如何在AWS Lambda上使用tabula阅读pdf？

“我知道我们必须下载 Java 才能运行，我在 IDE 上执行了该操作，并且成功了。但不知道如何在 AWS Lambda 上下载它。如果有人能帮助我，我将不胜感激。

我认为代码本身产生了我所期望的结果，但是，java正是我所需要的。

这是我收到的错误：

`

'[ERROR] JavaNotFoundError: javacommand is not found from this Python process.Please ensure Java is installed and PATH is set forjavaTraceback (most recent call last): File "/var/task/lambda_function.py", line 30, in lambda_handler tables = tabula.read_pdf(io.BytesIO(file_content), pages='all') File "/opt/python/tabula/io.py", line 425, in read_pdf output = _run(java_options, tabula_options, path, encoding) File "/opt/python/tabula/io.py", line 99, in _run raise JavaNotFoundError(JAVA_NOT_FOUND_ERROR)'

            import json
            import boto3
            import pandas as pd
            import io
            import re
            import tabula
            import numpy as np
            def f_remove_accents(old):
                """
            #    Removes common accent characters, lower form.
            #    Uses: regex.
                """
                new = old.lower()
                new = re.sub(r'[àáâãäå]', 'a', new)
                new = re.sub(r'[èéêë]', 'e', new)
                new = re.sub(r'[ìíîï]', 'i', new)
                new = re.sub(r'[òóôõö]', 'o', new)
                new = re.sub(r'[ùúûü]', 'u', new)
                new = re.sub(r'[ç]', 'c', new)
                return new
            def lambda_handler(event, context):
                s3 = boto3.client("s3")
                if event:
                    s3_records = event["Records"][0]
                    bucket_name = str(s3_records["s3"]["bucket"]["name"])
                    file_name = str(s3_records["s3"]["object"]["key"])
                    file_obj = s3.get_object(Bucket=bucket_name, Key=file_name)
                    file_content = file_obj["Body"].read()
            
                    tables = tabula.read_pdf(io.BytesIO(file_content), pages='all')
            
            
                    # Create an empty DataFrame to store all the modified tables
                    modified_tables = []
            
                    # Apply functions to the content of each table
                    for table in tables:
                        # Convert the DataFrame to a NumPy array
                        table_array = table.values.astype(str)
            
                        # Remove accents
                        remove_accents_func = np.vectorize(f_remove_accents)
                        table_array = remove_accents_func(table_array)
            
                        # Replace ';' with ' '
                        table_array = np.char.replace(table_array, ';', ' ')
            
                        # Convert to upper case
                        table_array = np.char.upper(table_array)
            
                        # Create a new DataFrame with the modified array
                        modified_table = pd.DataFrame(table_array, columns=table.columns)
            
                        # Append the modified table to the list
                        modified_tables.append(modified_table)
            
                    # Concatenate all the modified tables into a single DataFrame
                    final_df = pd.concat(modified_tables, ignore_index=True)
            
                    # Save the final DataFrame as a CSV file
                    name_of_return_file = f'{file_name[:-4]}_return.csv'
                    final_df.to_csv(name_of_return_file, sep=';', index=False)
            
                    # Read the CSV file content
                    with open(name_of_return_file, 'rb') as file:
                        csv_content = file.read()
            
                    # Upload the CSV file to the destination bucket
                    s3.put_object(Body=csv_content, Bucket='bucket-recebendo', Key=name_of_return_file)

这就是我如何在 Lambda 上建立并运行表格。我使用了Lambda的OCI能力来打包需求。

首先，我使用 VSCode 创建文件夹结构来放置 lambda 函数和 Docker 文件。

我创建了一个名为“lambda_function.py”的文件，其中包含上面列出的代码。我创建了一个 pip requests.txt 文件，其中包含以下库（您不应该使用它，而应该使用固定版本，我这样做只是为了速度）

我确实必须更改您的代码，并且我进行了以下更改（导入语句）

import json
import boto3
import pandas as pd
import io
import re
from tabula.io import read_pdf
import numpy as np
..
..

