diff --git a/source/containers/document-pii-detection/Dockerfile b/source/containers/document-pii-detection/Dockerfile
index 91f1b3f8..d0de5bc8 100644
--- a/source/containers/document-pii-detection/Dockerfile
+++ b/source/containers/document-pii-detection/Dockerfile
@@ -1,27 +1,30 @@
-FROM public.ecr.aws/lambda/python:3.9
+# temp stage
+FROM python:3.12-slim-bullseye as builder
 
-ARG FUNCTION_DIR="/opt/ml/code"
-COPY requirements.txt ${FUNCTION_DIR}/requirements.txt
-RUN python3.9 -m pip install -r ${FUNCTION_DIR}/requirements.txt
+# Install build dependencies
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends gcc g++
 
-COPY main.py parser_factory.py ${FUNCTION_DIR}/
-COPY parsers/ ${FUNCTION_DIR}/parsers/
+# Install Python dependencies first
+COPY requirements.txt .
+RUN pip wheel --no-cache-dir --no-deps --wheel-dir /app/wheels -r requirements.txt
 
-ARG OCR_MODEL_URL="https://aws-gcr-solutions-assets.s3.cn-northwest-1.amazonaws.com.cn/ai-solution-kit/infer-ocr-model/standard"
-ARG OCR_MODEL_VERSION="v1.0.0"
-ARG FD_MODEL_URL="https://aws-gcr-solutions-assets.s3.cn-northwest-1.amazonaws.com.cn/ai-solution-kit/face-detection"
-ARG FD_MODEL_VERSION="1.2.0"
+# prod stage
+FROM python:3.12-slim-bullseye
 
-RUN yum install -y wget
-RUN mkdir -p ${FUNCTION_DIR}/ocr_model
-RUN wget -c $OCR_MODEL_URL/$OCR_MODEL_VERSION/classifier.onnx -O ${FUNCTION_DIR}/ocr_model/classifier.onnx
-RUN wget -c $OCR_MODEL_URL/$OCR_MODEL_VERSION/det_standard.onnx -O ${FUNCTION_DIR}/ocr_model/det_standard.onnx
-RUN wget -c $OCR_MODEL_URL/$OCR_MODEL_VERSION/keys_v1.txt -O ${FUNCTION_DIR}/ocr_model/keys_v1.txt
-RUN wget -c $OCR_MODEL_URL/$OCR_MODEL_VERSION/rec_standard.onnx -O ${FUNCTION_DIR}/ocr_model/rec_standard.onnx
-RUN mkdir -p ${FUNCTION_DIR}/fd_model
-RUN wget -c ${FD_MODEL_URL}/${FD_MODEL_VERSION}/det.onnx -O ${FUNCTION_DIR}/fd_model/det.onnx
+ARG FUNCTION_DIR="/opt/ml/code"
 
 WORKDIR ${FUNCTION_DIR}
 
-# Command can be overwritten by providing a different command in the template directly.
-ENTRYPOINT ["python"]
+RUN apt-get update && apt-get install -y --no-install-recommends libmagic1 && rm -rf /var/lib/apt/lists/*
+
+COPY ocr_model/ ${FUNCTION_DIR}/ocr_model/
+COPY fd_model/ ${FUNCTION_DIR}/fd_model/
+COPY --from=builder /app/wheels ${FUNCTION_DIR}/wheels
+RUN pip install --no-cache ${FUNCTION_DIR}/wheels/*
+
+COPY parsers/ ${FUNCTION_DIR}/parsers/
+COPY requirements.txt main.py parser_factory.py ${FUNCTION_DIR}/
+
+
+CMD ["python3", "main.py"]
\ No newline at end of file
diff --git a/source/containers/document-pii-detection/fd_model/det.onnx b/source/containers/document-pii-detection/fd_model/det.onnx
new file mode 100644
index 00000000..dfcd378b
Binary files /dev/null and b/source/containers/document-pii-detection/fd_model/det.onnx differ
diff --git a/source/containers/document-pii-detection/main.py b/source/containers/document-pii-detection/main.py
index 7aa831e0..67024838 100644
--- a/source/containers/document-pii-detection/main.py
+++ b/source/containers/document-pii-detection/main.py
@@ -1,18 +1,21 @@
-import json
-import boto3
-import os
-import sys
-import pandas as pd
-import re
 import argparse
 import copy
-import logging
-import tempfile
+import json
 import pathlib
+import re
+import tempfile
 from collections import defaultdict
+from multiprocessing import Process, Queue, Lock
+
+import boto3
+import pandas as pd
+import psutil
+import pyarrow as pa
+import pyarrow.parquet as pq
 
 from parser_factory import ParserFactory
 
+
 def remove_symbols(input_string):
     # Define the pattern to match symbols
     symbol_pattern = r'\W'
@@ -22,6 +25,7 @@ def remove_symbols(input_string):
 
     return output_string
 
+
 def group_files_by_extension(sample_files):
     """
     Group files by extension.
@@ -35,6 +39,7 @@ def group_files_by_extension(sample_files):
         sample_file_extension_dict[sample_file_extension].append(sample_file)
     return sample_file_extension_dict
 
