Amazon Bedrock Claude 帮助制造业实现产品缺陷识别和分析

Human: 你是一个缺陷检测机器人，需要根据缺陷编码或者描述提供原因和处理方案，参考内容如下
“缺陷信息的描述和解释xxxxxxx”
Human：因为化学成分引起的问题处理措施有哪些？ 

import json
import os
import sys
import base64
import io


import boto3
import botocore
from PIL import Image
# module_path = "."
sys.path.append('./utils') 
from utils import bedrock
from botocore.exceptions import ClientError
import logging
logger = logging.getLogger("rewrite_news")
# import bedrock

# ---- ⚠️ Un-comment and edit the below lines as needed for your AWS setup ⚠️ ----

# os.environ["AWS_DEFAULT_REGION"] = "<REGION_NAME>"  # E.g. "us-east-1"
# os.environ["AWS_PROFILE"] = "<YOUR_PROFILE>"
# os.environ["BEDROCK_ASSUME_ROLE"] = "<YOUR_ROLE_ARN>"  # E.g. "arn:aws:..."


boto3_bedrock = bedrock.get_bedrock_client(
    assumed_role=os.environ.get("BEDROCK_ASSUME_ROLE", None),
    region=os.environ.get("AWS_DEFAULT_REGION", None)
)

boto3_bedrock = bedrock.get_bedrock_client(
    assumed_role=os.environ.get("BEDROCK_ASSUME_ROLE", None),
    region=os.environ.get("AWS_DEFAULT_REGION", None)
)

def invoke_claude_3_with_text(prompt):
    """
    Invokes Anthropic Claude 3 Sonnet to run an inference using the input
    provided in the request body.

    :param prompt: The prompt that you want Claude 3 to complete.
    :return: Inference response from the model.
    """

    # Initialize the Amazon Bedrock runtime client
    client = boto3.client(
        service_name="bedrock-runtime", region_name="us-west-2"
    )

    # Invoke Claude 3 with the text prompt
    model_id = "anthropic.claude-3-sonnet-20240229-v1:0"

    try:
        response = client.invoke_model(
            modelId=model_id,
            body=json.dumps(
                {
                    "anthropic_version": "bedrock-2023-05-31",
                    "max_tokens": 4096,
                    "messages": [
                        {
                            "role": "user",
                            "content": [{"type": "text", "text": prompt}],
                        }
                    ],
                }
            ),
        )

        # Process and print the response
        result = json.loads(response.get("body").read())
        input_tokens = result["usage"]["input_tokens"]
        output_tokens = result["usage"]["output_tokens"]
        output_list = result.get("content", [])

        print("Invocation details:")
        print(f"- The input length is {input_tokens} tokens.")
        print(f"- The output length is {output_tokens} tokens.")

        print(f"- The model returned {len(output_list)} response(s):")
        for output in output_list:
            print(output["text"])

        return result

    except ClientError as err:
        logger.error(
            "Couldn't invoke Claude 3 Sonnet. Here's why: %s: %s",
            err.response["Error"]["Code"],
            err.response["Error"]["Message"],
        )
        raise

prompt = """ Human: 你是一个缺陷检测机器人，需要根据缺陷编码或者描述提供原因和处理方案，
参考内容如下 缺陷信息的描述和解释xxxxxx """

import time

start_time = time.time()

invoke_claude_3_with_text(prompt)
end_time = time.time()
print('Claude3 sonnet Duration: {:.3f} seconds'.format(end_time - start_time))

#Copyright 2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-custom-labels-developer-guide/blob/master/LICENSE-SAMPLECODE.)

import boto3
import io
from PIL import Image, ImageDraw, ExifTags, ImageColor, ImageFont

def show_custom_labels(model,bucket,photo, min_confidence):
    client=boto3.client('rekognition')

    #Call DetectCustomLabels
    response = client.detect_custom_labels(Image={'S3Object': {'Bucket': bucket, 'Name': photo}},
        MinConfidence=min_confidence,
        ProjectVersionArn=model)
    return response['CustomLabels']


lableeee=[]
def main():

    bucket='custom-labels-console-us-west-2-89e6059fcc'
    # photo='gongxiang/inference/1102-9 Porosity - secundary - micro detail shape - gray iron.png'
    photo='gongxiang/inference/1101-77.png'
  
    model='arn:aws:rekognition:us-west-2:517141035927:project/gongxiang-defect-detection/version/gongxiang-defect-detection.2023-12-07T14.49.13/1701931754280'
    min_confidence=85

    customLabels=show_custom_labels(model,bucket,photo, min_confidence)
    for customLabel in customLabels:
        print('当前图片缺陷分类为： ' + str(customLabel['Name']))
        print('置信度： ' + str(customLabel['Confidence']))
        lableeee.append(str(customLabel['Name']))



if __name__ == "__main__":
    main()

request: body 中存储图片 base64
response：
{
"filename": "CVoE12I5J2e9tf32mcxeVEurPVNxrCM2ouD5eTvHvnI25dsDJL.png"
}

request: https://xxxx/defect_detect?CVoE12I5J2e9tf32mcxeVEurPVNxrCM2ouD5eTvHvnI25dsDJL.png

response: [{"Name": "1101", "Confidence": 99.47200012207031}]

request:
http://defect-analysis-alb-xxxx..elb.amazonaws.com?defect_code=1101
response:
{"message":" xxx。"}

request:
http://defect-analysis-alb-xxxxx.elb.amazonaws.com/?question=请帮我总结一些1101故障

response:
{"message":" xxx。"}