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import fitz
from fastapi import FastAPI, File, UploadFile, Form
from fastapi.responses import JSONResponse
from transformers import pipeline
from PIL import Image
from io import BytesIO
from starlette.middleware import Middleware
from starlette.middleware.cors import CORSMiddleware
from pdf2image import convert_from_bytes
app = FastAPI()
# Set up CORS middleware
origins = ["*"] # or specify your list of allowed origins
app.add_middleware(
CORSMiddleware,
allow_origins=origins,
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
nlp_qa = pipeline("document-question-answering", model="jinhybr/OCR-DocVQA-Donut")
nlp_qa_v2 = pipeline("document-question-answering", model="faisalraza/layoutlm-invoices")
nlp_qa_v3 = pipeline("question-answering", model="deepset/roberta-base-squad2")
description = """
## Image-based Document QA
This API performs document question answering using a LayoutLMv2-based model.
### Endpoints:
- **POST /uploadfile/:** Upload an image file to extract text and answer provided questions.
- **POST /pdfQA/:** Provide a PDF file to extract text and answer provided questions.
"""
app = FastAPI(docs_url="/", description=description)
@app.post("/uploadfile/", description="Upload an image file to extract text and answer provided questions.")
async def perform_document_qa(
file: UploadFile = File(...),
questions: str = Form(...),
):
try:
# Read the uploaded file as bytes
contents = await file.read()
# Open the image using PIL
image = Image.open(BytesIO(contents))
# Perform document question answering for each question using LayoutLMv2-based model
answers_dict = {}
for question in questions.split(','):
result = nlp_qa(
image,
question.strip()
)
# Access the 'answer' key from the first item in the result list
answer = result[0]['answer']
# Format the question as a string without extra characters
formatted_question = question.strip("[]")
answers_dict[formatted_question] = answer
return answers_dict
except Exception as e:
return JSONResponse(content=f"Error processing file: {str(e)}", status_code=500)
@app.post("/uploadfilev2/", description="Upload an image file to extract text and answer provided questions.")
async def perform_document_qa(
file: UploadFile = File(...),
questions: str = Form(...),
):
try:
# Read the uploaded file as bytes
contents = await file.read()
# Open the image using PIL
image = Image.open(BytesIO(contents))
# Perform document question answering for each question using LayoutLMv2-based model
answers_dict = {}
for question in questions.split(','):
result = nlp_qa_v2(
image,
question.strip()
)
# Access the 'answer' key from the first item in the result list
answer = result[0]['answer']
# Format the question as a string without extra characters
formatted_question = question.strip("[]")
answers_dict[formatted_question] = answer
return answers_dict
except Exception as e:
return JSONResponse(content=f"Error processing file: {str(e)}", status_code=500)
@app.post("/uploadfilev3/", description="Upload an image file to extract text and answer provided questions.")
async def perform_document_qa(
context: str = Form(...),
question: str = Form(...),
):
try:
QA_input = {
'question': question,
'context': context
}
res = nlp_qa_v3(QA_input)
return res['answer']
except Exception as e:
return JSONResponse(content=f"Error processing file: {str(e)}", status_code=500)
# Set up CORS middleware
origins = ["*"] # or specify your list of allowed origins
app.add_middleware(
CORSMiddleware,
allow_origins=origins,
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
) |