888 lines
33 KiB
Python
888 lines
33 KiB
Python
from fastapi import FastAPI, UploadFile, HTTPException, Body
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from PIL import Image
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import pytesseract
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from doctr.models import ocr_predictor
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from doctr.io import DocumentFile
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from PyPDF2 import PdfReader
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import pdfplumber
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import camelot
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import spacy
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import logging
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import io
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from logging.handlers import RotatingFileHandler
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import re
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from datetime import datetime
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from io import BytesIO
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import requests
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LOG_PATH = "/var/log/automation-service.log"
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file_handler = RotatingFileHandler(
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LOG_PATH,
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maxBytes=10*1024*1024,
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backupCount=5,
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encoding="utf-8"
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)
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file_handler.setFormatter(logging.Formatter(
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"%(asctime)s - %(levelname)s - %(name)s - %(message)s"
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))
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class AHKParser:
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lab = "AHK"
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def _clean_value(self, value):
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"""Nettoie la valeur en supprimant les espaces inutiles"""
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if value:
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return value.strip()
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return value
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def parse(self, text):
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"""Parse le texte et retourne un dictionnaire structuré"""
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result = {
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"lab": self.lab,
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"report": self._extract_report_info(text),
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"contract": self._extract_contract_info(text),
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"parties": self._extract_parties_info(text),
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"shipment": self._extract_shipment_info(text),
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"weights": self._extract_weights_info(text)
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}
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self.data = result
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return result
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def _extract_report_info(self, text):
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"""Extrait les informations du rapport"""
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report_info = {
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"reference": None,
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"file_no": None,
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"date": None
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}
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# Recherche de la référence client - plus précise
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ref_match = re.search(r'Client\s+Reference:\s*(S-\d+\s*/\s*INV\s*\d+)', text)
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if ref_match:
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report_info["reference"] = self._clean_value(ref_match.group(1))
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# Recherche du numéro de fichier AHK
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file_no_match = re.search(r'AHK\s+S/([\w/]+)', text)
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if file_no_match:
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report_info["file_no"] = self._clean_value(file_no_match.group(1))
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# Recherche de la date du rapport
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date_match = re.search(r'Signed\s+on\s*(\d{1,2}-[A-Za-z]{3}-\d{4})', text)
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if date_match:
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report_info["date"] = self._clean_value(date_match.group(1))
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return report_info
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def _extract_contract_info(self, text):
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"""Extrait les informations du contrat"""
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contract_info = {
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"contract_no": None,
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"invoice_no": None,
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"lc_no": None,
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"origin": None,
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"commodity": None
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}
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# Extraction de la référence client
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ref_match = re.search(r'Client\s+Ref\s+No\.\s*:\s*([^\n]+)', text)
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if ref_match:
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ref_text = ref_match.group(1).strip()
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# Sépare S-3488 et INV 4013
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parts = re.split(r'[/\s]+', ref_text)
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for part in parts:
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if part.startswith('S-'):
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contract_info["contract_no"] = part.strip()
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elif part.startswith('INV'):
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contract_info["invoice_no"] = part.strip()
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# Extraction de l'origine et de la marchandise - regex plus précise
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growth_match = re.search(r'Growth\s*:\s*([A-Z\s]+?)(?=\s*(?:Vessel|$))', text)
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if growth_match:
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origin_text = growth_match.group(1).strip()
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if "AUSTRALIAN" in origin_text.upper():
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contract_info["origin"] = "AUSTRALIA"
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contract_info["commodity"] = "RAW COTTON"
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return contract_info
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def _extract_parties_info(self, text):
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"""Extrait les informations sur les parties"""
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parties_info = {
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"seller": None,
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"buyer": None,
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"carrier": None
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}
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# Extraction du vendeur (Client) - regex plus précise
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seller_match = re.search(r'Client\s*:\s*([^\n:]+?)(?=\s*(?:Client Ref|Buyer|$))', text)
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if seller_match:
