Update app.py

app.py

@@ -1,71 +1,71 @@
-import gradio as gr
-from PIL import Image
-import os
-import tensorflow as tf
-import requests
-
-from .models.auto_encoder_gray2color import SpatialAttention
-
-# Load the saved model once at startup
-load_model_path = "./ckpts/best_model.h5"
-if not os.path.exists(load_model_path):
-    os.makedirs(os.path.dirname(load_model_path), exist_ok=True)
-    url = "https://huggingface.co/danhtran2mind/autoencoder-grayscale-to-color-landscape/resolve/main/ckpts/best_model.h5"
-    print(f"Downloading model from {url}...")
-    with requests.get(url, stream=True) as r:
-        r.raise_for_status()
-        with open(load_model_path, "wb") as f:
-            for chunk in r.iter_content(chunk_size=8192):
-                f.write(chunk)
-    print("Download complete.")
-
-print(f"Loading model from {load_model_path}...")
-loaded_autoencoder = tf.keras.models.load_model(
-    load_model_path,
-    custom_objects={'SpatialAttention': SpatialAttention}
-)
-
-def process_image(input_img):
-    # Convert PIL Image to numpy array and normalize
-    img = input_img.convert("RGB")
-    img = img.resize((256, 256))  # adjust size as needed
-    img_array = tf.keras.preprocessing.image.img_to_array(img) / 255.0
-    img_array = img_array[None, ...]  # add batch dimension
-
-    # Run inference
-    output_array = loaded_autoencoder.predict(img_array)
-    output_img = tf.keras.preprocessing.image.array_to_img(output_array[0])
-
-    return output_img
-
-custom_css = """
-body {background: linear-gradient(135deg, #232526 0%, #414345 100%) !important;}
-.gradio-container {background: transparent !important;}
-h1, .gr-title {color: #00e6d3 !important; font-family: 'Segoe UI', sans-serif;}
-.gr-description {color: #e0e0e0 !important; font-size: 1.1em;}
-.gr-input, .gr-output {border-radius: 18px !important; box-shadow: 0 4px 24px rgba(0,0,0,0.18);}
-.gr-button {background: linear-gradient(90deg, #00e6d3 0%, #0072ff 100%) !important; color: #fff !important; border: none !important; border-radius: 12px !important;}
-"""
-
-demo = gr.Interface(
-    fn=process_image,
-    inputs=gr.Image(type="pil", label="Upload Grayscale Landscape", image_mode="L", shape=(256, 256)),
-    outputs=gr.Image(type="pil", label="Colorized Output"),
-    title="🌄 Gray2Color Landscape Autoencoder",
-    description=(
-        "<div style='font-size:1.15em;line-height:1.6em;'>"
-        "Transform your <b>grayscale landscape</b> photos into vivid color with a state-of-the-art autoencoder.<br>"
-        "Simply upload a grayscale image and see the magic happen!"
-        "</div>"
-    ),
-    theme="soft",
-    css=custom_css,
-    allow_flagging="never",
-    examples=[
-        ["examples/grayscale_landscape1.jpg"],
-        ["examples/grayscale_landscape2.jpg"]
-    ]
-)
-
-if __name__ == "__main__":
+import gradio as gr
+from PIL import Image
+import os
+import tensorflow as tf
+import requests
+
+from models.auto_encoder_gray2color import SpatialAttention
+
+# Load the saved model once at startup
+load_model_path = "./ckpts/best_model.h5"
+if not os.path.exists(load_model_path):
+    os.makedirs(os.path.dirname(load_model_path), exist_ok=True)
+    url = "https://huggingface.co/danhtran2mind/autoencoder-grayscale-to-color-landscape/resolve/main/ckpts/best_model.h5"
+    print(f"Downloading model from {url}...")
+    with requests.get(url, stream=True) as r:
+        r.raise_for_status()
+        with open(load_model_path, "wb") as f:
+            for chunk in r.iter_content(chunk_size=8192):
+                f.write(chunk)
+    print("Download complete.")
+
+print(f"Loading model from {load_model_path}...")
+loaded_autoencoder = tf.keras.models.load_model(
+    load_model_path,
+    custom_objects={'SpatialAttention': SpatialAttention}
+)
+
+def process_image(input_img):
+    # Convert PIL Image to numpy array and normalize
+    img = input_img.convert("RGB")
+    img = img.resize((256, 256))  # adjust size as needed
+    img_array = tf.keras.preprocessing.image.img_to_array(img) / 255.0
+    img_array = img_array[None, ...]  # add batch dimension
+
+    # Run inference
+    output_array = loaded_autoencoder.predict(img_array)
+    output_img = tf.keras.preprocessing.image.array_to_img(output_array[0])
+
+    return output_img
+
+custom_css = """
+body {background: linear-gradient(135deg, #232526 0%, #414345 100%) !important;}
+.gradio-container {background: transparent !important;}
+h1, .gr-title {color: #00e6d3 !important; font-family: 'Segoe UI', sans-serif;}
+.gr-description {color: #e0e0e0 !important; font-size: 1.1em;}
+.gr-input, .gr-output {border-radius: 18px !important; box-shadow: 0 4px 24px rgba(0,0,0,0.18);}
+.gr-button {background: linear-gradient(90deg, #00e6d3 0%, #0072ff 100%) !important; color: #fff !important; border: none !important; border-radius: 12px !important;}
+"""
+
+demo = gr.Interface(
+    fn=process_image,
+    inputs=gr.Image(type="pil", label="Upload Grayscale Landscape", image_mode="L", shape=(256, 256)),
+    outputs=gr.Image(type="pil", label="Colorized Output"),
+    title="🌄 Gray2Color Landscape Autoencoder",
+    description=(
+        "<div style='font-size:1.15em;line-height:1.6em;'>"
+        "Transform your <b>grayscale landscape</b> photos into vivid color with a state-of-the-art autoencoder.<br>"
+        "Simply upload a grayscale image and see the magic happen!"
+        "</div>"
+    ),
+    theme="soft",
+    css=custom_css,
+    allow_flagging="never",
+    examples=[
+        ["examples/grayscale_landscape1.jpg"],
+        ["examples/grayscale_landscape2.jpg"]
+    ]
+)
+
+if __name__ == "__main__":
     demo.launch()