add count only api option

app.py

@@ -118,7 +118,7 @@ def confidence_analysis(periodicity, counts, frames, out_dir='confidence_animati
     plt.close(fig)
 
 
-def inference(x, img_size=192, seq_len=64, stride_length=32, stride_pad=3, batch_size=4, miss_threshold=0.85, median_pred_filter=True, center_crop=True, both_feet=True, api_call=False):
+def inference(x, count_only_api, img_size=192, seq_len=64, stride_length=32, stride_pad=3, batch_size=4, miss_threshold=0.85, median_pred_filter=True, center_crop=True, both_feet=True, api_call=False):
     print(x)
     api = HfApi(token=os.environ['DATASET_SECRET'])
     out_file = str(uuid.uuid1())
@@ -217,9 +217,7 @@ def inference(x, img_size=192, seq_len=64, stride_length=32, stride_pad=3, batch
             
     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]
-    if api_call:
-        return np.array2string(periodLength, formatter={'float_kind':lambda x: "%.3f" % x}).replace('\n', ''), np.array2string(periodicity, formatter={'float_kind':lambda x: "%.3f" % x}).replace('\n', '')
-   
+    
     if median_pred_filter:
         periodicity = medfilt(periodicity, 5)
         periodLength = medfilt(periodLength, 5)
@@ -243,6 +241,15 @@ def inference(x, img_size=192, seq_len=64, stride_length=32, stride_pad=3, batch
     else:
         count_msg = f"## Predicted Count (one foot): {count_pred:.1f}"
 
+    if api_call:
+        if count_only_api:
+            return f"{count_pred:.2f}"
+        else:
+            return np.array2string(periodLength, formatter={'float_kind':lambda x: "%.2f" % x}).replace('\n', ''), \
+                np.array2string(periodicity, formatter={'float_kind':lambda x: "%.2f" % x}).replace('\n', ''), \
+                np.array2string(count_pred, formatter={'float_kind':lambda x: "%.2f" % x}).replace('\n', '')
+   
+
     jumps_per_second = np.clip(1 / ((periodLength / fps) + 0.05), 0, 8)
     jumping_speed = np.copy(jumps_per_second)
     misses = periodicity < miss_threshold
@@ -306,11 +313,12 @@ with gr.Blocks() as demo:
     gr.Markdown(DESCRIPTION)
     with gr.Column():
         with gr.Row():
-            in_video = gr.Video(type="file", label="Input Video", elem_id='input-video', format='mp4').style(width=400)
-            with gr.Column():
-                with gr.Row():
-                    run_button = gr.Button(label="Run", elem_id='run-button').style(full_width=False)
-                    count_only = gr.Button(label="Run (No Viz)", elem_id='count-only', visible=False).style(full_width=False)
+            in_video = gr.Video(label="Input Video", elem_id='input-video', format='mp4', width=400, scale=2)
+            
+    with gr.Row():
+        run_button = gr.Button(label="Run", elem_id='run-button', style=dict(full_width=False), scale=1)
+        api_dummy_button = gr.Button(label="Run (No Viz)", elem_id='count-only', visible=False, scale=2)
+        count_only = gr.Checkbox(label="Count Only", visible=False)
 
     with gr.Column(elem_id='output-video-container'):
         with gr.Row():
@@ -322,8 +330,7 @@ with gr.Blocks() as demo:
                 #out_video = gr.PlayableVideo(label="Output Video", elem_id='output-video', format='mp4')
         out_plot = gr.Plot(label="Jumping Speed", elem_id='output-plot')
         out_hist = gr.Plot(label="Speed Histogram", elem_id='output-hist')
-                
-    inputs = [in_video]
+              
     with gr.Accordion(label="Instructions and more information", open=False):
         instructions = "## Instructions:"
         instructions += "\n* Upload a video and click 'Run' to get a prediction of the number of jumps (either one foot, or both). This could take a couple minutes! The model is trained on single rope and double dutch speed, but try out any videos you want."
@@ -346,13 +353,13 @@ with gr.Blocks() as demo:
                         [a, True, False, -1, True, 1.0, 0.95],
                         [b, False, True, -1, True, 1.0, 0.95],
                     ],
-                inputs=inputs,
+                inputs=[in_video],
                 outputs=[out_text, out_plot, out_hist],
                 fn=inference, cache_examples=os.getenv('SYSTEM') == 'spaces')
-
-    run_button.click(inference, inputs, outputs=[out_text, out_plot, out_hist])
+    demo_inference = partial(inference, count_only_api=False)
+    run_button.click(demo_inference, [in_video], outputs=[out_text, out_plot, out_hist])
     api_inference = partial(inference, api_call=True)
-    count_only.click(api_inference, inputs, outputs=[period_length, periodicity], api_name='inference')
+    api_dummy_button.click(api_inference, [in_video, count_only], outputs=[period_length], api_name='inference')
 
 
 if __name__ == "__main__":