Update app.py

app.py

@@ -26,8 +26,7 @@ craft = Craft(output_dir=None,
 
 
 # load image examples from the IAM database
-urls = ['https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg', 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSoolxi9yWGAT5SLZShv8vVd0bz47UWRzQC19fDTeE8GmGv_Rn-PCF1pP1rrUx8kOjA4gg&usqp=CAU',
-        'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRNYtTuSBpZPV_nkBYPMFwVVD9asZOPgHww4epu9EqWgDmXW--sE2o8og40ZfDGo87j5w&usqp=CAU']
+urls = ['https://cdn.shopify.com/s/files/1/0275/6457/2777/files/Penwritten_2048x.jpg']
 for idx, url in enumerate(urls):
   image = Image.open(requests.get(url, stream=True).raw)
   image.save(f"image_{idx}.png")
@@ -63,7 +62,7 @@ def process_image(image):
 title = "Interactive demo: TrOCR"
 description = "Demo for Microsoft's TrOCR, an encoder-decoder model consisting of an image Transformer encoder and a text Transformer decoder for state-of-the-art optical character recognition (OCR) on single-text line images. This particular model is fine-tuned on IAM, a dataset of annotated handwritten images. To use it, simply upload an image or use the example image below and click 'submit'. Results will show up in a few seconds."
 article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2109.10282'>TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models</a> | <a href='https://github.com/microsoft/unilm/tree/master/trocr'>Github Repo</a></p>"
-examples =[["image_0.png"], ["image_1.png"], ["image_2.png"]]
+examples =[["image_0.png"]]
 
 iface = gr.Interface(fn=process_image, 
                      inputs=gr.inputs.Image(type="pil"),