return frame filepaths

app.py

@@ -22,7 +22,7 @@ with gr.Blocks() as demo:
             submit_btn = gr.Button(value="Generate")
         with gr.Column():
             animation = gr.Video(label="Result")
-            frames = gr.Gallery(type="pil", label="Frames", format="png")
+            frames = gr.Gallery(type="filepath", label="Frames", format="png")
 
     submit_btn.click(
         run_app, inputs=[char_imgs, mocap, tr_steps, inf_steps, fps, remove_bg, resize_inputs], outputs=[animation, frames]

main.py

@@ -84,6 +84,7 @@ max_batch_size = 8
 
 def save_temp_imgs(imgs):
     os.makedirs('temp', exist_ok=True)
+    results = []
 
     for img in imgs:
 
@@ -100,11 +101,16 @@ def save_temp_imgs(imgs):
 
             # Print the server's response
             print("Status Code:", response.status_code)
-            print("Response JSON:", response.json())
+
+            data = response.json()
+            print("Response JSON:", data)
+            results.append(data['data']['url'])
 
         except requests.exceptions.RequestException as e:
             print(f"An error occurred: {e}")
 
+    return results
+
 
 # Pose detection ==============================================================================================
 
@@ -1082,7 +1088,7 @@ def run_inference(images, video_path, train_steps=100, inference_steps=10, fps=1
     target_poses, in_pose = prepare_inputs_inference(in_img, video_path, fps, dwpose, 'target', is_app)
     
     results = inference(modelId, in_img, in_pose, target_poses, inference_steps, None, vae, unet, image_encoder_p, is_app)
-    save_temp_imgs(results)
+    urls = save_temp_imgs(results)
 
     if should_gen_vid:
         if debug:
@@ -1092,7 +1098,7 @@ 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', urls
     
 
 def run_app(images, video_path, train_steps=100, inference_steps=10, fps=12, bg_remove=False, resize_inputs=True):