test

app.py

@@ -21,15 +21,15 @@ import torch
 from mmocr.apis import MMOCRInferencer
 ocr = MMOCRInferencer(det='TextSnake', rec='ABINet_Vision')
 
-# url = (
-#     "https://upload.wikimedia.org/wikipedia/commons/3/38/Adorable-animal-cat-20787.jpg"
-# )
-# path_input = "./cat.jpg"
-# urllib.request.urlretrieve(url, filename=path_input)
+url = (
+    "https://upload.wikimedia.org/wikipedia/commons/3/38/Adorable-animal-cat-20787.jpg"
+)
+path_input = "./cat.jpg"
+urllib.request.urlretrieve(url, filename=path_input)
 
-# url = "https://upload.wikimedia.org/wikipedia/commons/4/43/Cute_dog.jpg"
-# path_input = "./dog.jpg"
-# urllib.request.urlretrieve(url, filename=path_input)
+url = "https://upload.wikimedia.org/wikipedia/commons/4/43/Cute_dog.jpg"
+path_input = "./dog.jpg"
+urllib.request.urlretrieve(url, filename=path_input)
 
 # model = keras_model(weights="imagenet")
 
@@ -41,7 +41,8 @@ ocr = MMOCRInferencer(det='TextSnake', rec='ABINet_Vision')
 # )
 
 
-# def do_process(img, baseline):
+def do_process(img, baseline):
+    return img
 #     instance = image.img_to_array(img)
 #     instance = np.expand_dims(instance, axis=0)
 #     instance = preprocess_input(instance)
@@ -85,9 +86,9 @@ ocr = MMOCRInferencer(det='TextSnake', rec='ABINet_Vision')
 #     return img_res, img_flt, dctPreds
 
 
-# input_im = gr.inputs.Image(
-#     shape=(224, 224), image_mode="RGB", invert_colors=False, source="upload", type="pil"
-# )
+input_im = gr.inputs.Image(
+    shape=(224, 224), image_mode="RGB", invert_colors=False, source="upload", type="pil"
+)
 # input_drop = gr.inputs.Dropdown(
 #     label="Baseline (default: random)",
 #     choices=["random", "black", "white", "blur"],
@@ -95,24 +96,24 @@ ocr = MMOCRInferencer(det='TextSnake', rec='ABINet_Vision')
 #     type="value",
 # )
 
-# output_img = gr.outputs.Image(label="Output of Integrated Gradients", type="pil")
+output_img = gr.outputs.Image(label="Output of Integrated Gradients", type="pil")
 # output_base = gr.outputs.Image(label="Baseline image", type="pil")
 # output_label = gr.outputs.Label(label="Classification results", num_top_classes=3)
 
-# title = "XAI - Integrated gradients"
-# description = "Playground: Integrated gradients for a ResNet model trained on Imagenet dataset. Tools: Alibi, TF, Gradio."
-# examples = [["./cat.jpg", "blur"], ["./dog.jpg", "random"]]
-# article = "<p style='text-align: center'><a href='https://github.com/mawady' target='_blank'>By Dr. Mohamed Elawady</a></p>"
-# iface = gr.Interface(
-#     fn=do_process,
-#     inputs=[input_im, input_drop],
-#     outputs=[output_img, output_base, output_label],
-#     live=False,
-#     interpretation=None,
-#     title=title,
-#     description=description,
-#     article=article,
-#     examples=examples,
-# )
+title = "XAI - Integrated gradients"
+description = "Playground: Integrated gradients for a ResNet model trained on Imagenet dataset. Tools: Alibi, TF, Gradio."
+examples = [["./cat.jpg", "blur"], ["./dog.jpg", "random"]]
+article = "<p style='text-align: center'><a href='https://github.com/mawady' target='_blank'>By Dr. Mohamed Elawady</a></p>"
+iface = gr.Interface(
+    fn=do_process,
+    inputs=[input_im],
+    outputs=[output_img],
+    live=False,
+    interpretation=None,
+    title=title,
+    description=description,
+    article=article,
+    examples=examples,
+)
 
 # iface.launch(debug=True)