+
 def split_dictionary(raw_dictionary, chunk_size=100):
     keys = list(raw_dictionary.keys())
     total_items = len(keys)
@@ -50,8 +55,8 @@ def split_dictionary(raw_dictionary, chunk_size=100):
 
     return split_dictionaries
 
-def get_previous_tables(glue_client, database_name):
 
+def get_previous_tables(glue_client, database_name):
     tables = []
 
     next_token = ""
@@ -70,17 +75,16 @@ def get_previous_tables(glue_client, database_name):
     return tables
 
 
-def organize_table_info(table_name, result_bucket_name, original_bucket_name, file_info, columns, file_category, split_file_content_id):
-
+def organize_table_info(table_name, result_bucket_name, original_bucket_name, file_info, columns, file_category,
+                        split_file_content_id):
     simplified_file_info = copy.deepcopy(file_info)
     simplified_file_info['sample_files'] = simplified_file_info['sample_files'][:10]
     description = json.dumps(simplified_file_info, ensure_ascii=False)
     s3_location = f"s3://{result_bucket_name}/parser_results/{table_name}/"
-    input_format = 'org.apache.hadoop.mapred.TextInputFormat'
+    input_format = 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
     output_format = 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
     table_type = 'EXTERNAL_TABLE'
-    serde_info = {'SerializationLibrary': 'org.apache.hadoop.hive.serde2.OpenCSVSerde',
-                'Parameters': {'field.delim': ','}}
+    serde_info = {'SerializationLibrary': 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'}
     parameters = {'originalFileBucketName': original_bucket_name,
                   'originalFileType': file_info['file_type'],
                   'originalFilePath': file_info['file_path'],
@@ -88,13 +92,13 @@ def organize_table_info(table_name, result_bucket_name, original_bucket_name, fi
                   'originalFileCategory': file_category,
                   'sizeKey': str(file_info['total_file_size']),
                   'objectCount': str(file_info['total_file_count']),
-                  'Unstructured': 'true',
-                  'classification': 'csv',
+                  'Unstructured': 'false',
+                  'classification': 'parquet',
                   'splitFileContentId': str(split_file_content_id)}
     glue_table_columns = [{'Name': 'index', 'Type': 'string'}]
     for column in columns:
         glue_table_columns.append({'Name': column, 'Type': 'string'})
-    
+
     glue_table_info = {
         'Name': table_name,
         'Description': description,
@@ -112,6 +116,7 @@ def organize_table_info(table_name, result_bucket_name, original_bucket_name, fi
     }
     return glue_table_info
 
+
 def batch_process_files(s3_client, bucket_name, file_info, file_category):
     """
     Batch process files in a folder with the same schema.
@@ -126,7 +131,7 @@ def batch_process_files(s3_client, bucket_name, file_info, file_category):
         "total_file_size": 421226,
         "total_file_count": 1,
         "sample_files": [
-            "工商营业执照.jpg"
+            "xxxxx.jpg"
         ]
     }
 
@@ -143,7 +148,7 @@ def batch_process_files(s3_client, bucket_name, file_info, file_category):
         grouped_sample_files = group_files_by_extension(sample_files)
 
         for sample_file_extension, sample_file_extension_files in grouped_sample_files.items():
-        
+
             parser = ParserFactory.create_parser(file_type=sample_file_extension, s3_client=s3_client)
 
             for sample_file in sample_file_extension_files:
@@ -156,14 +161,15 @@ def batch_process_files(s3_client, bucket_name, file_info, file_category):
         for sample_file in sample_files:
             sample_file_name_no_symbol = remove_symbols(sample_file)
             file_contents[f"{sample_file_name_no_symbol}"] = ['This file is marked as Contains-PII.']
-    
+
     elif file_category == 'exclude_files':
         for sample_file in sample_files:
             sample_file_name_no_symbol = remove_symbols(sample_file)
             file_contents[f"{sample_file_name_no_symbol}"] = ['This file is marked as Non-PII.']
-            
+
     return file_contents
 
+
 def process_file(parser, bucket_name, object_key):
     """
     Process a single file.
@@ -172,11 +178,11 @@ def process_file(parser, bucket_name, object_key):
 
     json_format_content = {}
     json_format_content[f"{object_key}"] = file_content
-    
+
     return json_format_content
 
