Try to fix the flagging mode error

.specstory/history/2025-03-27_10-40-gradio-code-update-and-error-fix.md

@@ -81,6 +81,44 @@ These changes maintain all the same functionality while using the current Gradio
 
 ---
 
+_**User**_
+
+Now the error become this
+
+```
+/usr/local/lib/python3.10/site-packages/gradio/interface.py:415: UserWarning: The `allow_flagging` parameter in `Interface` is deprecated.Use `flagging_mode` instead.
+  warnings.warn(
+Traceback (most recent call last):
+  File "/home/user/app/app.py", line 73, in <module>
+    demo = gr.Interface(
+  File "/usr/local/lib/python3.10/site-packages/gradio/interface.py", line 425, in __init__
+    raise ValueError(
+ValueError: Invalid value for `flagging_mode` parameter.Must be: 'auto', 'manual', or 'never'.
+```
+
+---
+
+_**Assistant**_
+
+
+
+---
+
+```diff
+      outputs=gr.Image(type="numpy", label="Generated - Conditioned Images"),
+      examples=examples,
+-     allow_flagging=False,
++     flagging_mode="never",
+      analytics_enabled=False,
+      title=title,
+```
+
+---
+
+I've updated the code by replacing the deprecated `allow_flagging=False` parameter with the new `flagging_mode="never"` parameter. The newer versions of Gradio have replaced the boolean flag with a more flexible string parameter that supports different flagging modes: 'auto', 'manual', or 'never'.
+
+---
+
 
 
 ---

app.py

@@ -8,79 +8,109 @@ import gradio as gr
 from huggingface_hub import from_pretrained_keras
 
 model = from_pretrained_keras("keras-io/GauGAN-Image-generation")
-  
+
+
 def predict(image_file, segmentation_png, bitmap_img):
 
-  image_list = [segmentation_png, image_file, bitmap_img]
+    image_list = [segmentation_png, image_file, bitmap_img]
+
+    image = tf.image.decode_png(tf.io.read_file(image_list[1]), channels=3)
+    image = tf.cast(image, tf.float32) / 127.5 - 1
 
-  image = tf.image.decode_png(tf.io.read_file(image_list[1]), channels=3)
-  image = tf.cast(image, tf.float32) / 127.5 - 1
+    segmentation_file = tf.image.decode_png(tf.io.read_file(image_list[0]), channels=3)
+    segmentation_file = tf.cast(segmentation_file, tf.float32) / 127.5 - 1
 
-  segmentation_file = tf.image.decode_png(tf.io.read_file(image_list[0]), channels=3)
-  segmentation_file = tf.cast(segmentation_file, tf.float32)/127.5 - 1
+    label_file = tf.image.decode_bmp(tf.io.read_file(image_list[2]), channels=0)
+    # label_file = tf.image.rgb_to_grayscale(tf.image.decode_bmp(tf.io.read_file(image_list[2]), channels=3))
+    # print("after decode_bmp --> ", label_file.shape, type(label_file))
+    label_file = tf.squeeze(label_file)
 
-  label_file = tf.image.decode_bmp(tf.io.read_file(image_list[2]), channels=0)
-  # label_file = tf.image.rgb_to_grayscale(tf.image.decode_bmp(tf.io.read_file(image_list[2]), channels=3))
-  # print("after decode_bmp --> ", label_file.shape, type(label_file))
-  label_file = tf.squeeze(label_file)
+    image_list = [segmentation_file, image, label_file]
 
-  image_list = [segmentation_file, image, label_file]
+    crop_size = tf.convert_to_tensor((256, 256))
 
-  crop_size = tf.convert_to_tensor((256, 256))
+    image_shape = tf.shape(image_list[1])[:2]
 
-  image_shape = tf.shape(image_list[1])[:2]
+    margins = image_shape - crop_size
 
-  margins = image_shape - crop_size
+    y1 = tf.random.uniform(shape=(), maxval=margins[0], dtype=tf.int32)
+    x1 = tf.random.uniform(shape=(), maxval=margins[1], dtype=tf.int32)
+    y2 = y1 + crop_size[0]
+    x2 = x1 + crop_size[1]
 
-  y1 = tf.random.uniform(shape=(), maxval=margins[0], dtype=tf.int32)
-  x1 = tf.random.uniform(shape=(), maxval=margins[1], dtype=tf.int32)
-  y2 = y1 + crop_size[0]
-  x2 = x1 + crop_size[1]
+    cropped_images = []
+    for img in image_list:
+        cropped_images.append(img[y1:y2, x1:x2])
 
