main.py

from config import Settings
from preprocess import Preprocessor
import ocr
from PIL import Image
from transformers import LiltForTokenClassification, AutoTokenizer
import token_classification
import torch
from fastapi import FastAPI, UploadFile, Form, HTTPException
from contextlib import asynccontextmanager
import json
import io
from models import LiLTRobertaLikeForRelationExtraction
from base64 import b64decode 
from handwritting_detection import DetectHandwritting
import pandas as pd
config = {}

@asynccontextmanager
async def lifespan(app: FastAPI):
    settings = Settings()
    config['settings'] = settings
    config['device'] = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    
    config['processor'] = Preprocessor(settings.TOKENIZER)
    config['tokenizer'] = AutoTokenizer.from_pretrained(settings.TOKENIZER)
    config['ser_model'] = LiltForTokenClassification.from_pretrained(settings.SER_MODEL)
    config['re_model'] = LiLTRobertaLikeForRelationExtraction.from_pretrained(settings.RE_MODEL)
    config['TROCR_API'] = settings.TROCR_API_URL
    yield
    # Clean up and release the resources
    config.clear()

app = FastAPI(lifespan=lifespan)

@app.get("/")
def api_home():
    return {'detail': 'Welcome to Sri-Doc space'}

@app.post("/submit-doc")
async def ProcessDocument(file: UploadFile):
  content = await file.read()
  ocr_df, image = ApplyOCR(content)
  if len(ocr_df) < 2:
    raise HTTPException(status_code=400, detail="Cannot apply OCR to the image")
  try:
    tokenClassificationOutput, img_size = LabelTokens(ocr_df, image)
  except Exception as e:
    print(e)
    raise HTTPException(status_code=400, detail="Entity identification failed")
  
  try:
   reOutput = ExtractRelations(tokenClassificationOutput, ocr_df, img_size)
  except Exception as e:
    print(e)
    raise HTTPException(status_code=400, detail="Relation extraction failed")
  return reOutput

@app.post("/submit-doc-base64")
async def ProcessDocument(file: str = Form(...)):
  try:
    head, file = file.split(',')
    str_as_bytes = str.encode(file)
    content = b64decode(str_as_bytes)
  except:
    raise HTTPException(status_code=400, detail="Invalid image")
  ocr_df, image = ApplyOCR(content)
  if len(ocr_df) < 2:
    raise HTTPException(status_code=400, detail="Cannot apply OCR to the image")
  try:
    tokenClassificationOutput, img_size = LabelTokens(ocr_df, image)
  except Exception as e:
    print(e)
    raise HTTPException(status_code=400, detail="Entity identification failed")
  try:
    reOutput = ExtractRelations(tokenClassificationOutput, ocr_df, img_size)
  except Exception as e:
    print(e)
    raise HTTPException(status_code=400, detail="Relation extraction failed")
  return reOutput

def ApplyOCR(content):
  try:
    image = Image.open(io.BytesIO(content))
  except:
    raise HTTPException(status_code=400, detail="Invalid image")

  try:
    printed_img, handwritten_imgs = DetectHandwritting(image)
  except:
    raise HTTPException(status_code=400, detail="Handwritting detection failed")

  try:
    jpeg_bytes = io.BytesIO()
    printed_img.save(jpeg_bytes, format='PNG')
    # printed_img.save('temp/printed_text_image.jpeg', format='PNG')
    printed_content = jpeg_bytes.getvalue()
    vision_client = ocr.VisionClient(config['settings'].GCV_AUTH)
    printed_ocr_df = vision_client.ocr(printed_content, printed_img)
    # printed_ocr_df.to_csv('temp/complete_image_ocr.csv', index=False)
    # return  printed_ocr_df, image
  except Exception as e:
    print(e)
    raise HTTPException(status_code=400, detail="Printed OCR process failed")
  
  try:
    trocr_client = ocr.TrOCRClient(config['settings'].TROCR_API_URL)
    handwritten_ocr_df = trocr_client.ocr(handwritten_imgs, image)
  except Exception as e:
    print(e)
    raise HTTPException(status_code=400, detail="handwritten OCR process failed")

  ocr_df = pd.concat([handwritten_ocr_df, printed_ocr_df])
  return ocr_df, image


def LabelTokens(ocr_df, image):
  input_ids, attention_mask, token_type_ids, bbox, token_actual_boxes, offset_mapping = config['processor'].process(ocr_df, image = image)
  token_labels = token_classification.classifyTokens(config['ser_model'], input_ids, attention_mask, bbox, offset_mapping)
  return {"token_labels": token_labels, "input_ids": input_ids, "bbox":bbox, "attention_mask":attention_mask}, image.size

def ExtractRelations(tokenClassificationOutput, ocr_df, img_size):
  token_labels = tokenClassificationOutput['token_labels']
  input_ids = tokenClassificationOutput['input_ids']
  attention_mask = tokenClassificationOutput["attention_mask"]
  bbox_org = tokenClassificationOutput["bbox"]

  merged_output, merged_words = token_classification.createEntities(config['ser_model'], token_labels, input_ids, ocr_df, config['tokenizer'], img_size, bbox_org)

  entities = merged_output['entities']
  input_ids = torch.tensor([merged_output['input_ids']]).to(config['device'])
  bbox = torch.tensor([merged_output['bbox']]).to(config['device'])
  attention_mask = torch.tensor([merged_output['attention_mask']]).to(config['device'])

  id2label = {"HEADER":0, "QUESTION":1, "ANSWER":2}
  decoded_entities = []
  for entity in entities:
    decoded_entities.append((entity['label'], config['tokenizer'].decode(input_ids[0][entity['start']:entity['end']])))
    entity['label'] = id2label[entity['label']]

  config['re_model'].to(config['device'])
  entity_dict = {'start': [entity['start'] for entity in entities], 'end': [entity['end'] for entity in entities], 'label': [entity['label'] for entity in entities]}
  relations = [{'start_index': [], 'end_index': [], 'head': [], 'tail': []}]
  with torch.no_grad():
    outputs = config['re_model'](input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, entities=[entity_dict], relations=relations)

  decoded_pred_relations = []
  for relation in outputs.pred_relations[0]:
    head_start, head_end = relation['head']
    tail_start, tail_end = relation['tail']
    question =  config['tokenizer'].decode(input_ids[0][head_start:head_end])
    answer = config['tokenizer'].decode(input_ids[0][tail_start:tail_end])
    decoded_pred_relations.append((question, answer))

  return {"pred_relations":json.dumps(outputs.pred_relations[0]), "entities":json.dumps(entities), "input_ids": json.dumps(input_ids.tolist()), "bboxes": json.dumps(bbox_org.tolist()),"token_labels":json.dumps(token_labels), "decoded_entities": json.dumps(decoded_entities), "decoded_pred_relations":json.dumps(decoded_pred_relations)}