-def create_glue_table(glue_client, database_name, table_name, glue_table_info):
 
+def create_glue_table(glue_client, database_name, table_name, glue_table_info):
     # Check if table exists
     try:
         response = glue_client.get_table(
@@ -195,18 +201,93 @@ def create_glue_table(glue_client, database_name, table_name, glue_table_info):
             TableInput=glue_table_info
         )
 
-    print(response)
+
+# Worker function to process a detection_files item
+def worker(queue,
+           lock,
+           crawler_result_bucket_name,
+           destination_database,
+           original_file_bucket_name,
+           region_name):
+    while not queue.empty():
+        with lock:
+            item = queue.get()
+            if item is None:
+                break
+
+        glue_client = boto3.client('glue', region_name=region_name)
+        s3_client = boto3.client('s3', region_name=region_name)
+        file_category = item[0]
+        file = item[1]
+        file_path = file[0]
+        file_info = file[1]
+        try:
+            print('Worker is processing ' + file_path)
+            file_contents = batch_process_files(s3_client, original_file_bucket_name, file_info, file_category)
+            # split file_contents into several dictionaries, with which contain 100 files at most
+            split_file_contents_list = split_dictionary(file_contents, chunk_size=100)
+            len_split_file_contents_list = len(split_file_contents_list)
+            for split_file_content_id, split_file_contents in enumerate(split_file_contents_list):
+                # convert file_contents to dataframe
+                df = pd.DataFrame.from_dict(split_file_contents, orient='index')
+                df = df.transpose()
+                columns = df.columns.tolist()
+
+                # dump file_info into string and encode in base64 as filename
+                file_path = file_path.lstrip('./')
+                file_path_process_list = file_path.split('/')
+                if len_split_file_contents_list > 1:
+                    file_path_process_list = file_path_process_list[:-1] + [
+                        f"part{str(split_file_content_id)}"] + file_path_process_list[-1:]
+
+                table_name = '_'.join(file_path_process_list)
+
+                table_name = table_name.replace('.', '_')
+                table_name = original_file_bucket_name + '_' + table_name
+
+                # Convert the DataFrame to a PyArrow Table
+                table = pa.Table.from_pandas(df)
+
+                # Create a PyArrow Writer for Parquet format without compression
+                sink = pa.BufferOutputStream()
+                writer = pq.ParquetWriter(sink, table.schema)
+
+                # Write the PyArrow Table to the buffer
+                writer.write_table(table)
+
+                # Close the writer
+                writer.close()
+
+                # Get the uncompressed in-memory Parquet data as bytes
+                parquet_bytes = sink.getvalue().to_pybytes()
+
+                # Upload the uncompressed Parquet data to S3
+                s3_client.put_object(
+                    Body=parquet_bytes,
+                    Bucket=crawler_result_bucket_name,
+                    Key=f"parser_results/{table_name}/result.parquet"
+                )
+
+                glue_table_info = organize_table_info(table_name, crawler_result_bucket_name,
+                                                      original_file_bucket_name, file_info, columns, file_category,
+                                                      split_file_content_id)
+                create_glue_table(glue_client, destination_database, table_name, glue_table_info)
+
+        except Exception as e:
+            print(f"Error occurred processing {file_path}. Error message: {e}")
+        print('Worker is finished on ' + file_path)
+
 
 def main(param_dict):
     original_bucket_name = param_dict['SourceBucketName']
     crawler_result_bucket_name = param_dict['ResultBucketName']
     region_name = param_dict['RegionName']
-
+    worker_num = psutil.cpu_count(logical=False)
     crawler_result_object_key = f"crawler_results/{original_bucket_name}_info.json"
     destination_database = f"SDPS-unstructured-{original_bucket_name}"
 
-    s3_client = boto3.client('s3', region_name = region_name)
-    glue_client = boto3.client('glue', region_name = region_name)
+    s3_client = boto3.client('s3', region_name=region_name)
+    glue_client = boto3.client('glue', region_name=region_name)
 
     # 1. Create a Glue Database
     try:
@@ -218,76 +299,70 @@ def main(param_dict):
     except glue_client.exceptions.AlreadyExistsException:
         print(f"Database '{destination_database}' already exists. Skipping database creation...")
         # Need to delete previous created tables
-        print(f"Deleting all tables in database '{destination_database}'...")
-        previous_tables = get_previous_tables(glue_client, destination_database)
-        for table in previous_tables:
-            response = glue_client.delete_table(
-                DatabaseName=destination_database,
-                Name=table['Name']
-            )
-        print(f"Deleted all tables in database '{destination_database}'. Start creating tables...")
 
