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agents.py

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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
+)

app.py

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+import streamlit as st
+import re
+import sys
+from crewai import Crew, Process
+import os
+import asyncio 
+from agents import (
+    news_research_agent,
+    precaution_agent,
+    report_generation_agent,
+    generate_report_and_send_email  
+)
+from tasks import (
+    news_research_task,
+    precaution_task,
+    report_generation_task,
+)
+
+# Used to stream sys output on the Streamlit frontend
+class StreamToContainer:
+    def __init__(self, container):
+        self.container = container
+        self.buffer = []
+        self.colors = ['red', 'green', 'blue', 'orange']
+        self.color_index = 0
+
+    def write(self, data):
+        # Filter out ANSI escape codes using a regular expression
+        cleaned_data = re.sub(r'\x1B\[[0-9;]*[mK]', '', data)
+
+        # Check if the text contains the specified phrase and apply color
+        if "Entering new CrewAgentExecutor chain" in cleaned_data:
+            self.color_index = (self.color_index + 1) % len(self.colors)
+            cleaned_data = cleaned_data.replace(
+                "Entering new CrewAgentExecutor chain",
+                f":{self.colors[self.color_index]}[Entering new CrewAgentExecutor chain]",
+            )
+
+        # Apply colors to agent names
+        for agent_name in ["News Research and Summarization Agent", 
+                           "Precaution Recommendation Agent", 
+                           "Comprehensive Report Generation Agent"]:
+            if agent_name in cleaned_data:
+                cleaned_data = cleaned_data.replace(agent_name, f":{self.colors[self.color_index]}[{agent_name}]")
+
+        if "Finished chain." in cleaned_data:
+            cleaned_data = cleaned_data.replace("Finished chain.", f":{self.colors[self.color_index]}[Finished chain.]")
+
+        self.buffer.append(cleaned_data)
+        if "\n" in data:
+            self.container.markdown(''.join(self.buffer), unsafe_allow_html=True)
+            self.buffer = []
+
+# Streamlit UI
+st.header("News Summarization & Precaution Recommendation System")
+st.subheader("Generate a comprehensive report based on news articles!", divider="rainbow", anchor=False)
+
+# User input form
+with st.form("form"):
+    input_text = st.text_input("Enter a topic or keyword", key="input_text")
+    email = st.text_input("Enter your email address", key="email")
+    submitted = st.form_submit_button("Submit")
+
+# Process the submission
+if submitted:
+    with st.status("🤖 **Agents at work...**", expanded=True, state="running") as status:
+        with st.container(height=300):
+            sys.stdout = StreamToContainer(st)
+
+            # Defining the crew comprising of different agents
+            crew = Crew(
+                agents=[news_research_agent, precaution_agent, report_generation_agent],
+                tasks=[news_research_task, precaution_task, report_generation_task],
+                process=Process.sequential,
+                verbose=True
+            )
+            result = crew.kickoff(inputs={"input_text": input_text, "email": email})
+
+        status.update(label="✅ Your Report is ready", state="complete", expanded=False)
+    
+    st.subheader("Comprehensive Report is ready!", anchor=False, divider="rainbow")
+    asyncio.run(generate_report_and_send_email(result, email))
+    st.markdown(result)
+    
+
+    # Enable file download
+    st.download_button(
+        label="Download Report",
+        data=result,  
+        file_name=f"{input_text}_News_Report.txt",  
+        mime="text/plain",  
+    )

requirements.txt

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+load_dotenv
+streamlit
+crewai
+langchain_google_genai
+langchain_openai
+crewai_tools
+smtplib

tasks.py

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+from crewai import Task
+from tools import tool
+from agents import (
+    news_research_agent,
+    precaution_agent,
+    report_generation_agent,
+)
+
+# 1. News Research and Summarization Task
+news_research_task = Task(
+    description="Research and summarize the top news article related to {input_text}.",
+    expected_output="A four-sentence summary of the top news article.",
+    tools=[tool],
+    agent=news_research_agent,
+)
+
+# 2. Precaution Recommendation Task
+precaution_task = Task(
+    description="Generate three precautionary steps based on the summary of the news article.",
+    expected_output="Three precautionary steps to mitigate potential risks.",
+    tools=[tool],
+    agent=precaution_agent,
+)
+
+# 3. Comprehensive Report Generation Task
+report_generation_task = Task(
+    description="Compile the news summary and precautionary steps into a comprehensive report and send it to {email}.",
+    expected_output="A detailed report containing the news summary and precautionary steps, sent to the provided email.",
+    tools=[tool],
+    agent=report_generation_agent,
+    async_execution=False,  # This can be adjusted based on your needs
+)

tools.py

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+from dotenv import load_dotenv
+from crewai_tools import SerperDevTool
+import os
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Set the API key for Serper (Google Search Tool)
+os.environ["SERPER_API_KEY"] = os.environ.get("SERPER_API_KEY")
+
+# Initialize the SerperDevTool for Google searches
+tool = SerperDevTool()