Update app.py

app.py

@@ -25,33 +25,35 @@ aesthetic_pipe = pipeline("image-classification",
                           torch_dtype=dtype)
 
 def predict(image, files=None):
-  print(image, files)
-  images_paths = [image]
-  if not files == None: 
+    print(image, files)
+    images_paths = [image]
+    if not files == None: 
     images_paths = list(map(lambda x: x.name, files))
-  pil_images = [Image.open(image_path).convert("RGB") for image_path in images_paths]
-  
-  style = style_pipe(pil_images)
-  aesthetic = aesthetic_pipe(pil_images)
-  nsfw = nsfw_pipe(pil_images)
-  results = [ a + b + c  for (a,b,c) in zip(style, aesthetic, nsfw)]
-
-  label_data = [{ row["label"]:row["score"] for row in image } for image in results]
+    pil_images = [Image.open(image_path).convert("RGB") for image_path in images_paths]
+    
+    style = style_pipe(pil_images)
+    aesthetic = aesthetic_pipe(pil_images)
+    nsfw = nsfw_pipe(pil_images)
+    results = [ a + b + c  for (a,b,c) in zip(style, aesthetic, nsfw)]
+    
+    label_data = {}
+    if image:
+        label_data = [{ row["label"]:row["score"] for row in image } for image in results]
                 
-  return label_data[0], label_data
+    return label_data[0], results
 
 with gr.Blocks() as blocks:
-  with gr.Row():
-    with gr.Column():
-      image = gr.Image(label="Image to test", type="filepath")
-      files = gr.File(label="Multipls Images", file_types=["image"], file_count="multiple")
-    with gr.Column():
-      label = gr.Label(label="style")
-      results = gr.JSON(label="Results")
-      # gallery = gr.Gallery().style(grid=[2], height="auto")
-  btn = gr.Button("Run")
-
-  btn.click(fn=predict, inputs=[image, files], outputs=[label, results], api_name="inference")
+    with gr.Row():
+        with gr.Column():
+            image = gr.Image(label="Image to test", type="filepath")
+            files = gr.File(label="Multipls Images", file_types=["image"], file_count="multiple")
+        with gr.Column():
+            label = gr.Label(label="style")
+            results = gr.JSON(label="Results")
+            # gallery = gr.Gallery().style(grid=[2], height="auto")
+    btn = gr.Button("Run")
+    
+    btn.click(fn=predict, inputs=[image, files], outputs=[label, results], api_name="inference")
 
 blocks.queue()
 blocks.launch(debug=True,inline=True)