-    # 2. Download the crawler result from S3 and 
+        response = glue_client.delete_database(Name=destination_database)
+        response = glue_client.create_database(
+            DatabaseInput={
+                'Name': destination_database
+            }
+        )
+        print(f"Re-created database '{destination_database}'")
+
+    # 2. Download the crawler result from S3
     with tempfile.NamedTemporaryFile(mode='w') as temp:
         temp_file_path = temp.name
-        s3_client.download_file(Bucket=crawler_result_bucket_name, Key=crawler_result_object_key, Filename=temp_file_path)
+        s3_client.download_file(Bucket=crawler_result_bucket_name, Key=crawler_result_object_key,
+                                Filename=temp_file_path)
         with open(temp_file_path, 'r') as f:
             bucket_info = json.load(f)
 
-
-    
     # 4. Batch process files in same folder with same type
+    queue = Queue()
+    lock = Lock()
+
     original_file_bucket_name = bucket_info['bucket_name']
+    num_items = 0
     for file_category in ['detection_files', 'include_files', 'exclude_files']:
-        files = bucket_info[file_category]
-        for file_path, file_info in files.items():
-            print(f"Processing {file_path}...")
-            try:
-                file_contents = batch_process_files(s3_client, original_file_bucket_name, file_info, file_category)
-
-                # split file_contents into several dictionaries, with which contain 100 files at most
-                split_file_contents_list = split_dictionary(file_contents, chunk_size=100)
-                len_split_file_contents_list = len(split_file_contents_list)
-                for split_file_content_id, split_file_contents in enumerate(split_file_contents_list):
-                    # convert file_contents to dataframe
-                    df = pd.DataFrame.from_dict(split_file_contents, orient='index')
-                    df = df.transpose()
-                    columns = df.columns.tolist()
-
-                    # dump file_info into string and encode in base64 as filename
-                    file_path = file_path.lstrip('./')
-                    file_path_process_list = file_path.split('/')
-                    if len_split_file_contents_list > 1:
-                        file_path_process_list = file_path_process_list[:-1] + [f"part{str(split_file_content_id)}"] + file_path_process_list[-1:]
-                    
-                    table_name = '_'.join(file_path_process_list)
-
-                    table_name = table_name.replace('.', '_')
-                    table_name = original_file_bucket_name + '_' + table_name
-
-                    # save to csv and upload to s3
-                    with tempfile.NamedTemporaryFile(mode='w') as temp:
-                        csv_file_path = temp.name
-                        df.to_csv(csv_file_path, header=False)
-                        s3_client.upload_file(csv_file_path, crawler_result_bucket_name, f"parser_results/{table_name}/result.csv")
-
-                    glue_table_info = organize_table_info(table_name, crawler_result_bucket_name, original_file_bucket_name, file_info, columns, file_category, split_file_content_id)
-                    create_glue_table(glue_client, destination_database, table_name, glue_table_info)
-
-            except Exception as e:
-                print(f"Error occured processing {file_path}. Error message: {e}")
-
-    
+        for files in bucket_info[file_category].items():
+            queue.put((file_category, files))
+            num_items += 1
+
+    processes = []
+    worker_num = min(worker_num, num_items)
+    for _ in range(worker_num):
+        p = Process(
+            target=worker,
+            args=(
+                queue,
+                lock,
+                crawler_result_bucket_name,
+                destination_database,
+                original_file_bucket_name,
+                region_name
+            )
+        )
+        processes.append(p)
+        p.start()
+
+    # Add None to the queue to signal workers to exit
+    for _ in range(worker_num):
+        queue.put(None)
+
+    # Wait for all worker processes to finish
+    for p in processes:
+        p.join()
+
+
 if __name__ == '__main__':
     parser = argparse.ArgumentParser(...)
-    parser.add_argument('--SourceBucketName', type=str, default='icyxu-glue-assets-member-a',
+    parser.add_argument('--SourceBucketName', type=str, default='',
                         help='crawler_result_bucket_name')
-    parser.add_argument('--ResultBucketName', type=str, default='icyxu-glue-assets-member-a',
+    parser.add_argument('--ResultBucketName', type=str, default='',
                         help='crawler_result_bucket_name')
-    parser.add_argument('--RegionName', type=str, default='us-west-2',
+    parser.add_argument('--RegionName', type=str, default='',
                         help='crawler_result_object_key')
 
