return thumbnails

app.py

@@ -23,6 +23,7 @@ with gr.Blocks() as demo:
         with gr.Column():
             animation = gr.Video(label="Result")
             frames = gr.Gallery(type="pil", label="Frames", format="png")
+            frames_thumb = gr.Gallery(type="pil", label="Thumbnails", format="png")
 
     submit_btn.click(
         run_app, inputs=[char_imgs, mocap, tr_steps, inf_steps, fps, remove_bg, resize_inputs], outputs=[animation, frames]
@@ -33,7 +34,7 @@ with gr.Blocks() as demo:
     )
     
     inference_btn.click(
-        run_inference, inputs=[char_imgs, mocap, tr_steps, inf_steps, fps, modelId, remove_bg, resize_inputs], outputs=[animation, frames]
+        run_inference, inputs=[char_imgs, mocap, tr_steps, inf_steps, fps, modelId, remove_bg, resize_inputs], outputs=[animation, frames, frames_thumb]
     )
 
 

main.py

@@ -127,6 +127,14 @@ def save_temp_imgs(imgs):
     return results
 
 
+def getThumbnails(imgs):
+    thumbs = []
+    thumb_size = (256, 256)
+    for img in imgs:
+        thumbs.append(img.thumbnail(thumb_size))
+    return thumbs
+
+
 # Pose detection ==============================================================================================
 
 def load_models():
@@ -1113,7 +1121,7 @@ def run_inference(images, video_path, train_steps=100, inference_steps=10, fps=1
     
     print("Done!")
     
-    return out_vid+'.webm', results
+    return out_vid+'.webm', results, getThumbnails(results)
     
 
 def run_app(images, video_path, train_steps=100, inference_steps=10, fps=12, bg_remove=False, resize_inputs=True):