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parties_info["seller"] = self._clean_value(seller_match.group(1))
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# Extraction de l'acheteur (Buyer) - regex plus précise
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buyer_match = re.search(r'Buyer\s*:\s*([^\n:]+?)(?=\s*(?:Total Bales|$))', text)
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if buyer_match:
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parties_info["buyer"] = self._clean_value(buyer_match.group(1))
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# Extraction du transporteur (nom du navire seulement)
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vessel_match = re.search(r'Vessel\s*:\s*([A-Z\s]+?)(?=\s*(?:Arrival|Voy|$))', text)
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if vessel_match:
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parties_info["carrier"] = self._clean_value(vessel_match.group(1))
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return parties_info
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def _extract_shipment_info(self, text):
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"""Extrait les informations d'expédition"""
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shipment_info = {
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"vessel": None,
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"bl_no": None,
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"bl_date": None,
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"port_loading": None,
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"port_destination": None,
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"arrival_date": None,
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"weighing_place": None,
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"weighing_method": None,
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"bales": None
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}
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# Extraction du navire (nom seulement)
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vessel_match = re.search(r'Vessel\s*:\s*([A-Z\s]+?)(?=\s*(?:Arrival|Voy|$))', text)
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if vessel_match:
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shipment_info["vessel"] = self._clean_value(vessel_match.group(1))
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# Extraction du numéro de connaissement (seulement le numéro)
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bl_no_match = re.search(r'B/L\s+No\.\s*:\s*([A-Z0-9]+)(?=\s|$)', text)
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if bl_no_match:
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shipment_info["bl_no"] = self._clean_value(bl_no_match.group(1))
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# Extraction de la date du connaissement
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bl_date_match = re.search(r'B/L\s+Date\s*:\s*(\d{1,2}-[A-Za-z]{3}-\d{4})(?=\s|$)', text)
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if bl_date_match:
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shipment_info["bl_date"] = self._clean_value(bl_date_match.group(1))
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# Extraction du port de destination (sans le "Tare")
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dest_match = re.search(r'Destination\s*:\s*([A-Z,\s]+?)(?=\s*(?:Tare|$))', text)
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if dest_match:
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shipment_info["port_destination"] = self._clean_value(dest_match.group(1))
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# Extraction de la date d'arrivée
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arrival_match = re.search(r'Arrival\s+Date\s*:\s*(\d{1,2}-[A-Za-z]{3}-\d{4})(?=\s|$)', text)
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if arrival_match:
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shipment_info["arrival_date"] = self._clean_value(arrival_match.group(1))
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# Extraction de la méthode de pesée
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weighing_method_match = re.search(r'Weighing\s+method\s*:\s*([^\n]+?)(?=\s*(?:Tare|$))', text)
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if weighing_method_match:
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shipment_info["weighing_method"] = self._clean_value(weighing_method_match.group(1))
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# Extraction du nombre de balles
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bales_match = re.search(r'Total\s+Bales\s*:\s*(\d+)(?=\s|$)', text)
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if bales_match:
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try:
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shipment_info["bales"] = int(bales_match.group(1).strip())
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except ValueError:
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shipment_info["bales"] = None
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return shipment_info
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def _extract_weights_info(self, text):
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"""Extrait les informations de poids"""
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weights_info = {
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"gross_landed_kg": None,
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"tare_kg": None,
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"net_landed_kg": None,
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"invoice_net_kg": None,
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"gain_loss_kg": None,
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"gain_loss_percent": None
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}
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# Extraction du poids brut débarqué (corrigé - doit être 100580 kg)
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gross_landed_match = re.search(r'LANDED WEIGHTS[\s\S]*?Gross\s*:\s*([\d.,]+)\s*kg', text)
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if gross_landed_match:
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try:
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weights_info["gross_landed_kg"] = float(gross_landed_match.group(1).replace(',', '').strip())
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except ValueError:
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pass
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# Extraction du poids de tare
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tare_match = re.search(r'Tare\s*:\s*([\d.,]+)\s*kg', text)
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if tare_match:
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try:
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weights_info["tare_kg"] = float(tare_match.group(1).replace(',', '').strip())
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except ValueError:
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pass
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# Extraction du poids net débarqué (corrigé - doit être 100078.40 kg)
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net_landed_match = re.search(r'LANDED WEIGHTS[\s\S]*?Net\s*:\s*([\d.,]+)\s*kg', text)
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if net_landed_match:
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try:
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weights_info["net_landed_kg"] = float(net_landed_match.group(1).replace(',', '').strip())
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except ValueError:
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pass
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# Extraction du poids net facturé (101299 kg)
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invoice_net_match = re.search(r'INVOICE WEIGHTS[\s\S]*?Net\s*:\s*([\d.,]+)\s*kg', text)
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if invoice_net_match:
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try:
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weights_info["invoice_net_kg"] = float(invoice_net_match.group(1).replace(',', '').strip())
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except ValueError:
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pass
