Update app.py

app.py

@@ -5,14 +5,15 @@ import json
 
 def prediction(image):
 
-    #we first use mp-02/layoutlmv3-finetuned-cord on the image, which gives us a JSON with some info and a blurred 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
     j1, image_blurred = sroie_prediction(image)
     
-    #then we use the model fine-tuned on sroie (for now it is Theivaprakasham/layoutlmv3-finetuned-sroie)
+    # then we use the model fine-tuned on cord on the blurred image
     img = image_blurred.copy()
     j2, image_final = cord_prediction(img)
 
-    #we then link the two json files
+    # link the two json files
     if len(j1) == 0:
         j3 = j2
     else:
@@ -22,8 +23,8 @@ 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 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.jpeg']]
+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']]
 
 css = """.output_image, .input_image {height: 600px !important}"""