Merge branch 'main' of https://huggingface.co/spaces/dylanplummer/NextJump

app.py

@@ -98,9 +98,9 @@ def inference(x, count_only_api, api_key, img_size=192, seq_len=64, stride_lengt
     period_lengths = np.zeros(len(all_frames) + seq_len + stride_length)
     periodicities = np.zeros(len(all_frames) + seq_len + stride_length)
     full_marks = np.zeros(len(all_frames) + seq_len + stride_length)
-    event_type_logits = np.zeros((len(all_frames) + seq_len + stride_length, 6))
+    event_type_logits = np.zeros((len(all_frames) + seq_len + stride_length, 7))
     period_length_overlaps = np.zeros(len(all_frames) + seq_len + stride_length)
-    event_type_logit_overlaps = np.zeros((len(all_frames) + seq_len + stride_length, 6))
+    event_type_logit_overlaps = np.zeros((len(all_frames) + seq_len + stride_length, 7))
     for _ in range(seq_len + stride_length):  # pad full sequence
         all_frames.append(all_frames[-1])
     batch_list = []
@@ -240,7 +240,7 @@ def inference(x, count_only_api, api_key, img_size=192, seq_len=64, stride_lengt
     jumps_per_second[misses] = 0
     frame_type = np.array(['miss' if miss else 'frame' for miss in misses])
     frame_type[full_marks > marks_threshold] = 'jump'
-    per_frame_event_types = np.clip(per_frame_event_types, 0, 6) / 6
+    per_frame_event_types = np.clip(per_frame_event_types, 0, 7) / 7
     df = pd.DataFrame.from_dict({'period length': periodLength, 
                                  'jumping speed': jumping_speed,
                                 'jumps per second': jumps_per_second,
@@ -302,7 +302,7 @@ def inference(x, count_only_api, api_key, img_size=192, seq_len=64, stride_lengt
     
     # make a bar plot of the event type distribution
 
-    bar = px.bar(x=['single rope speed', 'double dutch', 'double unders', 'single bounces', 'double bounces', 'triple unders'], 
+    bar = px.bar(x=['single rope speed', 'double dutch', 'double unders', 'single bounces', 'double bounces', 'triple unders', 'other'], 
                  y=event_type_probs,
                  template="plotly_dark",
                  title="Event Type Distribution",