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# Extraction de la perte en kg
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loss_match = re.search(r'LOSS\s*:\s*-\s*([\d.,]+)\s*kg', text)
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if loss_match:
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try:
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weights_info["gain_loss_kg"] = -float(loss_match.group(1).replace(',', '').strip())
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except ValueError:
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pass
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# Extraction du pourcentage de perte
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percent_match = re.search(r'Percentage\s*:\s*-\s*([\d.,]+)%', text)
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if percent_match:
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try:
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weights_info["gain_loss_percent"] = -float(percent_match.group(1).replace(',', '').strip())
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except ValueError:
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pass
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return weights_info
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import re
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class IntertekParser:
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lab = "Intertek"
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def _clean_value(self, value):
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"""Nettoie la valeur en supprimant les espaces inutiles"""
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if value:
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return value.strip()
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return value
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def _extract_number(self, text, pattern, is_int=False):
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"""Extrait un nombre (int ou float) du texte selon un pattern regex"""
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match = re.search(pattern, text)
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if match:
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try:
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# Nettoie la chaîne numérique
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num_str = match.group(1).replace(',', '').replace(' ', '').strip()
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if is_int:
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return int(num_str)
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else:
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return float(num_str)
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except (ValueError, AttributeError):
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return None
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return None
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def parse(self, text):
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"""Parse le texte et retourne un dictionnaire structuré"""
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result = {
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"lab": self.lab,
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"report": self._extract_report_info(text),
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"contract": self._extract_contract_info(text),
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"parties": self._extract_parties_info(text),
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"shipment": self._extract_shipment_info(text),
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"weights": self._extract_weights_info(text)
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}
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return result
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def _extract_report_info(self, text):
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"""Extrait les informations du rapport"""
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report_info = {
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"reference": None,
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"file_no": None,
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"date": None
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}
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# Recherche de la référence globale
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ref_match = re.search(r'Global Ref\s*:\s*(GLO-\d+-[A-Z]+)', text)
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if ref_match:
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report_info["reference"] = self._clean_value(ref_match.group(1))
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# Recherche du numéro de fichier
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file_no_match = re.search(r'Report\s*/\s*File No\s*:\s*([A-Z]+-AGR\d+-?)', text)
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if file_no_match:
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report_info["file_no"] = self._clean_value(file_no_match.group(1))
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# Recherche de la date du rapport
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date_match = re.search(r'Dated\s*:\s*(\d{1,2}\.\d{1,2}\.\d{4})', text)
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if date_match:
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report_info["date"] = self._clean_value(date_match.group(1))
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return report_info
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def _extract_contract_info(self, text):
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"""Extrait les informations du contrat"""
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contract_info = {
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"contract_no": None,
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"invoice_no": None,
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"lc_no": None, # Non présent dans ce rapport
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"origin": None,
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"commodity": None
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}
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# Extraction du numéro de contrat
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contract_match = re.search(r'Contract No\s*:\s*([A-Z]?-\d+)', text)
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if contract_match:
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contract_info["contract_no"] = self._clean_value(contract_match.group(1))
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# Extraction du numéro de facture
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invoice_match = re.search(r'Invoice No\s*:\s*(\d+)', text)
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if invoice_match:
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contract_info["invoice_no"] = self._clean_value(invoice_match.group(1))
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# Extraction de l'origine et de la marchandise
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growth_match = re.search(r'Growth\s*:\s*([A-Z\s]+)(?=\s*Shipper|\n|$)', text)
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if growth_match:
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origin_text = growth_match.group(1).strip()
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if "GREECE" in origin_text.upper():
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contract_info["origin"] = "GREECE"
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contract_info["commodity"] = "RAW COTTON"
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return contract_info
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def _extract_parties_info(self, text):
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"""Extrait les informations sur les parties"""
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parties_info = {
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"seller": None,
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"buyer": None,
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"carrier": None
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}
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# Extraction du vendeur (Shipper)
