Update app.py

app.py

@@ -1,52 +1,52 @@
-from cord_inference import prediction as cord_prediction
-from sroie_inference import prediction as sroie_prediction
-import gradio as gr
-import json
-
-def prediction(image_path: str):
-
-    #we first use mp-02/layoutlmv3-finetuned-cord on the image, which gives us a JSON with some info and a blurred image
-    d, image = sroie_prediction(image_path)
-
-    #we save the blurred image in order to pass it to the other model
-    image_path_blurred = image_path.split('.')[0] + '_blurred.' + image_path.split('.')[1]
-    image.save(image_path_blurred)
-
-    #then we use the model fine-tuned on sroie (for now it is Theivaprakasham/layoutlmv3-finetuned-sroie)
-    d1, image1 = cord_prediction(image_path_blurred)
-
-    #we then link the two json files
-    if len(d) == 0:
-        k = d1
-    else:
-        k = json.dumps(d).split('}')[0] + ', ' + json.dumps(d1).split('{')[1]
-
-    return d, image, d1, image1, k
-
-# p,i,j = prediction("11990982-img.png")
-# print(p)
-
-
-title = "Interactive demo: LayoutLMv3 for receipts"
-description = "Demo for Microsoft's LayoutLMv3, a Transformer for state-of-the-art document image understanding tasks. This particular model is fine-tuned on CORD and SROIE, which are datasets of receipts.\n It firsts uses the fine-tune on SROIE to extract date, company and address, then the fine-tune on CORD for the other info.\n To use it, simply upload an image or use the example image below. Results will show up in a few seconds."
-examples = [['image.jpg']]
-
-css = """.output_image, .input_image {height: 600px !important}"""
-
-
-# we use a gradio interface that takes in input an image and return a JSON file that contains its info
-# we show also the intermediate steps (first we take some info with the model fine-tuned on SROIE and we blur the relative boxes
-# then we pass the image to the model fine-tuned on CORD
-iface = gr.Interface(fn=prediction,
-                     inputs=gr.Image(type="filepath"),
-                     outputs=[gr.JSON(label="json parsing"),
-                              gr.Image(type="pil", label="blurred image"),
-                              gr.JSON(label="json parsing"),
-                              gr.Image(type="pil", label="annotated image"),
-                              gr.JSON(label="json parsing")],
-                     title=title,
-                     description=description,
-                     examples=examples,
-                     css=css)
-
-iface.launch()
+from cord_inference import prediction as cord_prediction
+from sroie_inference import prediction as sroie_prediction
+import gradio as gr
+import json
+
+def prediction(image_path: str):
+
+    #we first use mp-02/layoutlmv3-finetuned-cord on the image, which gives us a JSON with some info and a blurred image
+    d, image = sroie_prediction(image_path)
+
+    #we save the blurred image in order to pass it to the other model
+    image_path_blurred = image_path.split('.')[0] + '_blurred.' + image_path.split('.')[1]
+    image.save(image_path_blurred)
+
+    #then we use the model fine-tuned on sroie (for now it is Theivaprakasham/layoutlmv3-finetuned-sroie)
+    d1, image1 = cord_prediction(image_path_blurred)
+
+    #we then link the two json files
+    if len(d) == 0:
+        k = d1
+    else:
+        k = json.dumps(d).split('}')[0] + ', ' + json.dumps(d1).split('{')[1]
+
+    return d, image, d1, image1, k
+
+# p,i,j = prediction("11990982-img.png")
+# print(p)
+
+
+title = "Interactive demo: LayoutLMv3 for receipts"
+description = "Demo for Microsoft's LayoutLMv3, a Transformer for state-of-the-art document image understanding tasks. This particular model is fine-tuned on CORD and SROIE, which are datasets of receipts.\n It firsts uses the fine-tune on SROIE to extract date, company and address, then the fine-tune on CORD for the other info.\n To use it, simply upload an image or use the example image below. Results will show up in a few seconds."
+examples = [['image.png']]
+
+css = """.output_image, .input_image {height: 600px !important}"""
+
+
+# we use a gradio interface that takes in input an image and return a JSON file that contains its info
+# we show also the intermediate steps (first we take some info with the model fine-tuned on SROIE and we blur the relative boxes
+# then we pass the image to the model fine-tuned on CORD
+iface = gr.Interface(fn=prediction,
+                     inputs=gr.Image(type="filepath"),
+                     outputs=[gr.JSON(label="json parsing"),
+                              gr.Image(type="pil", label="blurred image"),
+                              gr.JSON(label="json parsing"),
+                              gr.Image(type="pil", label="annotated image"),
+                              gr.JSON(label="json parsing")],
+                     title=title,
+                     description=description,
+                     examples=examples,
+                     css=css)
+
+iface.launch()