fast api and streamlit

app.py

@@ -1,18 +1,43 @@
+from base64 import b64decode, b64encode
+from io import BytesIO
+
+from fastapi import FastAPI, File, Form
+from PIL import Image
 from transformers import pipeline
 import streamlit as st
-import os
 
-print(os.popen(f'apt search tesseract').read())
 
-nlp = pipeline(
-    "document-question-answering",
-    model="impira/layoutlm-document-qa",
-)
+# 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!
+# """
+
+# 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")
+image = 'https://templates.invoicehome.com/invoice-template-us-neat-750px.png'
+
+question = "What is the invoice number?"
+output = pipe(image, question)
+
+st.write(output)
 
-nlp(
-    "https://templates.invoicehome.com/invoice-template-us-neat-750px.png",
-    "What is the invoice number?"
-)
+# @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
 
-st.title("Hello Document OCR")
-st.write("Vat amount")
+# @app.get("/hello")
+# def read_root():
+#     return {"Hello": "World"}

requirements.txt

@@ -1,7 +1,9 @@
 streamlit==0.84.1
-transformers
-Pillow
-pytesseract
-tesseract
-torch>=1.7.0
-torchvision>=0.8.1
+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