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seller_match = re.search(r'Shipper\s*:\s*([^\n]+?)(?=\s*(?:Buyer|$))', text)
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if seller_match:
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parties_info["seller"] = self._clean_value(seller_match.group(1))
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# Extraction de l'acheteur (Buyer)
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buyer_match = re.search(r'Buyer\s*:\s*([^\n]+?)(?=\s*(?:CONTAINER|TOTAL|$))', text)
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if buyer_match:
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parties_info["buyer"] = self._clean_value(buyer_match.group(1))
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# Extraction du transporteur (nom du navire seulement)
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vessel_match = re.search(r'Vessel\s*:\s*([A-Z\s]+?)(?=\s*(?:Arrival|$))', text)
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if vessel_match:
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parties_info["carrier"] = self._clean_value(vessel_match.group(1))
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return parties_info
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def _extract_shipment_info(self, text):
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"""Extrait les informations d'expédition"""
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shipment_info = {
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"vessel": None,
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"bl_no": None,
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"bl_date": None, # Non présent dans ce rapport
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"port_loading": None, # Non présent dans ce rapport
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"port_destination": None, # Non présent dans ce rapport
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"arrival_date": None,
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"weighing_place": None,
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"weighing_method": None,
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"bales": None
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}
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# Extraction du navire
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vessel_match = re.search(r'Vessel\s*:\s*([A-Z\s]+?)(?=\s*(?:Arrival|$))', text)
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if vessel_match:
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shipment_info["vessel"] = self._clean_value(vessel_match.group(1))
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# Extraction du numéro de connaissement
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bl_no_match = re.search(r'B/L\s+No\.\s*:\s*([A-Z0-9]+)', text)
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if bl_no_match:
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shipment_info["bl_no"] = self._clean_value(bl_no_match.group(1))
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# Extraction de la date d'arrivée
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arrival_match = re.search(r'Arrival Date\s*:\s*(\d{1,2}\.\d{1,2}\.\d{4})', text)
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if arrival_match:
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shipment_info["arrival_date"] = self._clean_value(arrival_match.group(1))
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# Extraction du lieu de pesée
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weighing_place_match = re.search(r'Weighed at\s*:\s*([^\n]+?)(?=\s*(?:Vessel|$))', text)
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if weighing_place_match:
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shipment_info["weighing_place"] = self._clean_value(weighing_place_match.group(1))
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# Extraction de la méthode de pesée
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# Recherche dans les remarques
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remarks_section = re.search(r'REMARKS\s*(.+?)(?=ISSUED BY|$)', text, re.DOTALL | re.IGNORECASE)
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if remarks_section:
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remarks_text = remarks_section.group(1)
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if "weighbridge" in remarks_text.lower():
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shipment_info["weighing_method"] = "Weighbridge weighing by empty/full truck"
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# Extraction du nombre de balles (à partir du total)
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bales_match = re.search(r'TOTAL\s+(\d{1,4}(?:,\d{3})?)\s+[\d,]+\.\d{2}', text)
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if not bales_match:
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# Essayons une autre approche
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bales_match = re.search(r'Invoice Quantity\s*:\s*(\d+)\s+Bales', text)
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if bales_match:
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try:
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bales_str = bales_match.group(1).replace(',', '').strip()
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shipment_info["bales"] = int(bales_str)
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except ValueError:
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shipment_info["bales"] = None
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return shipment_info
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def _extract_weights_info(self, text):
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"""Extrait les informations de poids"""
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weights_info = {
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"gross_landed_kg": None,
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"tare_kg": None,
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"net_landed_kg": None,
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"invoice_net_kg": None,
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"gain_loss_kg": None,
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"gain_loss_percent": None
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}
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# Extraction du poids brut débarqué
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gross_match = re.search(r'Gross Landed Weight\s*:\s*([\d,]+\.\d{2})\s*kgs', text)
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if gross_match:
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weights_info["gross_landed_kg"] = float(gross_match.group(1).replace(',', ''))
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# Extraction du poids de tare
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tare_match = re.search(r'Invoice Tare\s*:\s*([\d,]+\.\d{2})\s*Kgs', text)
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if tare_match:
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weights_info["tare_kg"] = float(tare_match.group(1).replace(',', ''))
|
|
|
|
# Extraction du poids net débarqué
|
|
net_landed_match = re.search(r'Net Landed Weight\s*:\s*([\d,]+\.\d{2})\s*Kgs', text)
|
|
if net_landed_match:
|
|
weights_info["net_landed_kg"] = float(net_landed_match.group(1).replace(',', ''))
|
|
|
|
# Extraction du poids net facturé
|
|
invoice_net_match = re.search(r'Net Invoice Weight\s*:\s*([\d,]+\.\d{2})\s*Kgs', text)
|
|
if invoice_net_match:
|
|
weights_info["invoice_net_kg"] = float(invoice_net_match.group(1).replace(',', ''))
|
|
|
|
# Extraction du gain en kg
|
|
gain_match = re.search(r'Gain\s+([\d,]+\.\d{2})\s*Kgs', text)
|
|
if gain_match:
|
|
weights_info["gain_loss_kg"] = float(gain_match.group(1).replace(',', ''))
|
|
|
|
# Extraction du pourcentage de gain (0.4% dans le tableau)
|
|
percent_match = re.search(r'TOTAL\s+\d+\s+[\d,]+\.\d{2}\s+([\d.]+)%', text)
|
|
if percent_match:
|
|
try:
|
|
weights_info["gain_loss_percent"] = float(percent_match.group(1))
|
|
except ValueError:
|
|
pass
|
|
|
|
return weights_info
|
|
|
|
# Configure root logger explicitly
|
|
root = logging.getLogger()
|
|
root.setLevel(logging.INFO)
|
|
root.addHandler(file_handler)
|
|
root.addHandler(logging.StreamHandler())
|
|
|
|
# Use root logger for your app
|
|
logger = logging.getLogger(__name__)
|
|
|
|
app = FastAPI()
|
|
logger.info("Loading models...")
