test fast api

app.py

@@ -17,24 +17,21 @@ import streamlit as st
 
 # pytesseract.pytesseract.tesseract_cmd = r’./Tesseract-OCR/tesseract.exe’
 choices = os.popen('tesseract --list-langs').read().split('\n')[1:-1]
-# 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!
-# """
+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)
+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)
+#st.write(output)
 
 # @app.post("/predict")
 # def predict(image_file: bytes = File(...), question: str = Form(...)):
@@ -48,6 +45,10 @@ st.write(output)
 #     output = pipe(image, question)
 #     return output
 
-# @app.get("/hello")
-# def read_root():
-#     return {"Hello": "World"}
+@app.get("/hello")
+def read_root():
+    image = 'https://templates.invoicehome.com/invoice-template-us-neat-750px.png'
+
+    question = "What is the invoice number?"
+    output = pipe(image, question)
+    return output