     args, _ = parser.parse_known_args()
     param_dict = copy.copy(vars(args))
-    
+
     main(param_dict)
diff --git a/source/containers/document-pii-detection/ocr_model/classifier.onnx b/source/containers/document-pii-detection/ocr_model/classifier.onnx
new file mode 100644
index 00000000..bd615425
Binary files /dev/null and b/source/containers/document-pii-detection/ocr_model/classifier.onnx differ
diff --git a/source/containers/document-pii-detection/ocr_model/det_standard.onnx b/source/containers/document-pii-detection/ocr_model/det_standard.onnx
new file mode 100644
index 00000000..a3dc4f99
Binary files /dev/null and b/source/containers/document-pii-detection/ocr_model/det_standard.onnx differ
diff --git a/source/containers/document-pii-detection/ocr_model/keys_v1.txt b/source/containers/document-pii-detection/ocr_model/keys_v1.txt
new file mode 100644
index 00000000..3290aadb
--- /dev/null
+++ b/source/containers/document-pii-detection/ocr_model/keys_v1.txt
@@ -0,0 +1,9219 @@
+'
+疗
+绚
+诚
+娇
+溜
+题
+贿
+者
+廖
+更
+纳
+加
+奉
+公
+一
+就
+汴
+计
+与
+路
+房
+原
+妇
+2
+0
+8
+-
+7
+其
+>
+:
+]
+,
+，
+骑
+刈
+全
+消
+昏
+傈
+安
+久
+钟
+嗅
+不
+影
+处
+驽
+蜿
+资
+关
+椤
+地
+瘸
+专
+问
+忖
+票
+嫉
+炎
+韵
+要
+月
+田
+节
+陂
+鄙
+捌
+备
+拳
+伺
+眼
+网
+盎
+大
+傍
+心
+东
+愉
+汇
+蹿
+科
+每
+业
+里
+航
+晏
+字
+平
+录
+先
+1
+3
+彤
+鲶
+产
+稍
+督
+腴
+有
+象
+岳
+注
+绍
+在
+泺
+文
+定
+核
+名
+水
+过
+理
+让
+偷
+率
+等
+这
+发
+”
+为
+含
+肥
+酉
+相
+鄱
+七
+编
+猥
+锛
+日
+镀
+蒂
+掰
+倒
+辆
+栾
+栗
+综
+涩
+州
+雌
+滑
+馀
+了
+机
+块
+司
+宰
+甙
+兴
+矽
+抚
+保
+用
+沧
+秩
+如
+收
+息
+滥
+页
+疑
+埠
+!
+！
+姥
+异
+橹
+钇
+向
+下
+跄
+的
+椴
+沫
+国
+绥
+獠
+报
+开
+民
+蜇
+何
+分
+凇
+长
+讥
+藏
+掏
+施
+羽
+中
+讲
+派
+嘟
+人
+提
+浼
+间
+世
+而
+古
+多
+倪
+唇
+饯
+控
+庚
+首
+赛
+蜓
+味
+断
+制
+觉
+技
+替
+艰
+溢
+潮
+夕
+钺
+外
+摘
+枋
+动
+双
+单
+啮
+户
+枇
+确
+锦
+曜
+杜
+或
+能
+效
+霜
+盒
+然
+侗
+电
+晁
+放
+步
+鹃
+新
+杖
+蜂
+吒
+濂
+瞬
+评
+总
+隍
+对
+独
+合
+也
+是
+府
+青
+天
+诲
+墙
+组
+滴
+级
+邀
+帘
+示
+已
+时
+骸
+仄
+泅
+和
+遨
+店
+雇
+疫
+持
+巍
+踮
+境
+只
+亨
+目
+鉴
+崤
+闲
+体
+泄
+杂
+作
+般
+轰
+化
+解
+迂
+诿
+蛭
+璀
+腾
+告
+版
+服
+省
+师
+小
+规
+程
+线
+海
+办
+引
+二
+桧
+牌
+砺
+洄
+裴
+修
+图
+痫
+胡
+许
+犊
+事
+郛
+基
+柴
+呼
+食
+研
+奶
+律
+蛋
+因
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+箪
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diff --git a/source/containers/document-pii-detection/ocr_model/rec_standard.onnx b/source/containers/document-pii-detection/ocr_model/rec_standard.onnx
new file mode 100644
index 00000000..f546d7cf
Binary files /dev/null and b/source/containers/document-pii-detection/ocr_model/rec_standard.onnx differ
diff --git a/source/containers/document-pii-detection/parser_factory.py b/source/containers/document-pii-detection/parser_factory.py
index 9948c062..8f0f364a 100644
--- a/source/containers/document-pii-detection/parser_factory.py
+++ b/source/containers/document-pii-detection/parser_factory.py
@@ -23,7 +23,7 @@ def create_parser(file_type, s3_client):
         elif file_type.lower() in ['.eml']:
             return EmailParser(s3_client=s3_client)
         elif file_type.lower() in ['.jpg', '.jpeg', '.png', ".gif", ".bmp", ".tiff", ".tif"]:
-            return ImageParser(s3_client=s3_client, fd_model_path='./fd_model/', 
+            return ImageParser(s3_client=s3_client, fd_model_path='./fd_model/',
                                ocr_model_path='./ocr_model/')
         else:
             raise ValueError('Unsupported file type')
\ No newline at end of file
diff --git a/source/containers/document-pii-detection/parsers/doc_parser.py b/source/containers/document-pii-detection/parsers/doc_parser.py
index 20f1d118..42be60ce 100644
--- a/source/containers/document-pii-detection/parsers/doc_parser.py
+++ b/source/containers/document-pii-detection/parsers/doc_parser.py
@@ -1,17 +1,18 @@
 