|
|
|
|
nlp = spacy.load("en_core_web_sm")
|
|
predictor = ocr_predictor(pretrained=True)
|
|
|
|
logger.info("Models loaded successfully.")
|
|
|
|
# =============================
|
|
# 🧠 Smart OCR
|
|
# =============================
|
|
# @app.post("/ocr")
|
|
# async def ocr(file: UploadFile):
|
|
# logger.info(f"Received OCR request: {file.filename}")
|
|
# try:
|
|
# file_data = await file.read()
|
|
# ext = file.filename.lower()
|
|
|
|
# # --------- PDF with native text ---------
|
|
# if ext.endswith(".pdf"):
|
|
# logger.info("PDF detected → Extracting native text first")
|
|
# reader = PdfReader(io.BytesIO(file_data))
|
|
# direct_text = "".join(
|
|
# page.extract_text() or "" for page in reader.pages
|
|
# )
|
|
|
|
# if direct_text.strip():
|
|
# logger.info("Native PDF text found → No OCR needed")
|
|
# return {"ocr_text": direct_text}
|
|
|
|
# # -------- Fallback: scanned PDF OCR --------
|
|
# logger.info("No native text → PDF treated as scanned → OCR")
|
|
# from pdf2image import convert_from_bytes
|
|
# images = convert_from_bytes(file_data)
|
|
# text = ""
|
|
# for i, img in enumerate(images):
|
|
# logger.info(f"OCR page {i+1}/{len(images)}")
|
|
# text += pytesseract.image_to_string(img) + "\n"
|
|
|
|
# return {"ocr_text": text}
|
|
|
|
# # --------- Image file OCR ---------
|
|
# logger.info("Image detected → Running OCR")
|
|
# img = Image.open(io.BytesIO(file_data))
|
|
# text = pytesseract.image_to_string(img)
|
|
# return {"ocr_text": text}
|
|
|
|
# except Exception as e:
|
|
# logger.error(f"OCR failed: {e}", exc_info=True)
|
|
# raise HTTPException(status_code=500, detail=str(e))
|
|
@app.post("/ocr")
|
|
async def ocr(file: UploadFile):
|
|
"""
|
|
Smart PDF processing optimized for cotton landing reports
|
|
"""
|
|
logger.info(f"Smart OCR request: {file.filename}")
|
|
|
|
try:
|
|
file_data = await file.read()
|
|
|
|
# Strategy 1: Try pdfplumber (best for digital PDFs)
|
|
try:
|
|
with pdfplumber.open(io.BytesIO(file_data)) as pdf:
|
|
text_parts = []
|
|
tables_found = []
|
|
|
|
for page in pdf.pages:
|
|
# Extract text
|
|
page_text = page.extract_text(x_tolerance=2, y_tolerance=2)
|
|
if page_text:
|
|
text_parts.append(page_text)
|
|
|
|
# Look for tables (common in landing reports)
|
|
tables = page.extract_tables({
|
|
"vertical_strategy": "text",
|
|
"horizontal_strategy": "text",
|
|
"snap_tolerance": 5,
|
|
})
|
|
|
|
for table in tables:
|
|
if table and len(table) > 1:
|
|
tables_found.append(table)
|
|
|
|
combined_text = "\n".join(text_parts)
|
|
return {"ocr_text": combined_text}
|
|
# if combined_text.strip():
|
|
# logger.info(f"pdfplumber extracted {len(combined_text)} chars")
|
|
|
|
# # Try parsing structured data
|
|
# structured_data = parse_cotton_report(combined_text)
|
|
|
|
# # Check if we got key fields
|
|
# if (structured_data.get("shipment", {}).get("bales") and
|
|
# structured_data.get("weights", {}).get("net_landed_kg")):
|
|
# logger.info("Successfully parsed structured data from pdfplumber")
|
|
# return {
|
|
# "method": "pdfplumber",
|
|
# "structured_data": structured_data,
|
