Create app.py

app.py

@@ -0,0 +1,55 @@
+import os
+import cv2
+import tempfile
+from modelscope.outputs import OutputKeys
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+import PIL
+from pathlib import Path
+import gradio as gr
+import numpy as np
+import requests
+from io import BytesIO
+from PIL import Image
+
+# Load the model into memory to make running multiple predictions efficien
+img_colorization = pipeline(Tasks.image_colorization, model='iic/cv_ddcolor_image-colorization')
+
+
+def load_image_from_url(url):
+    response = requests.get(url)
+    img = Image.open(BytesIO(response.content))
+    return img
+
+def inference(img, img_url=None):
+    if img_url:
+        img = load_image_from_url(img_url)
+    img = np.array(img)
+    output = img_colorization(img[..., ::-1])
+    result = output[OutputKeys.OUTPUT_IMG].astype(np.uint8)
+    temp_dir = tempfile.mkdtemp()
+    out_path = os.path.join(temp_dir, 'old-to-color.png')
+    cv2.imwrite(out_path, result)
+    upload_url = "https://api.postimages.org/upload"
+    files = {'file': open(out_path, 'rb')}
+    response = requests.post(upload_url, files=files)
+    files.close()
+    image_url = response.json()['url']  # رابط الصورة المحملة
+
+    return Path(out_path), image_url
+
+title = "Color Restorization Model"
+interface = gr.Interface(
+    inference,
+    inputs=[
+        gr.inputs.Image(type="pil", label="Input Image"),
+        gr.inputs.Textbox(placeholder="Enter Image URL (optional)", label="Image URL (optional)")
+    ],
+    outputs=[
+        gr.outputs.Image(type="pil", label="Output Image"),
+        gr.outputs.Textbox(label="Download Link")
+    ],
+    title=title
+)
+
+interface.launch(enable_queue=True)