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from crewai import Agent
import os
from dotenv import load_dotenv
from langchain_google_genai import ChatGoogleGenerativeAI
from tools import tool
import smtplib
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
load_dotenv()
import asyncio
try:
loop = asyncio.get_running_loop()
except RuntimeError:
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
# Defining the base llm model
llm = ChatGoogleGenerativeAI(
model="gemini-1.5-flash",
google_api_key=os.environ.get("GOOGLE_API_KEY"),
temperature=0.5,
verbose=True
)
def send_email(to_email, subject, body):
from_email = os.environ.get("FROM_EMAIL")
password = os.environ.get("EMAIL_PASSWORD")
smtp_server = os.environ.get("SMTP_SERVER")
smtp_port = int(os.environ.get("SMTP_PORT"))
# Create the email content
msg = MIMEMultipart()
msg['From'] = from_email
msg['To'] = to_email
msg['Subject'] = subject
msg.attach(MIMEText(body, 'plain'))
try:
# Create a secure SSL context and log in to the email server
with smtplib.SMTP(smtp_server, smtp_port) as server:
server.starttls() # Upgrade to a secure connection
server.login(from_email, password)
server.sendmail(from_email, to_email, msg.as_string())
print("Email sent successfully.")
except Exception as e:
print(f"Failed to send email: {e}")
async def generate_report_and_send_email(report, email):
subject = "Market Research & Precaution Report"
body = f"Here is your report:\n\n{report}"
print("Sending email to:", email)
send_email(email, subject, body)
# Define the agents
news_research_agent = Agent(
role="News Research and Summarization Agent",
goal="Research and summarize the top news article related to {input_text}.",
verbose=True,
memory=True,
backstory=("You are a News Research and Summarization Agent responsible for gathering news articles "
"related to user input. Your goal is to summarize the top article in four sentences."),
tools=[tool],
llm=llm,
allow_delegation=False
)
# 2. Precaution Recommendation Agent
precaution_agent = Agent(
role="Precaution Recommendation Agent",
goal="Provide three precautionary steps based on the summary of the top news article.",
verbose=True,
memory=True,
backstory=("You are a Precaution Recommendation Agent responsible for analyzing the summary of a news article "
"and generating three precautionary steps to mitigate any potential risks."),
tools=[tool],
llm=llm,
allow_delegation=False
)
# 3. Comprehensive Report Generation Agent
report_generation_agent = Agent(
role="Comprehensive Report Generation Agent",
goal="Create a comprehensive report combining the news summary and precautionary steps, then send it via email.",
verbose=True,
memory=True,
backstory=("You are a Comprehensive Report Generation Agent responsible for compiling the summary from the News Research Agent "
"and the precautionary steps from the Precaution Recommendation Agent into a detailed report."),
tools=[tool],
llm=llm,
allow_delegation=False
) |