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Dockerfile

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+# Use the official Python 3.9 image
+FROM python:3.9
+
+RUN apt-get update && apt-get install -y \
+  tesseract-ocr-all \
+  && rm -rf /var/lib/apt/lists/*
+
+# Set the working directory to /code
+WORKDIR /code
+
+# Copy the current directory contents into the container at /code
+COPY ./requirements.txt /code/requirements.txt
+
+# Install requirements.txt 
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# Set up a new user named "user" with user ID 1000
+RUN useradd -m -u 1000 user
+# Switch to the "user" user
+USER user
+# Set home to the user's home directory
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+# Set the working directory to the user's home directory
+WORKDIR $HOME/app
+
+# Copy the current directory contents into the container at $HOME/app setting the owner to the user
+COPY --chown=user . $HOME/app
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

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+from base64 import b64decode, b64encode
+from io import BytesIO
+
+from fastapi import FastAPI, File, Form
+from PIL import Image
+from transformers import pipeline
+
+
+description = """
+## DocQA with 🤗 transformers, FastAPI, and Docker
+
+This app shows how to do Document Question Answering using
+FastAPI in a Docker Space 🚀
+Check out the docs for the `/predict` endpoint below to try it out!
+
+## Usage
+
+#### Python
+
+```python
+import requests
+
+url = "https://dog-fastapi-document-qa.hf.space/predict"
+r = requests.get(
+    url,
+    files={"image_file": open("invoice.png", "rb")},
+    data={"question": "What is the invoice number?"}
+)
+print(r.json())
+```
+
+#### Curl
+
+```bash
+curl -X 'POST' \
+  'https://dog-fastapi-document-qa.hf.space/predict' \
+  -H 'accept: application/json' \
+  -H 'Content-Type: multipart/form-data' \
+  -F 'image_file=@invoice.png;type=image/png' \
+  -F 'question=What is the invoice number?'
+```
+"""
+
+# NOTE - we configure docs_url to serve the interactive Docs at the root path
+# of the app. This way, we can use the docs as a landing page for the app on Spaces.
+app = FastAPI(docs_url="/", description=description)
+
+pipe = pipeline("document-question-answering", model="impira/layoutlm-document-qa")
+
+
+@app.post("/predict")
+def predict(image_file: bytes = File(...), question: str = Form(...)):
+    """
+    Using the document-question-answering pipeline from `transformers`, take
+    a given input document (image) and a question about it, and return the
+    predicted answer. The model used is available on the hub at:
+    [`impira/layoutlm-document-qa`](https://huggingface.co/impira/layoutlm-document-qa).
+    """
+    image = Image.open(BytesIO(image_file))
+    output = pipe(image, question)
+    return output

requirements.txt

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+fastapi==0.74.*
+requests==2.27.*
+uvicorn[standard]==0.17.*
+sentencepiece==0.1.*
+torch==1.11.*
+transformers[vision]==4.*
+pytesseract==0.3.10
+python-multipart==0.0.6