Feature/2023 10 issue 29

data_extractor/code/esg_data_pipeline/Dockerfile

@@ -1,22 +1,11 @@
-# Modified mmdetection Dockerfile
-ARG PYTORCH="1.4"
-ARG CUDA="10.1"
-ARG CUDNN="7"
+FROM ubuntu:22.04
+SHELL ["/bin/bash", "-c"]
 
-FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
-
-ENV TORCH_CUDA_ARCH_LIST="3.7 6.0 6.1 7.0+PTX"
-ENV TORCH_NVCC_FLAGS="-Xfatbin -compress-all"
-ENV CMAKE_PREFIX_PATH="$(dirname $(which conda))/../"
-
-RUN apt-key del 3bf863cc
-RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub
-
-RUN apt-key del 7fa2af80
-RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
+# no prompt during installation:
+ARG DEBIAN_FRONTEND=noninteractive
 
 # Added poppler-utils, default-jre installations
-RUN apt-get update && apt-get install -y git wget vim ninja-build poppler-utils default-jre \
+RUN apt-get update && apt-get install -y apt-utils git wget python3 python3-pip vim ninja-build poppler-utils default-jre \
     && apt-get clean \
     && rm -rf /var/lib/apt/lists/*
 
@@ -31,9 +20,9 @@ WORKDIR /app/code/esg_data_pipeline
 RUN pip install -e .
 
 RUN mkdir -p /app/server_logs
-RUN chmod -R 777 /app/server_logs
-
 RUN mkdir -p /app/data
+
+RUN chmod -R 777 /app/server_logs
 RUN chmod -R 777 /app/data
 
 CMD ./entry.sh

data_extractor/code/esg_data_pipeline/esg_data_pipeline/__init__.py

@@ -1,6 +1,5 @@
-from .components import Extractor, PDFTextExtractor, \
-                        TextCurator
-
+from .components import Extractor, PDFTextExtractor, TextCurator
+from .extraction_server import run_extraction
 import logging
 from .config import logging_config, config
 

data_extractor/code/esg_data_pipeline/esg_data_pipeline/components/base_curator.py

@@ -1,4 +1,3 @@
-import re
 from abc import abstractmethod
 import re
 

data_extractor/code/esg_data_pipeline/esg_data_pipeline/components/pdf_text_extractor.py

@@ -3,7 +3,6 @@
 import json
 import logging
 import os
-import re
 from pathlib import Path
 
 import pandas as pd
@@ -80,25 +79,26 @@ def extract_pdf_by_page(self, pdf_file):
             _logger.warning("{}: Unable to process {}".format(e, pdf_file))
             return {}
 
-        fp = open(pdf_file, 'rb')
+        pdf_content = {}
+
+        # Create a PDF resource manager
         rsrcmgr = PDFResourceManager()
-        retstr = io.BytesIO()
-        codec = 'utf-8'
         laparams = LAParams()
-        device = TextConverter(rsrcmgr, retstr, codec=codec, laparams=laparams)
-        interpreter = PDFPageInterpreter(rsrcmgr, device)
-
-        pdf_content = {}
-        for page_number, page in enumerate(PDFPage.get_pages(fp, check_extractable=False)):
-            interpreter.process_page(page)
-            data = retstr.getvalue().decode('utf-8')
-            data_paragraphs = self.process_page(data)
-            if len(data_paragraphs) == 0:
-                continue
-            pdf_content[page_number] = data_paragraphs
-            retstr.truncate(0)
-            retstr.seek(0)
-        fp.close()
+        retstr = io.StringIO()
+
+        with open(pdf_file, 'rb') as fp:
+            # Create a PDF page interpreter
+            device = TextConverter(rsrcmgr, retstr, laparams=laparams)
+            interpreter = PDFPageInterpreter(rsrcmgr, device)
+            for page_number, page in enumerate(PDFPage.get_pages(fp)):
+                interpreter.process_page(page)
+                data = retstr.getvalue()
+                data_paragraphs = self.process_page(data)
+                if len(data_paragraphs) == 0:
+                    continue
+                pdf_content[page_number] = data_paragraphs
+                retstr.truncate(0)
+                retstr.seek(0)
 
         return pdf_content
 

data_extractor/code/esg_data_pipeline/esg_data_pipeline/components/text_curator.py

