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README.md
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title: LaTeX OCR Demo
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sdk: gradio
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---
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title: LaTeX OCR Demo
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emoji: πβοΈβ π’
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sdk: gradio
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app.py
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import os
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import PIL
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import gradio as gr
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from PIL import Image
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os.system("pip install pix2tex")
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os.system("pip install opencv-python-headless==4.1.2.30")
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# Load model
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from pix2tex import cli as pix2tex
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model = pix2tex.LatexOCR()
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def inference(input_image_path):
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img = Image.open(input_image_path)
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output = model(img)
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print("Model output:",output)
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return output
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# Front end
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title = "Convert images of equations into LaTeX code πβοΈβ π’"
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description = "<div> Did you come across a complex mathematical expression that you want to refer to in your report/thesis? Is your freemium over at <a href='https://mathpix.com/' target='_blank'>Mathpix</a>? π« <br><br> Take a screenshot of the equation and use this application to convert it into LaTeX code. π To use it, simply upload your screenshot/equation image, or click one of the examples to load them. To verify the results, copy & paste the output in <a href='https://quicklatex.com/' target='_blank'>Quick LaTeX</a>. Read more at the links below.</div>"
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article = "<p style='text-align: center'><a href='https://lukas-blecher.github.io/LaTeX-OCR/' target='_blank'>pix2tex: Using a ViT to convert images of equations into LaTeX code</a> | <a href='https://github.com/lukas-blecher/LaTeX-OCR' target='_blank'>Github</a></p>"
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# UI
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gr.Interface(
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inference,
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title=title,
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description=description,
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article=article,
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inputs=gr.inputs.Image(type="filepath", label="Input: Image of your equation you want to covert."),
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outputs=gr.outputs.Textbox(type="text", label="Output: Converted LaTeX code."),
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examples=["./eqn1.png", "./eqn2.png", "./eqn3.png"],
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allow_flagging="never",
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analytics_enabled=False,
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).launch()
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eqn1.png
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eqn2.png
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eqn3.png
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requirements.txt
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torch
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torchvision
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Pillow
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