Update app.py

app.py

@@ -5,11 +5,11 @@ import json
 
 def prediction(image):
 
-    # we first use the model fine-tuned on sroie (for now it is Theivaprakasham/layoutlmv3-finetuned-sroie)
-    # on the image, which gives us a JSON with some info and we blur the corresponding boxes
+    # first use the model fine-tuned on sroie (for now it is Theivaprakasham/layoutlmv3-finetuned-sroie)
+    # on the image, which returns a JSON with some info and an image with the corresponding boxes blurred
     j1, image_blurred = sroie_prediction(image)
     
-    # then we use the model fine-tuned on cord on the blurred image
+    # then use the model fine-tuned on cord on the blurred image
     img = image_blurred.copy()
     j2, image_final = cord_prediction(img)
 
@@ -24,13 +24,12 @@ def prediction(image):
 
 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 space uses two instances of the model, one fine-tuned on CORD and the other SROIE.\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. To use it, simply upload an image or use the example image below. Results will show up in a few seconds."
-examples = [['image.jpeg']['image.png']]
+examples = [['image.jpeg'],['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
+# gradio interface that takes in input an image and return a JSON file that contains its info
+# for now it shows also the intermediate steps
 iface = gr.Interface(fn=prediction,
                      inputs=gr.Image(type="pil"),
                      outputs=[gr.JSON(label="json parsing"),