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import os
from crewai import Agent, Task, Crew
from langchain_groq import ChatGroq
import streamlit as st
# Initialize the LLM for the Marketing Research Agent
llm = ChatGroq(
groq_api_key="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",
model_name="llama3-70b-8192", # Replace with the actual Marketing Research model name
)
# Define the Marketing Research Agent with a specific goal
marketing_agent = Agent(
role='Marketing Research Agent',
goal='Provide in-depth insights and analysis on marketing trends, strategies, consumer behavior, and market research.',
backstory=(
"You are a Marketing Research Agent, skilled in gathering and analyzing information on market trends, "
"consumer behavior, competitive landscape, and marketing strategies. Your role is to answer marketing-related questions "
"with a detailed, data-driven approach, and strictly limit responses to marketing research only."
),
verbose=True,
llm=llm,
)
def process_question_with_agent(question):
# Describe the task for the agent
task_description = f"Research and provide a detailed answer to the marketing question: '{question}'"
# Define the task for the agent to generate a response to the question
research_task = Task(
description=task_description,
agent=marketing_agent,
human_input=False,
expected_output="Answer related to marketing research" # Placeholder for expected output
)
# Instantiate the crew with the defined agent and task
crew = Crew(
agents=[marketing_agent],
tasks=[research_task],
verbose=2,
)
# Get the crew to work on the task and return the result
result = crew.kickoff()
return result
# Set the title of your app with Markdown
st.markdown("<h1 style='text-align: center;'>Marketing Research Chatbot</h1>", unsafe_allow_html=True)
# Initialize chat history
if "messages" not in st.session_state:
st.session_state.messages = []
# Display chat messages from history on app rerun
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
# React to user input
if prompt := st.chat_input("Ask a marketing research question:"):
# Display user message in chat message container
st.chat_message("user").markdown(prompt)
# Add user message to chat history
st.session_state.messages.append({"role": "user", "content": prompt})
# Get the response from the Marketing Research Agent
with st.spinner("Processing..."):
response = process_question_with_agent(prompt)
# Display assistant response in chat message container
with st.chat_message("assistant"):
st.markdown(response)
# Add assistant response to chat history
st.session_state.messages.append({"role": "assistant", "content": response})
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