 import docx
+import io
 from .parser import BaseParser
 
 class DocParser(BaseParser):
     def __init__(self, s3_client):
         super().__init__(s3_client=s3_client)
 
-    def parse_file(self, doc_path):
+    def parse_file(self, doc_stream):
         """
         Extracts text from a doc file and returns a string of content.
         """
 
-        doc = docx.Document(doc_path)
+        doc = docx.Document(io.BytesIO(doc_stream))
         file_content = ""
         for para in doc.paragraphs:
             file_content += para.text + "\n"
diff --git a/source/containers/document-pii-detection/parsers/email_parser.py b/source/containers/document-pii-detection/parsers/email_parser.py
index 45a35120..c1f23dfc 100644
--- a/source/containers/document-pii-detection/parsers/email_parser.py
+++ b/source/containers/document-pii-detection/parsers/email_parser.py
@@ -2,33 +2,40 @@
 import os
 from .parser import BaseParser
 import quopri
-from email.parser import Parser as PyEmailParser
+from email.parser import BytesParser as PyEmailParser
+import io
+import chardet
 
 class EmailParser(BaseParser):
     def __init__(self, s3_client):
         super().__init__(s3_client=s3_client)
 
 
-    def parse_file(self, eml_path):
+    def parse_file(self, eml_stream):
         """
         Extracts text from a eml file and returns a string of content.
         """
 
-        file_encoding = self.get_encoding(eml_path)
+        # file_encoding = self.get_encoding(eml_stream)
+        result = chardet.detect(eml_stream)
+        file_encoding = result['encoding']
 
-        with open(eml_path) as stream:
-            parser = PyEmailParser()
-            message = parser.parse(stream)
+        # with open(eml_stream) as stream:
+        parser = PyEmailParser()
+        # message = parser.parsebytes(io.BytesIO(eml_stream))
 
         file_content = []
-        for part in message.walk():
-            if part.get_content_type().startswith('text/plain'):
-                part_payload = part.get_payload()
-                if file_encoding == 'us-ascii':
-                    decoded_string = quopri.decodestring(part_payload).decode('utf-8', errors='ignore')
-                else:
-                    decoded_string = part_payload
-                file_content.append(decoded_string)
+        eml_content = eml_stream.decode(file_encoding)  # Decode the stream if needed
+        msg = parser.parsebytes(eml_content.encode())
+
+        # text = ""
+        if msg.is_multipart():
+            for part in msg.walk():
+                content_type = part.get_content_type()
+                if content_type == "text/plain":
+                    file_content.append(part.get_payload(decode=True).decode())
+        else:
+            file_content.append(msg.get_payload(decode=True).decode())
 
 
         return ['\n'.join(file_content)]
diff --git a/source/containers/document-pii-detection/parsers/html_parser.py b/source/containers/document-pii-detection/parsers/html_parser.py
index 45f850b9..127c80d7 100644
--- a/source/containers/document-pii-detection/parsers/html_parser.py
+++ b/source/containers/document-pii-detection/parsers/html_parser.py
@@ -11,13 +11,12 @@ def __init__(self, s3_client):
         super().__init__(s3_client=s3_client)
         # additional PdfParser constructor code here
 
-    def parse_file(self, html_path):
+    def parse_file(self, html_stream):
         """
         Extracts text from a html file and returns a string of content.
         """
 
-        with open(html_path, "rb") as stream:
-            soup = BeautifulSoup(stream, 'lxml')
+        soup = BeautifulSoup(html_stream, 'lxml')
 