|
# "raw_text_sample": combined_text[:500]
|
|
# }
|
|
|
|
except Exception as e:
|
|
logger.warning(f"pdfplumber attempt: {e}")
|
|
|
|
# from pdf2image import convert_from_bytes
|
|
# images = convert_from_bytes(file_data, dpi=200)
|
|
|
|
# ocr_results = []
|
|
# for img in images:
|
|
# text = pytesseract.image_to_string(
|
|
# img,
|
|
# config='--psm 6 -c preserve_interword_spaces=1'
|
|
# )
|
|
# ocr_results.append(text)
|
|
|
|
# ocr_text = "\n".join(ocr_results)
|
|
|
|
# return {
|
|
# "method": "tesseract_ocr",
|
|
# "structured_data": ocr_text,
|
|
# "raw_text_sample": ocr_text[:500]
|
|
# }
|
|
|
|
except Exception as e:
|
|
logger.error(f"Smart OCR failed: {e}", exc_info=True)
|
|
return {
|
|
"error": str(e),
|
|
"success": False
|
|
}
|
|
# =============================
|
|
# 🧱 Structure / Layout
|
|
# =============================
|
|
@app.post("/structure")
|
|
async def structure(file: UploadFile):
|
|
logger.info(f"Received structure request: {file.filename}")
|
|
try:
|
|
file_data = await file.read()
|
|
ext = file.filename.lower()
|
|
|
|
if ext.endswith(".pdf"):
|
|
doc = DocumentFile.from_pdf(file_data)
|
|
logger.info(f"Structure prediction on PDF ({len(doc)} pages)")
|
|
else:
|
|
img = Image.open(io.BytesIO(file_data)).convert("RGB")
|
|
doc = DocumentFile.from_images([img])
|
|
logger.info("Structure prediction on image")
|
|
|
|
res = predictor(doc)
|
|
return {"structure": str(res)}
|
|
|
|
except Exception as e:
|
|
logger.error(f"Structure extraction failed: {e}", exc_info=True)
|
|
raise HTTPException(status_code=500, detail=str(e))
|
|
|
|
# =============================
|
|
# 📊 Tables extraction (PDF only)
|
|
# =============================
|
|
@app.post("/tables")
|
|
async def tables(file: UploadFile):
|
|
logger.info(f"Received table extraction request: {file.filename}")
|
|
try:
|
|
file_data = await file.read()
|
|
buffer = io.BytesIO(file_data)
|
|
|
|
tables = camelot.read_pdf(buffer)
|
|
logger.info(f"Found {len(tables)} tables")
|
|
return {"tables": [t.df.to_dict() for t in tables]}
|
|
|
|
except Exception as e:
|
|
logger.error(f"Table extraction failed: {e}", exc_info=True)
|
|
raise HTTPException(status_code=500, detail=str(e))
|
|
|
|
def safe_search(pattern, text, default=None, group_index=1, context=""):
|
|
"""Recherche sécurisée avec logging en cas d'absence de correspondance."""
|
|
m = re.search(pattern, text, re.I | re.S)
|
|
if not m:
|
|
logger.warning("Pattern not found for %s: %s", context, pattern)
|
|
return default
|
|
try:
|
|
return m.group(group_index).strip()
|
|
except IndexError:
|
|
logger.warning("Group index %d not found for %s: %s", group_index, context, pattern)
|
|
return default
|
|
|
|
def to_float(s):
|
|
if not s:
|
|
return None
|
|
s = s.replace(",", "").replace("Kgs", "").replace("kg", "").replace("%", "")
|
|
s = s.replace("lbs", "").replace("LBS", "")
|
|
s = s.strip()
|
|
try:
|
|
return float(s)
|
|
except:
|
|
return None
|
|
|
|
def section(text, start, end=None):
|
|
"""Extract a block of text between two headings, safely."""
