Add application file

app.py

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+import gradio as gr
+from PIL import Image
+
+inimg = gr.Image()
+outimg = gr.AnnotatedImage()
+custom_html = '''
+<center>
+  <div style="overflow:hidden ; max-width: fit-content; margin-left: auto; margin-right: auto;">
+      <div style="float: left; font-family: Arial; font-size: 25px; color: orange; margin: 10px;">Please</div>
+        <div style="float: left">
+            <a href="https://www.buymeacoffee.com/alloc7260">
+                <img src="https://img.buymeacoffee.com/button-api/?text=Buy me a coffee&emoji=☕&slug=alloc7260&button_colour=FFDD00&font_colour=000000&font_family=Arial&outline_colour=000000&coffee_colour=ffffff" />
+            </a>
+        </div>
+      <div style="float: left; font-family: Arial; font-size: 25px; color: orange; margin: 10px;">for upgrade to gpu instance</div>
+  </div>
+</center>
+'''
+
+def resize_image(image, max_width=1500):
+    original_width, original_height = image.size
+    aspect_ratio = original_width / original_height
+    new_width = min(original_width, max_width)
+    new_height = int(new_width / aspect_ratio)
+    resized_image = image.resize((new_width, new_height), Image.LANCZOS)
+    return resized_image
+
+def mask(img):
+  PIL_image = Image.fromarray(img.astype('uint8'), 'RGB')
+  resimg = resize_image(PIL_image)
+  import torch
+  from transformers import pipeline
+  with torch.inference_mode():
+    generator =  pipeline("mask-generation", "facebook/sam-vit-base", points_per_batch = 64)
+    outputs = generator(resimg, points_per_batch = 64)
+    outputs_masks = outputs['masks']
+    torch.cuda.empty_cache()
+  return (resimg, [(outputs_masks[i], str(i + 1)) for i in range(len(outputs_masks))])
+
+interface = gr.Blocks()
+
+with interface:
+  gr.Interface(mask, inimg, outimg)
+  che = gr.HTML(custom_html)
+
+interface.launch(show_api=False, debug=True)  

requirements.txt

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+torch==2.2.1
+transformers==4.38.2
+Pillow==9.4.0
+gradio==4.25.0