         # Convert tables to ASCII ones
         soup = self._replace_tables(soup)
diff --git a/source/containers/document-pii-detection/parsers/image_analysis/face_detection/face_detection_main.py b/source/containers/document-pii-detection/parsers/image_analysis/face_detection/face_detection_main.py
index 17fa0c16..4d18fa0f 100644
--- a/source/containers/document-pii-detection/parsers/image_analysis/face_detection/face_detection_main.py
+++ b/source/containers/document-pii-detection/parsers/image_analysis/face_detection/face_detection_main.py
@@ -5,6 +5,7 @@
 import cv2
 
 cuda_available = False
+onnxruntime.set_default_logger_severity(3)
 
 def distance2bbox(points, distance, max_shape=None):
     """Decode distance prediction to bounding box.
diff --git a/source/containers/document-pii-detection/parsers/image_analysis/general_ocr/ocr_main.py b/source/containers/document-pii-detection/parsers/image_analysis/general_ocr/ocr_main.py
index 679545e5..b263faad 100644
--- a/source/containers/document-pii-detection/parsers/image_analysis/general_ocr/ocr_main.py
+++ b/source/containers/document-pii-detection/parsers/image_analysis/general_ocr/ocr_main.py
@@ -13,6 +13,8 @@
 
 cuda_available = False
 
+onnxruntime.set_default_logger_severity(3)
+
 def sorted_boxes(dt_boxes):
     """
     Sort text boxes in order from top to bottom, left to right
diff --git a/source/containers/document-pii-detection/parsers/image_analysis/general_ocr/postprocess/db_postprocess.py b/source/containers/document-pii-detection/parsers/image_analysis/general_ocr/postprocess/db_postprocess.py
index 741a2c88..a4c40c7f 100644
--- a/source/containers/document-pii-detection/parsers/image_analysis/general_ocr/postprocess/db_postprocess.py
+++ b/source/containers/document-pii-detection/parsers/image_analysis/general_ocr/postprocess/db_postprocess.py
@@ -107,10 +107,10 @@ def get_mini_boxes(self, contour):
     def box_score_fast(self, bitmap, _box):
         h, w = bitmap.shape[:2]
         box = _box.copy()
-        xmin = np.clip(np.floor(box[:, 0].min()).astype(np.int), 0, w - 1)
-        xmax = np.clip(np.ceil(box[:, 0].max()).astype(np.int), 0, w - 1)
-        ymin = np.clip(np.floor(box[:, 1].min()).astype(np.int), 0, h - 1)
-        ymax = np.clip(np.ceil(box[:, 1].max()).astype(np.int), 0, h - 1)
+        xmin = np.clip(np.floor(box[:, 0].min()).astype(np.int32), 0, w - 1)
+        xmax = np.clip(np.ceil(box[:, 0].max()).astype(np.int32), 0, w - 1)
+        ymin = np.clip(np.floor(box[:, 1].min()).astype(np.int32), 0, h - 1)
+        ymax = np.clip(np.ceil(box[:, 1].max()).astype(np.int32), 0, h - 1)
 
         mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8)
         box[:, 0] = box[:, 0] - xmin
diff --git a/source/containers/document-pii-detection/parsers/image_parser.py b/source/containers/document-pii-detection/parsers/image_parser.py
index 2b51c2dd..a98dd4d9 100644
--- a/source/containers/document-pii-detection/parsers/image_parser.py
+++ b/source/containers/document-pii-detection/parsers/image_parser.py
@@ -4,6 +4,7 @@
 
 from PIL import Image
 import numpy as np
+import io
 
 from .image_analysis.face_detection import face_detection_main
 from .image_analysis.general_ocr import ocr_main
@@ -33,6 +34,12 @@ def read_img(self, file_path):
         
         return img
 
+    def read_img_stream(self, stream):
+        with Image.open(io.BytesIO(stream)) as img:
+            img = np.array(img.convert('RGB'))[:, :, :3]
+
+        return img
+
     def face_detection_pipeline(self, img):
         bboxes, kpss = self.face_detection_model.detect(img)
         return bboxes, kpss
@@ -43,9 +50,9 @@ def ocr_pipeline(self, img):
         dt_results = list(zip(dt_boxes, rec_res))
         return dt_results
 
-    def parse_file(self, file_path):
+    def parse_file(self, file_stream):
         file_content = []
-        img = self.read_img(file_path)
+        img = self.read_img_stream(file_stream)
 
         face_detection_result, _ = self.face_detection_pipeline(img)
         ocr_pipeline_result = self.ocr_pipeline(img)
diff --git a/source/containers/document-pii-detection/parsers/parser.py b/source/containers/document-pii-detection/parsers/parser.py
index e0f36edc..9f1725a1 100644
--- a/source/containers/document-pii-detection/parsers/parser.py
+++ b/source/containers/document-pii-detection/parsers/parser.py
@@ -2,6 +2,7 @@
 import magic
 import re
 from tempfile import NamedTemporaryFile
+import boto3
 