@@ -9,7 +9,7 @@
 
 import pandas as pd
 
-import esg_data_pipeline.utils.kpi_mapping as kpi_mapping
+import data_extractor.code.utils.kpi_mapping as kpi_mapping
 from .base_curator import BaseCurator
 
 logger = logging.getLogger(__name__)

data_extractor/code/esg_data_pipeline/esg_data_pipeline/config/__init__.py

@@ -1,2 +1 @@
 
-

data_extractor/code/esg_data_pipeline/esg_data_pipeline/config/config.py

@@ -1,4 +1,3 @@
-import os
 import pathlib
 
 # General config
@@ -9,15 +8,16 @@
 ROOT = CONFIG_FOLDER.parent.parent.parent.parent
 DATA_FOLDER = ROOT / "data"
 
-#Extraction inputs
+
+# Extraction inputs
 PDFTextExtractor_kwargs = {'min_paragraph_length': 30, 
-                            #Set to  ANNOTATION_FOLDER if you want to extract just pdfs mentioned in the annotations
-                            #Set to None to extract all pdfs in pdf folder (for production stage)
+                            # Set to  ANNOTATION_FOLDER if you want to extract just pdfs mentioned in the annotations
+                            # Set to None to extract all pdfs in pdf folder (for production stage)
                            'annotation_folder': None,
                            'skip_extracted_files': False
                            }
 
-#Curation inputs
+# Curation inputs
 TextCurator_kwargs = {
     'retrieve_paragraph': False,
     'neg_pos_ratio': 1,

data_extractor/code/esg_data_pipeline/esg_data_pipeline/extraction_server.py

@@ -7,11 +7,12 @@
 from flask import Flask, Response, request
 import shutil
 import traceback
-from s3_communication import S3Communication
+from data_extractor.code.utils.s3_communication import S3Communication
+
+from .components import Extractor
+from .config import config
+from .components import Curator
 
-from esg_data_pipeline.components import Extractor
-from esg_data_pipeline.config import config
-from esg_data_pipeline.components import Curator
 
 app = Flask(__name__)
 
@@ -33,21 +34,35 @@ def liveness():
 
 @app.route('/extract/')
 def run_extraction():
-    args = json.loads(request.args['payload'])
+    # args = json.loads(request.args['payload'])
+
+    # Local args dictionary
+    args = {"project_name": 'TEST'}
+    args.update({"s3_usage": False})
+    extraction_settings = {}
+    extraction_settings.update({"use_extractions": False})
+    extraction_settings.update({"seed": 42})
+    extraction_settings.update({"min_paragraph_length": 20})
+    extraction_settings.update({"annotation_folder": None})
+    extraction_settings.update({"skip_extracted_files": False})
+    extraction_settings.update({"store_extractions": True})
+    args.update({"extraction": extraction_settings})
+
+    # Load the extraction settings from args
     project_name = args["project_name"]
-
     extraction_settings = args['extraction']
-
-    BASE_DATA_PROJECT_FOLDER = config.DATA_FOLDER / project_name
-    config.PDF_FOLDER = BASE_DATA_PROJECT_FOLDER / 'interim' / 'pdfs'    
-    BASE_INTERIM_FOLDER = BASE_DATA_PROJECT_FOLDER / 'interim' / 'ml'
-    config.EXTRACTION_FOLDER = BASE_INTERIM_FOLDER / 'extraction'
-    config.ANNOTATION_FOLDER = BASE_INTERIM_FOLDER / 'annotations'
-    config.STAGE = 'extract'
-
-    create_directory(config.EXTRACTION_FOLDER)
-    create_directory(config.ANNOTATION_FOLDER)
-    create_directory(config.PDF_FOLDER)
+
+    # Create path strings for the different folders
+    base_data_project_folder = config.DATA_FOLDER / project_name
+    pdf_folder = base_data_project_folder / 'interim' / 'pdfs'
+    base_interim_folder = base_data_project_folder / 'interim' / 'ml'
+    extraction_folder = base_interim_folder / 'extraction'
+    annotation_folder = base_interim_folder / 'annotations'
+
+    # Create the folders if they do not already exist
+    os.makedirs(extraction_folder, exist_ok=True)
+    os.makedirs(annotation_folder, exist_ok=True)
+    os.makedirs(pdf_folder, exist_ok=True)
 
