Update app.py

app.py

@@ -5,32 +5,20 @@ from PIL import Image
 
 processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
 model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten")
-
-# load image examples
-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']
-for idx, url in enumerate(urls):
-  image = Image.open(requests.get(url, stream=True).raw)
-  image.save(f"image_{idx}.png")
-
+# 2 cpu and 16gib ram
 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 a (single-text line) image or use one of the example images 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"]]
-
-#css = """.output_image, .input_image {height: 600px !important}"""
+title = "Transforme(encoder-decoder) based Text OCR"
+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.
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2109.10282'>Transformer Optical Character Recognition with Pre-trained Models</a> | <a href='https://github.com/microsoft/unilm/tree/master/trocr'>Github Repo</a></p>"
 
 iface = gr.Interface(fn=process_image, 
                      inputs=gr.inputs.Image(type="pil"),