|
|
pattern_start = re.escape(start)
|
|
if end:
|
|
pattern_end = re.escape(end)
|
|
reg = re.compile(pattern_start + r"(.*?)" + pattern_end, re.S | re.I)
|
|
else:
|
|
reg = re.compile(pattern_start + r"(.*)", re.S | re.I)
|
|
m = reg.search(text)
|
|
if not m:
|
|
logger.warning("Section not found: start='%s', end='%s'", start, end)
|
|
return ""
|
|
return m.group(1).strip()
|
|
|
|
def extract_field(text, label, default=None):
|
|
"""Extract a line of the form 'Label: value', safely."""
|
|
pattern = rf"{re.escape(label)}\s*:?[\s]+([^\n]+)"
|
|
return safe_search(pattern, text, default=default, context=f"field '{label}'")
|
|
|
|
def extract(label, text, default=None):
|
|
"""
|
|
Robust extraction for OCR/PDF text.
|
|
Works with:
|
|
Label: Value
|
|
Label Value
|
|
Label .... Value
|
|
"""
|
|
if not text:
|
|
return default
|
|
|
|
patterns = [
|
|
rf"{re.escape(label)}\s*[:\-]?\s*([^\n\r]+)",
|
|
rf"{re.escape(label)}\s+([^\n\r]+)"
|
|
]
|
|
|
|
for p in patterns:
|
|
m = re.search(p, text, re.I)
|
|
if m:
|
|
return m.group(1).strip()
|
|
|
|
return default
|
|
|
|
def extract_report_metadata(text):
|
|
logger.info("Starting metadata extraction, text length=%d", len(text))
|
|
|
|
try:
|
|
# ----------- SECTIONS -----------
|
|
order_details = section(text, "Order details", "Weights")
|
|
invoice_section = section(text, "INVOICE WEIGHTS", "Bales Weighed")
|
|
landed_section = section(text, "Bales Weighed", "Outturn")
|
|
loss_section = section(text, "LOSS", "Invoice average")
|
|
avg_section = section(text, "Invoice average", "Comments")
|
|
signature_block = section(text, "Signed on")
|
|
|
|
# ----------- TOP INFO -----------
|
|
top_info = {
|
|
"produced_on": extract_field(text, "Produced On"),
|
|
"printed_date": extract_field(text, "Printed Date"),
|
|
"client_reference": extract_field(text, "Client Reference"),
|
|
"report_number": safe_search(r"(AHK\S+)", text, default="", context="report_number", group_index=1),
|
|
}
|
|
|
|
# ----------- ORDER DETAILS -----------
|
|
parties = {
|
|
"client": extract_field(order_details, "Client"),
|
|
"client_ref_no": extract_field(order_details, "Client Ref No"),
|
|
"buyer": extract_field(order_details, "Buyer"),
|
|
"destination": extract_field(order_details, "Destination"),
|
|
}
|
|
|
|
shipment = {
|
|
"total_bales": extract_field(order_details, "Total Bales"),
|
|
"vessel": extract_field(order_details, "Vessel"),
|
|
"voyage_no": extract_field(order_details, "Voy. No"),
|
|
"bl_no": extract_field(order_details, "B/L No"),
|
|
"bl_date": extract_field(order_details, "B/L Date"),
|
|
"growth": extract_field(order_details, "Growth"),
|
|
"arrival_date": extract_field(order_details, "Arrival Date"),
|
|
"first_weighing_date": extract_field(order_details, "First date of weighing"),
|
|
"last_weighing_date": extract_field(order_details, "Last Date of Weighing"),
|
|
"weighing_method": extract_field(order_details, "Weighing method"),
|
|
"tare_basis": extract_field(order_details, "Tare"),
|
|
}
|
|
|
|
# ----------- INVOICE SECTION -----------
|
|
invoice = {
|
|
"bales": extract_field(invoice_section, "Bales"),
|
|
"gross": extract_field(invoice_section, "Gross"),
|
|
"tare": extract_field(invoice_section, "Tare"),
|
|
"net": extract_field(invoice_section, "Net"),
|
|
}
|
|
|
|
# ----------- LANDED SECTION -----------
|
|
landed = {
|
|
"bales": extract_field(landed_section, "Bales"),
|
|
"gross": extract_field(landed_section, "Gross"),
|
|
"tare": extract_field(landed_section, "Tare"),
|
|
