write outputs as row of csv
             with open('api_calls.tsv', 'a') as f:
@@ -561,11 +547,6 @@ def inference(in_video, stream_url, start_time, end_time, beep_detection_on, eve
     except FileNotFoundError:
         pass
     
-    try:
-        os.remove('temp.wav')
-    except FileNotFoundError:
-        pass
-    
     return in_video, count_msg, fig, hist, bar
         
 
@@ -622,7 +603,6 @@ with gr.Blocks() as demo:
     examples = [
         #['https://hiemdall-dev2.azurewebsites.net/api/clip/clp_vrpWTyjM/mp4', '00:00:00', '00:01:10', True, 60],
         [None, 'https://hiemdall-dev2.azurewebsites.net/api/playlist/rec_UGEhqlMh/vod', '00:00:18', '00:00:55', True, 30],
-        [None, 'https://hiemdall-dev2.azurewebsites.net/api/playlist/rec_UGEhqlMh/vod', '00:00:18', '00:00:55', True, 30],
         #['https://hiemdall-dev2.azurewebsites.net/api/playlist/rec_rd2FAyUo/vod', '01:24:22', '01:25:35', True, 60]
         #['https://hiemdall-dev2.azurewebsites.net/api/playlist/rec_PY5Ukaua/vod, '00:52:53', '00:55:00', True, 120]
     ]