Add application file

app.py

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+import gradio as gr
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel
+import requests
+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
+
+    # generate (no beam search)
+    generated_ids = model.generate(pixel_values)
+
+    # decode
+    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+    return generated_text
+
+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"]]
+
+iface = gr.Interface(fn=process_image, 
+                     inputs=gr.inputs.Image(type="pil"), 
+                     outputs=gr.outputs.Textbox(),
+                     title=title,
+                     description=description,
+                     article=article,
+                     examples=examples)
+
+iface.launch()