 def merge_strings(substrings, delimiter='.', max_length=128, truncate_buffer=False):
     """
@@ -48,7 +49,7 @@ def parse_file(self, file_path, **kwargs):
     def load_content(self, bucket, object_key):
         """
         Downloads the file from S3.
-        """
+
         # Create a temporary file
         with NamedTemporaryFile() as temp_file:
             self.s3_client.download_file(Bucket=bucket, Key=object_key, Filename=temp_file.name)
@@ -60,6 +61,18 @@ def load_content(self, bucket, object_key):
                 print(f"Failed to parse file {object_key}. Error: {e}")
                 file_content = []
             processed_content = self.postprocess_content(file_content)
+        """
+        # begin download obj into memory
+        s3 = boto3.resource('s3', region_name='cn-northwest-1')
+        obj = s3.Object(bucket, object_key).get()
+
+        try:
+            file_content = self.parse_file(obj['Body'].read())
+        except Exception as e:
+            print(f"Failed to parse file {object_key}. Error: {e}")
+            file_content = []
+        processed_content = self.postprocess_content(file_content)
+        # end download obj into memory
 
         return processed_content
     
diff --git a/source/containers/document-pii-detection/parsers/pdf_parser.py b/source/containers/document-pii-detection/parsers/pdf_parser.py
index e1a07bb1..10dc4fb1 100644
--- a/source/containers/document-pii-detection/parsers/pdf_parser.py
+++ b/source/containers/document-pii-detection/parsers/pdf_parser.py
@@ -2,6 +2,7 @@
 import os
 import boto3
 from pypdf import PdfReader
+from io import BytesIO
 
 from .parser import BaseParser
 
@@ -10,13 +11,13 @@ def __init__(self, s3_client):
         super().__init__(s3_client=s3_client)
     
 
-    def parse_file(self, pdf_path):
+    def parse_file(self, pdf_stream):
         """
         Extracts text from a PDF file and returns a list of lines.
         """
 
         # Create a PDF reader object
-        pdf_reader = PdfReader(pdf_path)
+        pdf_reader = PdfReader(BytesIO(pdf_stream))
         file_content = []
 
         # Loop through each page in the PDF file
@@ -27,4 +28,5 @@ def parse_file(self, pdf_path):
             page_content = page.extract_text()
             file_content.append(page_content)
 
+        # print(file_content)
         return file_content
diff --git a/source/containers/document-pii-detection/parsers/txt_parser.py b/source/containers/document-pii-detection/parsers/txt_parser.py
index 68d663cc..483c8180 100644
--- a/source/containers/document-pii-detection/parsers/txt_parser.py
+++ b/source/containers/document-pii-detection/parsers/txt_parser.py
@@ -2,24 +2,26 @@
 import os
 from .parser import BaseParser
 import quopri
+import chardet
 
 class TxtParser(BaseParser):
     def __init__(self, s3_client):
         super().__init__(s3_client=s3_client)
 
-    def parse_file(self, txt_path):
+    def parse_file(self, txt_stream):
         """
         Extracts text from a TXT file and returns a list of lines.
         """
 
-        file_encoding = self.get_encoding(txt_path)
+        # file_encoding = self.get_encoding(txt_stream)
 
         # Read the file
-        with open(txt_path, 'rb') as file:
-            file_content_byte = file.read()
-        
-        file_content = file_content_byte.decode(file_encoding).encode('utf-8').decode('utf-8', errors='ignore')
-
+        # with open(txt_stream, 'rb') as file:
+        #     file_content_byte = file.read()
+        result = chardet.detect(txt_stream)
+        encoding = result['encoding']
+        file_content = txt_stream.decode(encoding)
+        # print(file_content)
         # if file_encoding == 'us-ascii':
         #     file_content = quopri.decodestring(file_content).decode('utf-8', errors='ignore')
 
diff --git a/source/containers/document-pii-detection/requirements.txt b/source/containers/document-pii-detection/requirements.txt
index 02028d4b..dafcb040 100644
--- a/source/containers/document-pii-detection/requirements.txt
+++ b/source/containers/document-pii-detection/requirements.txt
@@ -1,20 +1,17 @@
-requests
-boto3
+boto3==1.34.63
 six==1.16.0
-opencv-python-headless==4.5.3.56
-numpy<=1.23.5
-onnxruntime
+numpy==1.26.4
+onnxruntime==1.17.1
 Pillow==10.2.0
-pyclipper==1.3.0
-Shapely==1.7.1
-base64image==0.5.1
-urllib3==1.26.18
+pyclipper==1.3.0.post5
+Shapely==2.0.3
 python-dateutil==2.8.2
-certifi==2023.7.22
-idna==2.10
-chardet==4.0.0
-pypdf==3.17.0
+chardet==5.2.0
+pypdf==4.1.0
 python-magic==0.4.27
-python-docx==0.8.11
-bs4==0.0.1
-pandas==1.5.3
\ No newline at end of file
+python-docx==1.1.0
+bs4==0.0.2
+pandas==2.2.1
+pyarrow==15.0.2
+psutil==5.9.8
+opencv-python-headless==4.9.0.80