"net": extract_field(landed_section, "Net"),
|
|
}
|
|
|
|
# ----------- LOSS SECTION -----------
|
|
loss = {
|
|
"kg": extract_field(loss_section, "kg"),
|
|
"lb": extract_field(loss_section, "lb"),
|
|
"percent": extract_field(loss_section, "Percentage"),
|
|
}
|
|
|
|
# ----------- AVERAGES SECTION -----------
|
|
averages = {
|
|
"invoice_gross_per_bale": extract_field(avg_section, "Invoice average"),
|
|
"landed_gross_per_bale": extract_field(avg_section, "Landed average"),
|
|
}
|
|
|
|
# ----------- SIGNATURE -----------
|
|
signature = {
|
|
"signed_on": extract_field(signature_block, "Signed on"),
|
|
"signed_by": safe_search(r"\n([A-Za-z ]+)\nClient Services", signature_block, default="", context="signed_by"),
|
|
"role": "Client Services Coordinator",
|
|
"company": "Alfred H. Knight International Limited"
|
|
}
|
|
|
|
logger.info("Metadata extraction completed successfully")
|
|
return {
|
|
"report": top_info,
|
|
"parties": parties,
|
|
"shipment": shipment,
|
|
"weights": {
|
|
"invoice": invoice,
|
|
"landed": landed,
|
|
"loss": loss,
|
|
"averages": averages
|
|
},
|
|
"signature": signature
|
|
}
|
|
|
|
except Exception as e:
|
|
logger.exception("Unexpected error during metadata extraction")
|
|
raise HTTPException(status_code=500, detail=f"Metadata extraction failed: {e}")
|
|
|
|
def detect_template(text):
|
|
t = text.lower()
|
|
|
|
if "alfred h. knight" in t and "cotton landing report" in t:
|
|
return "AHK"
|
|
|
|
if "intertek" in t and "landing report" in t:
|
|
return "INTERTEK"
|
|
|
|
if "robertson international" in t or "ri ref no" in t:
|
|
return "ROBERTSON"
|
|
|
|
if "landing report" in t and "carcon cargo" in t:
|
|
return "SGS"
|
|
|
|
if "pacific inspection company" in t or "picl-bd.com" in t:
|
|
return "PICL"
|
|
|
|
return "UNKNOWN"
|
|
|
|
@app.post("/metadata")
|
|
async def metadata(text: str = Body(..., embed=True)):
|
|
return extract_report_metadata(text)
|
|
|
|
def call_extractor(text: str, lab: str = "AHK"):
|
|
url = "http://62.72.36.116:8090/extract"
|
|
params = {"lab": lab}
|
|
|
|
fake_file = BytesIO(text.encode("utf-8"))
|
|
|
|
files = {
|
|
"file": ("document.txt", fake_file, "text/plain")
|
|
}
|
|
|
|
response = requests.post(url, params=params, files=files, timeout=60)
|
|
response.raise_for_status()
|
|
|
|
return response.json()
|
|
|
|
|
|
@app.post("/parse")
|
|
async def parse_endpoint(text: str = Body(..., embed=True)):
|
|
lab = parse_report(text)
|
|
result = call_extractor(text, lab=lab)
|
|
return result
|
|
|
|
PARSERS = {
|
|
"AHK": AHKParser(),
|
|
"INTERTEK": IntertekParser()
|
|
}
|
|
|
|
def empty_weight_report(lab):
|
|
return {
|
|
"lab": lab,
|
|
"report": {"reference": None, "file_no": None, "date": None},
|
|
"contract": {"contract_no": None, "invoice_no": None, "lc_no": None, "origin": None, "commodity": None},
|
|
"parties": {"seller": None, "buyer": None, "carrier": None},
|
|
"shipment": {
|
|
"vessel": None, "bl_no": None, "bl_date": None, "port_loading": None,
|
|
"port_destination": None, "arrival_date": None,
|
|
"weighing_place": None, "weighing_method": None,
|
|
"bales": None
|
|
},
|
|
"weights": {
|
|
"gross_landed_kg": None, "tare_kg": None,
|
|
"net_landed_kg": None, "invoice_net_kg": None,
|
|
"gain_loss_kg": None, "gain_loss_percent": None
|
|
}
|
|
}
|
|
|
|
def parse_report(text):
|
|
template=detect_template(text)
|
|
# if template not in PARSERS:
|
|
# return {"template":"UNKNOWN"}
|
|
# return PARSERS[template].parse(text)
|
|
return template |