因为我收到错误“AttributeError: module 'tabula' has no attribute 'read_pdf'”

pandas
numpy
tabula-py
boto3

然后我创建了我的 Docker 文件（非常粗糙，没有优化，只是为了引导它并让它运行 - 你肯定想改进它）

FROM public.ecr.aws/lambda/python:3.10
COPY requirements.txt ${LAMBDA_TASK_ROOT}
COPY lambda_function.py ${LAMBDA_TASK_ROOT}
RUN yum install java-17-amazon-corretto-devel -y
RUN pip install --upgrade pip
RUN pip install --no-cache-dir -r requirements.txt
CMD [ "lambda_function.lambda_handler" ]

这就是我的布局：

├── Dockerfile
├── lambda_function.py
└── requirements.txt

然后，我运行以下命令在本地构建容器映像，然后将其上传到亚马逊 ecr（我创建了一个 ecr 存储库）

docker build -t lambda-tabula:1.0.0 . 
aws ecr get-login-password --region eu-west-1 | docker login --username AWS --password-stdin xxxx.dkr.ecr.eu-west-1.amazonaws.com
docker tag lambda-tabula:1.0.0 xxxx.dkr.ecr.eu-west-1.amazonaws.com/lambda-oci-demo:1.0.1
docker push xxxx.dkr.ecr.eu-west-1.amazonaws.com/lambda-oci-demo:1.0.1

我现在在 ECR 中有我的图像，我可以通过 arn 引用它（“xxxxx.dkr.ecr.eu-west-1.amazonaws.com/lambda-oci-demo:1.0.0”）

然后，我创建了一个新的 Lambda 函数，指定 OCI 图像，然后指向该图像。我为该函数创建了一个角色，该角色也将提供对此特定 S3 存储桶的权限。

您可以在以下位置获取更多详细信息文档在这里 https://docs.aws.amazon.com/lambda/latest/dg/python-image.html

这允许该函数运行，尽管由于当前函数想要写入只读文件系统而失败并出现不同的错误，因此我将 name_of_return_file = f'{file_name[:-4]}_return.csv' 更改为 name_of_return_file = f'/tmp/{file_name[:-4]}_return.csv'

一旦我解决了这个问题，效果就很好。这是更新后的代码

import json
import boto3
import pandas as pd
import io
import re
from tabula.io import read_pdf
import numpy as np
def f_remove_accents(old):
            new = old.lower()
            new = re.sub(r'[àáâãäå]', 'a', new)
            new = re.sub(r'[èéêë]', 'e', new)
            new = re.sub(r'[ìíîï]', 'i', new)
            new = re.sub(r'[òóôõö]', 'o', new)
            new = re.sub(r'[ùúûü]', 'u', new)
            new = re.sub(r'[ç]', 'c', new)
            return new
def lambda_handler(event, context):
            s3 = boto3.client("s3")
            if event:
                    #s3_records = event["Records"][0]
                    #bucket_name = str(s3_records["s3"]["bucket"]["name"])
                    #file_name = str(s3_records["s3"]["object"]["key"])
                    #file_obj = s3.get_object(Bucket=bucket_name, Key=file_name)
                    file_obj = s3.get_object(Bucket="tabula-demo", Key="invoice.pdf")
                    file_content = file_obj["Body"].read()
            
                    tables = read_pdf(io.BytesIO(file_content), pages='all')
            
            
                    # Create an empty DataFrame to store all the modified tables
                    modified_tables = []
            
                    # Apply functions to the content of each table
                    for table in tables:
                        # Convert the DataFrame to a NumPy array
                        table_array = table.values.astype(str)
            
                        # Remove accents
                        remove_accents_func = np.vectorize(f_remove_accents)
                        table_array = remove_accents_func(table_array)
            
                        # Replace ';' with ' '
                        table_array = np.char.replace(table_array, ';', ' ')
            
                        # Convert to upper case
                        table_array = np.char.upper(table_array)
            
                        # Create a new DataFrame with the modified array
                        modified_table = pd.DataFrame(table_array, columns=table.columns)
            
                        # Append the modified table to the list
                        modified_tables.append(modified_table)
            
                    # Concatenate all the modified tables into a single DataFrame
                    final_df = pd.concat(modified_tables, ignore_index=True)
            
                    # Save the final DataFrame as a CSV file
                    #name_of_return_file = f'{file_name[:-4]}_return.csv'
                    name_of_return_file = '/tmp/test_return.csv'
                    final_df.to_csv(name_of_return_file, sep=';', index=False)
            
                    # Read the CSV file content
                    with open(name_of_return_file, 'rb') as file:
                        csv_content = file.read()
            
                    # Upload the CSV file to the destination bucket
                    s3.put_object(Body=csv_content, Bucket='094459-lambda-libs', Key=name_of_return_file)

笔记！我必须修改上面的代码，因为我对文件进行了硬编码，因为我不知道您的输入文件是什么。