dog/fastapi-document-qa

1from base64 import b64decode, b64encode2from io import BytesIO3 4from fastapi import FastAPI, File, Form5from PIL import Image6from transformers import pipeline7 8 9description = """10## DocQA with 🤗 transformers, FastAPI, and Docker11 12This app shows how to do Document Question Answering using13FastAPI in a Docker Space 🚀14Check out the docs for the `/predict` endpoint below to try it out!15"""16 17# NOTE - we configure docs_url to serve the interactive Docs at the root path18# of the app. This way, we can use the docs as a landing page for the app on Spaces.19app = FastAPI(docs_url="/", description=description)20 21pipe = pipeline("document-question-answering", model="impira/layoutlm-document-qa")22 23 24@app.post("/predict")25def predict(image_file: bytes = File(...), question: str = Form(...)):26    """27    Using the document-question-answering pipeline from `transformers`, take28    a given input document (image) and a question about it, and return the29    predicted answer. The model used is available on the hub at:30    [`impira/layoutlm-document-qa`](https://huggingface.co/impira/layoutlm-document-qa).31    """32    image = Image.open(BytesIO(image_file))33    output = pipe(image, question)34    return output35