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app.py

@@ -1,6 +1,7 @@
 import torch
 import gradio as gr
 from clip_interrogator import Config, Interrogator
+import spaces
 
 
 config = Config()
@@ -10,9 +11,15 @@ config.chunk_size = 2048
 config.flavor_intermediate_count = 512
 config.blip_num_beams = 64
 ci = Interrogator(config)
+css = """
+#col-container {
+    margin: 0 auto;
+    max-width: 580px;
+}
+"""
 
-
-def inference(image, mode, best_max_flavors):
+@spaces.GPU
+def infer(image, mode, best_max_flavors):
     image = image.convert('RGB')
     if mode == 'best':
         prompt_result = ci.interrogate(image, max_flavors=int(best_max_flavors))
@@ -23,17 +30,17 @@ def inference(image, mode, best_max_flavors):
     return prompt_result
 
 
-with gr.Blocks() as demo:
-    with gr.Column():
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
         gr.Markdown("# CLIP Interrogator")
         input_image = gr.Image(type='pil', elem_id="input-img")
         with gr.Row():
             mode_input = gr.Radio(['best', 'classic', 'fast'], label='Select mode', value='best')
             flavor_input = gr.Slider(minimum=2, maximum=48, step=2, value=32, label='best mode max flavors')
-        submit_btn = gr.Button("Submit")
+        run_button = gr.Button("Submit")
         output_text = gr.Textbox(label="Description Output")
-    submit_btn.click(
-        fn=inference,
+    run_button.click(
+        fn=infer,
         inputs=[input_image, mode_input, flavor_input],
         outputs=[output_text],
         concurrency_limit=10