File size: 1,567 Bytes
db4810d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 |
"""
Automated news analysis and sentiment scoring using Bedrock.
@dev Ensure AWS environment variables are set correctly for Bedrock access.
"""
import os
import sys
from langchain_aws import ChatBedrock
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import argparse
import asyncio
from browser_use import Agent
from browser_use.browser.browser import Browser, BrowserConfig
from browser_use.controller.service import Controller
def get_llm():
return ChatBedrock(
model_id="us.anthropic.claude-3-5-sonnet-20241022-v2:0",
temperature=0.0,
max_tokens=None,
)
# Define the task for the agent
task = (
"Visit cnn.com, navigate to the 'World News' section, and identify the latest headline. "
"Open the first article and summarize its content in 3-4 sentences. "
"Additionally, analyze the sentiment of the article (positive, neutral, or negative) "
"and provide a confidence score for the sentiment. Present the result in a tabular format."
)
parser = argparse.ArgumentParser()
parser.add_argument('--query', type=str, help='The query for the agent to execute', default=task)
args = parser.parse_args()
llm = get_llm()
browser = Browser(
config=BrowserConfig(
# chrome_instance_path='/Applications/Google Chrome.app/Contents/MacOS/Google Chrome',
)
)
agent = Agent(
task=args.query, llm=llm, controller=Controller(), browser=browser, validate_output=True,
)
async def main():
await agent.run(max_steps=30)
await browser.close()
asyncio.run(main())
|