init

app.py

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+# Adapted from https://github.com/kingyiusuen/image-to-latex/blob/main/api/app.py
+
+from http import HTTPStatus
+from fastapi import FastAPI, File, UploadFile, Form
+from PIL import Image
+from io import BytesIO
+from pix2tex.cli import LatexOCR
+
+model = None
+app = FastAPI(title='pix2tex API')
+
+
+def read_imagefile(file) -> Image.Image:
+    image = Image.open(BytesIO(file))
+    return image
+
+
+@app.on_event('startup')
+async def load_model():
+    global model
+    if model is None:
+        model = LatexOCR()
+
+
+@app.get('/')
+def root():
+    '''Health check.'''
+    response = {
+        'message': HTTPStatus.OK.phrase,
+        'status-code': HTTPStatus.OK,
+        'data': {},
+    }
+    return response
+
+
+@app.post('/predict/')
+async def predict(file: UploadFile = File(...)) -> str:
+    """Predict the Latex code from an image file.
+
+    Args:
+        file (UploadFile, optional): Image to predict. Defaults to File(...).
+
+    Returns:
+        str: Latex prediction
+    """
+    global model
+    image = Image.open(file.file)
+    return model(image)
+
+
+@app.post('/bytes/')
+async def predict_from_bytes(file: bytes = File(...)) -> str:  # , size: str = Form(...)
+    """Predict the Latex code from a byte array
+
+    Args:
+        file (bytes, optional): Image as byte array. Defaults to File(...).
+
+    Returns:
+        str: Latex prediction
+    """
+    global model
+    #size = tuple(int(a) for a in size.split(','))
+    image = Image.open(BytesIO(file))
+    return model(image, resize=False)

requirements.txt

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+pix2tex[api]

run.py

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+from multiprocessing import Process
+import subprocess
+import os
+
+
+def start_api(path='.'):
+    subprocess.call(['uvicorn', 'app:app', '--port', '8502'], cwd=path)
+
+
+def start_frontend(path='.'):
+    subprocess.call(['streamlit', 'run', 'streamlit.py'], cwd=path)
+
+
+if __name__ == '__main__':
+    path = os.path.realpath(os.path.dirname(__file__))
+    api = Process(target=start_api, kwargs={'path': path})
+    api.start()
+    frontend = Process(target=start_frontend, kwargs={'path': path})
+    frontend.start()
+    api.join()
+    frontend.join()

streamlit.py

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+import requests
+from PIL import Image
+import streamlit
+
+if __name__ == '__main__':
+    streamlit.set_page_config(page_title='LaTeX-OCR')
+    streamlit.title('LaTeX OCR')
+    streamlit.markdown('Convert images of equations to corresponding LaTeX code.\n\nThis is based on the `pix2tex` module. Check it out [![github](https://img.shields.io/badge/LaTeX--OCR-visit-a?style=social&logo=github)](https://github.com/lukas-blecher/LaTeX-OCR)')
+
+    uploaded_file = streamlit.file_uploader(
+        'Upload an image an equation',
+        type=['png', 'jpg'],
+    )
+
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        streamlit.image(image)
+    else:
+        streamlit.text('\n')
+
+    if streamlit.button('Convert'):
+        if uploaded_file is not None and image is not None:
+            with streamlit.spinner('Computing'):
+                response = requests.post('http://127.0.0.1:8502/predict/', files={'file': uploaded_file.getvalue()})
+            if response.ok:
+                latex_code = response.json()
+                streamlit.code(latex_code, language='latex')
+                streamlit.markdown(f'$\\displaystyle {latex_code}$')
+            else:
+                streamlit.error(response.text)
+        else:
+            streamlit.error('Please upload an image.')