first commit

.gitignore

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+venv
+.env

app.py

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+import gradio as gr
+import requests
+import zipfile
+import io
+from PIL import Image
+import openai
+import os
+from scipy import ndimage
+from dotenv import load_dotenv
+import numpy as np
+
+load_dotenv()
+
+
+openai.api_key = os.getenv('OPENAI_API_KEY')
+removebg_api_key = os.getenv('REMOVEBG_API_KEY')
+
+
+def get_circle_footprint(size):
+    xx, yy = np.mgrid[0:size * 2, 0:size * 2]
+    fp = ((xx - size) ** 2 + (yy - size) ** 2) < (size) ** 2
+    return fp
+
+
+def test(prompt, img_path, mask_margin):
+    response = requests.post(
+        'https://api.remove.bg/v1.0/removebg',
+        files={'image_file': open(img_path, 'rb')},
+        data={
+            'size': 'auto',
+            'format': 'zip'
+        },
+        headers={'X-Api-Key': removebg_api_key},
+    )
+    if response.status_code == requests.codes.ok:
+        zipFile = zipfile.ZipFile(io.BytesIO(response.content))
+        maskIm = Image.open(io.BytesIO(zipFile.read('alpha.png')))
+
+        alpha= maskIm.getchannel(0)
+        if mask_margin > 0:
+            inflated_alpha = ndimage.maximum_filter(input=np.array(alpha), footprint=get_circle_footprint(mask_margin))
+            alpha = Image.fromarray(np.uint8(inflated_alpha))
+        maskIm.paste((255),[0,0,maskIm.size[0],maskIm.size[1]])
+        maskIm.putalpha(alpha)
+
+        maskFile = io.BytesIO()
+        maskIm.save(maskFile, format='PNG')
+        maskFile.seek(0)
+
+        response = openai.Image.create_edit(
+            image=open(img_path, "rb"),
+            mask=maskFile,
+            prompt=prompt,
+            n=1,
+            size="512x512"
+        )
+        return response['data'][0]['url']
+
+        # with open('no-bg.zip', 'wb') as out:
+        #     out.write(response.content)
+    else:
+        print("Error:", response.status_code, response.text)
+
+
+
+# demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+demo = gr.Interface(test, inputs = [
+    'text',
+    gr.Image(type='filepath', shape=(500,500), label='image'),
+    gr.Slider(minimum=0, maximum=10, value=5, step=1, label="mask margin")
+], outputs=["image"])
+
+demo.launch()