     s3_usage = args["s3_usage"]
     if s3_usage:
@@ -68,48 +83,53 @@ def run_extraction():
         )
         if extraction_settings['use_extractions']:
             s3c_main.download_files_in_prefix_to_dir(project_prefix + '/output/TEXT_EXTRACTION', 
-                                                     config.EXTRACTION_FOLDER)
+                                                     extraction_folder)
         s3c_interim.download_files_in_prefix_to_dir(project_prefix + '/interim/ml/annotations', 
-                                                     config.ANNOTATION_FOLDER)
+                                                     annotation_folder)
         if args['mode'] == 'train':
             s3c_main.download_files_in_prefix_to_dir(project_prefix + '/input/pdfs/training', 
-                                                     config.PDF_FOLDER)
+                                                     pdf_folder)
         else:
             s3c_main.download_files_in_prefix_to_dir(project_prefix + '/input/pdfs/inference', 
-                                                     config.PDF_FOLDER)
+                                                     pdf_folder)
 
-    pdfs = glob.glob(os.path.join(config.PDF_FOLDER, "*.pdf"))
+    pdfs = glob.glob(os.path.join(pdf_folder, "*.pdf"))
     if len(pdfs) == 0:
-        msg = "No pdf files found in the pdf directory ({})".format(config.PDF_FOLDER)
+        msg = "No pdf files found in the pdf directory ({})".format(pdf_folder)
         return Response(msg, status=500)
-
-    annotation_files = glob.glob(os.path.join(config.ANNOTATION_FOLDER, "*.csv"))
+
+    """
+    # TODO Why do we need annotation at all? Actually extraction does not need that!
+    annotation_files = glob.glob(os.path.join(annotation_folder, "*.csv"))
     if len(annotation_files) == 0:
         msg = "No annotations.csv file found on S3."
         return Response(msg, status=500)
     elif len(annotation_files) > 2:
         msg = "Multiple annotations.csv files found on S3."
         return Response(msg, status=500)
-
+    """
+
+    # Update the settings in config to the user settings
+    config.STAGE = 'extract'
     config.SEED = extraction_settings["seed"]
     config.PDFTextExtractor_kwargs['min_paragraph_length'] = extraction_settings["min_paragraph_length"]
     config.PDFTextExtractor_kwargs['annotation_folder'] = extraction_settings["annotation_folder"]
     config.PDFTextExtractor_kwargs['skip_extracted_files'] = extraction_settings["skip_extracted_files"]
 
+    # Create an extractor class element with the newly updated extraction settings
     ext = Extractor(config.EXTRACTORS)
-
     try:
         t1 = time.time()
-        ext.run_folder(config.PDF_FOLDER, config.EXTRACTION_FOLDER)
+        ext.run_folder(pdf_folder, extraction_folder)
         t2 = time.time()
     except Exception as e:
         msg = "Error during extraction\nException:" + str(e)
         return Response(msg, status=500)
 
-    extracted_files = os.listdir(config.EXTRACTION_FOLDER)
+    extracted_files = os.listdir(extraction_folder)
     if len(extracted_files) == 0:
         msg = "Extraction Failed. No file was found in the extraction directory ({})"\
-            .format(config.EXTRACTION_FOLDER)
+            .format(extraction_folder)
         return Response(msg, status=500)
 
     failed_to_extract = ""
@@ -124,32 +144,49 @@ def run_extraction():
         msg += "The following pdf files, however,  did not get extracted:\n" + failed_to_extract
 
     if s3_usage:
-        s3c_interim.upload_files_in_dir_to_prefix(config.EXTRACTION_FOLDER, 
+        s3c_interim.upload_files_in_dir_to_prefix(extraction_folder,
                                                   project_prefix + '/interim/ml/extraction')
         # clear folder
-        create_directory(config.EXTRACTION_FOLDER)
-        create_directory(config.ANNOTATION_FOLDER)
-        create_directory(config.PDF_FOLDER)
+        create_directory(extraction_folder)
+        create_directory(annotation_folder)
+        create_directory(pdf_folder)
     time_elapsed = str(timedelta(seconds=t2 - t1))
     msg += "\nTime elapsed:{}".format(time_elapsed)
     return Response(msg, status=200)
 
