" rather than '

app.py

@@ -31,7 +31,7 @@ if torch.cuda.device_count() > 0:
     snapshot_download(repo_id="openai/clip-vit-large-patch14", repo_type="model", local_dir="ckpts/text_encoder_2", force_download=True)
 
 def initialize_model(model_path):
-    print('initialize_model: ' + model_path)
+    print("initialize_model: " + model_path)
     if torch.cuda.device_count() == 0:
         return None
     
@@ -42,7 +42,7 @@ def initialize_model(model_path):
     
     print(f"`models_root` exists: {models_root_path}")
     hunyuan_video_sampler = HunyuanVideoSampler.from_pretrained(models_root_path, args=args)
-    print('Model initialized: ' + model_path)
+    print("Model initialized: " + model_path)
     return hunyuan_video_sampler
 
 model = initialize_model("ckpts")
@@ -57,7 +57,7 @@ def generate_video(
     flow_shift,
     embedded_guidance_scale
 ):
-    print('generate_video (prompt: ' + prompt + ')')
+    print("generate_video (prompt: " + prompt + ")")
     return generate_video_gpu(
         model,
         prompt,
@@ -82,16 +82,16 @@ def generate_video_gpu(
     flow_shift,
     embedded_guidance_scale
 ):
-    print('generate_video_gpu (prompt: ' + prompt + ')')
+    print("generate_video_gpu (prompt: " + prompt + ")")
     if torch.cuda.device_count() == 0:
-        gr.Warning('Set this space to GPU config to make it work.')
+        gr.Warning("Set this space to GPU config to make it work.")
         return None
     
     seed = None if seed == -1 else seed
     width, height = resolution.split("x")
     width, height = int(width), int(height)
     negative_prompt = "" # not applicable in the inference
-    print('Predicting video...')
+    print("Predicting video...")
 
     outputs = model.predict(
         prompt=prompt,
@@ -108,8 +108,8 @@ def generate_video_gpu(
         embedded_guidance_scale=embedded_guidance_scale
     )
     
-    print('Video predicted')
-    samples = outputs['samples']
+    print("Video predicted")
+    samples = outputs["samples"]
     sample = samples[0].unsqueeze(0)
     
     save_path = "./gradio_outputs"
@@ -118,9 +118,9 @@ def generate_video_gpu(
     time_flag = datetime.fromtimestamp(time.time()).strftime("%Y-%m-%d-%H:%M:%S")
     video_path = f"{save_path}/{time_flag}_seed{outputs['seeds'][0]}_{outputs['prompts'][0][:100].replace('/','')}.mp4"
     save_videos_grid(sample, video_path, fps=24)
-    logger.info(f'Sample saved to: {video_path}')
+    logger.info(f"Sample saved to: {video_path}")
     
-    print('Return the video')
+    print("Return the video")
     return video_path
 
 def create_demo(model_path):