-  cropped_images = []
-  for img in image_list:
-    cropped_images.append(img[y1:y2, x1:x2])
+    final_img_list = [
+        tf.expand_dims(cropped_images[0], axis=0),
+        tf.expand_dims(cropped_images[1], axis=0),
+        tf.expand_dims(tf.one_hot(cropped_images[2], 12), axis=0),
+    ]
 
-  final_img_list = [tf.expand_dims(cropped_images[0], axis=0), tf.expand_dims(cropped_images[1], axis=0), tf.expand_dims(tf.one_hot(cropped_images[2], 12), axis=0)]
+    # print(final_img_list[0].shape)
+    # print(final_img_list[1].shape)
+    # print(final_img_list[2].shape)
 
-  # print(final_img_list[0].shape)
-  # print(final_img_list[1].shape)
-  # print(final_img_list[2].shape)
+    latent_vector = tf.random.normal(shape=(1, 256), mean=0.0, stddev=2.0)
 
-  latent_vector = tf.random.normal(shape=(1, 256), mean=0.0, stddev=2.0)
+    # Generate fake images
+    fake_image = model.predict([latent_vector, final_img_list[2]])
+    fake_img = tf.squeeze(fake_image, axis=0)
+
+    return np.array((fake_img + 1) / 2)
 
-  # Generate fake images
-  fake_image = model.predict([latent_vector, final_img_list[2]])
-  fake_img = tf.squeeze(fake_image, axis=0)
 
-  return np.array((fake_img+1)/2)
-  
 # Define inputs with modern Gradio syntax
 ground_truth = gr.Image(type="filepath", label="Ground Truth - Real Image (jpg)")
 segmentation = gr.Image(type="filepath", label="Corresponding Segmentation (png)")
-bitmap = gr.Image(type="filepath", label="Corresponding bitmap image (bmp)", image_mode="L")
+bitmap = gr.Image(
+    type="filepath", label="Corresponding bitmap image (bmp)", image_mode="L"
+)
 
-examples = [["facades_data/cmp_b0010.jpg", "facades_data/cmp_b0010.png", "facades_data/cmp_b0010.bmp"],
-            ["facades_data/cmp_b0020.jpg", "facades_data/cmp_b0020.png", "facades_data/cmp_b0020.bmp"],
-            ["facades_data/cmp_b0030.jpg", "facades_data/cmp_b0030.png", "facades_data/cmp_b0030.bmp"],
-            ["facades_data/cmp_b0040.jpg", "facades_data/cmp_b0040.png", "facades_data/cmp_b0040.bmp"], 
-            ["facades_data/cmp_b0050.jpg", "facades_data/cmp_b0050.png", "facades_data/cmp_b0050.bmp"]]
+examples = [
+    [
+        "facades_data/cmp_b0010.jpg",
+        "facades_data/cmp_b0010.png",
+        "facades_data/cmp_b0010.bmp",
+    ],
+    [
+        "facades_data/cmp_b0020.jpg",
+        "facades_data/cmp_b0020.png",
+        "facades_data/cmp_b0020.bmp",
+    ],
+    [
+        "facades_data/cmp_b0030.jpg",
+        "facades_data/cmp_b0030.png",
+        "facades_data/cmp_b0030.bmp",
+    ],
+    [
+        "facades_data/cmp_b0040.jpg",
+        "facades_data/cmp_b0040.png",
+        "facades_data/cmp_b0040.bmp",
+    ],
+    [
+        "facades_data/cmp_b0050.jpg",
+        "facades_data/cmp_b0050.png",
+        "facades_data/cmp_b0050.bmp",
+    ],
+]
 
 title = "GauGAN For Conditional Image Generation"
 description = "Upload an Image or take one from examples to generate realistic images that are conditioned on cue images and segmentation maps"
 
 # Create interface with modern Gradio syntax
 demo = gr.Interface(
-    fn=predict, 
+    fn=predict,
     inputs=[ground_truth, segmentation, bitmap],
     outputs=gr.Image(type="numpy", label="Generated - Conditioned Images"),
     examples=examples,
-    allow_flagging=False,
+    flagging_mode="never",
     analytics_enabled=False,
     title=title,
     description=description,
-    article="<center>Space By: <u><a href='https://github.com/robotjellyzone'><b>Kavya Bisht</b></a></u> \n Based on <a href='https://keras.io/examples/generative/gaugan/'><b>this notebook</b></a></center>"
+    article="<center>Space By: <u><a href='https://github.com/robotjellyzone'><b>Kavya Bisht</b></a></u> \n Based on <a href='https://keras.io/examples/generative/gaugan/'><b>this notebook</b></a></center>",
 )
 
 if __name__ == "__main__":
-    demo.launch(enable_queue=True, debug=True)
+    demo.launch(enable_queue=True, debug=True)