 
 @app.route('/curate/')
 def run_curation():
-    args = json.loads(request.args['payload'])
+    #args = json.loads(request.args['payload'])
+
+    # Local args dictionary
+    args = {"project_name": 'TEST'}
+    args.update({"s3_usage": False})
+    curation_settings = {}
+    curation_settings.update({"retrieve_paragraph": False})
+    curation_settings.update({"neg_pos_ratio": 1})
+    curation_settings.update({"columns_to_read": ["company", "source_file", "source_page", "kpi_id", "year", "answer", "data_type", "relevant_paragraphs"]})
+    curation_settings.update({"company_to_exclude": []})
+    curation_settings.update({"create_neg_samples": True})
+    curation_settings.update({"min_length_neg_sample": 50})
+    curation_settings.update({"seed": 41})
+    args.update({"curation": curation_settings})
+
+    # Load the extraction settings from args
     project_name = args["project_name"]
-    curation_settings = args["curation"]
+    curation_settings = args['curation']
 
     BASE_DATA_PROJECT_FOLDER = config.DATA_FOLDER / project_name
     BASE_INTERIM_FOLDER = BASE_DATA_PROJECT_FOLDER / 'interim' / 'ml'
-    config.EXTRACTION_FOLDER = BASE_INTERIM_FOLDER / 'extraction'
+    extraction_folder = BASE_INTERIM_FOLDER / 'extraction'
     config.CURATION_FOLDER = BASE_INTERIM_FOLDER / 'curation'
-    config.ANNOTATION_FOLDER = BASE_INTERIM_FOLDER / 'annotations'
+    annotation_folder = BASE_INTERIM_FOLDER / 'annotations'
     config.KPI_FOLDER = BASE_DATA_PROJECT_FOLDER / 'interim' / 'kpi_mapping'
-    create_directory(config.EXTRACTION_FOLDER)
-    create_directory(config.CURATION_FOLDER)
-    create_directory(config.ANNOTATION_FOLDER)
+
+    os.makedirs(extraction_folder, exist_ok=True)
+    os.makedirs(config.CURATION_FOLDER, exist_ok=True)
+    os.makedirs(annotation_folder, exist_ok=True)
+    os.makedirs(config.KPI_FOLDER, exist_ok=True)
 
     s3_usage = args["s3_usage"]
     if s3_usage:
@@ -169,9 +206,9 @@ def run_curation():
             s3_bucket=os.getenv(s3_settings['interim_bucket']['s3_bucket_name']),
         )
         s3c_main.download_files_in_prefix_to_dir(project_prefix + '/input/kpi_mapping', config.KPI_FOLDER)
-        s3c_interim.download_files_in_prefix_to_dir(project_prefix + '/interim/ml/extraction', config.EXTRACTION_FOLDER)
+        s3c_interim.download_files_in_prefix_to_dir(project_prefix + '/interim/ml/extraction', extraction_folder)
         s3c_main.download_files_in_prefix_to_dir(project_prefix + '/input/annotations',
-                                                 config.ANNOTATION_FOLDER)
+                                                 annotation_folder)
 
     shutil.copyfile(os.path.join(config.KPI_FOLDER, "kpi_mapping.csv"), "/app/code/kpi_mapping.csv")
 
@@ -183,21 +220,21 @@ def run_curation():
     config.TextCurator_kwargs['min_length_neg_sample'] = curation_settings['min_length_neg_sample']
     config.SEED = curation_settings['seed']
 
-    try:
-        if len(config.CURATORS) != 0:
-            cur = Curator(config.CURATORS)
-            cur.run(config.EXTRACTION_FOLDER, config.ANNOTATION_FOLDER, config.CURATION_FOLDER)
-    except Exception as e:
-        msg = "Error during curation\nException:" + str(repr(e)) + traceback.format_exc()
-        return Response(msg, status=500)
+    #try:
+    if len(config.CURATORS) != 0:
+        cur = Curator(config.CURATORS)
+        cur.run(extraction_folder, annotation_folder, config.CURATION_FOLDER)
+    #except Exception as e:
+        #msg = "Error during curation\nException:" + str(repr(e)) + traceback.format_exc()
+        #return Response(msg, status=500)
 
     if s3_usage:
         s3c_interim.upload_files_in_dir_to_prefix(config.CURATION_FOLDER, 
                                                   project_prefix + '/interim/ml/curation')
         # clear folder
         create_directory(config.KPI_FOLDER)
-        create_directory(config.EXTRACTION_FOLDER)
-        create_directory(config.ANNOTATION_FOLDER)
+        create_directory(extraction_folder)
+        create_directory(annotation_folder)
         create_directory(config.CURATION_FOLDER)
 
     return Response("Curation OK", status=200)