fix url and app

app.py

@@ -5,15 +5,6 @@ from PIL import Image
 processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
 model = VisionEncoderDecoderModel.from_pretrained("quocanh944/tr-ocr")
 
-# load image examples from the IAM database
-urls = ['./images/a01-000u-00.png',
-        './images/a01-000x-04.png',
-        './images/a01-003-10.png']
-
-for idx, url in enumerate(urls):
-    image = Image.open(url)
-    image.save(f"image_{idx}.png")
-
 def process_image(image):
     # prepare image
     pixel_values = processor(image, return_tensors="pt").pixel_values
@@ -29,11 +20,11 @@ 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 = "TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models | Github Repo"
-examples =[["image_0.png"], ["image_1.png"], ["image_2.png"]]
+examples =[["./images/image_0.png"], ["./images/image_1.png"], ["./images/image_2.png"], ["./images/image_3.png"]]
 
 iface = gr.Interface(fn=process_image, 
-                     inputs=gr.inputs.Image(type="pil"), 
-                     outputs=gr.outputs.Textbox(),
+                     inputs=gr.Image(type="pil"), 
+                     outputs=gr.Textbox(),
                      title=title,
                      description=description,
                      article=article,