Update app.py

app.py

@@ -8,48 +8,27 @@ 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
+"""Load the model into memory to make running multiple predictions efficient"""
 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):
+    image = cv2.imread(str(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])
+    output = img_colorization(image[..., ::-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)
 
-    return Path(out_path), image_url
 
 title = "Color Restorization Model"
-interface = gr.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")
-    ],
+    [gr.inputs.Image(type="filepath", label="Input")],
+    gr.outputs.Image(type="pil", label="Output"),
     title=title
-)
-
-interface.launch(enable_queue=True)
+).launch(enable_queue=True)