Create app.py

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("microsoft/trocr-base-handwritten")
+# 2 cpu and 16gib ram
+def process_image(image):
+    pixel_values = processor(image, return_tensors="pt").pixel_values
+    generated_ids = model.generate(pixel_values)
+    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    return generated_text
+
+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"), 
+                     outputs=gr.outputs.Textbox(),
+                     title=title,
+                     description=description,
+                     article=article)
+iface.launch(debug=False)