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import streamlit as st
from langchain_groq import ChatGroq
import yfinance as yf
# Initialize the ChatGroq model using the secret API key
llm = ChatGroq(model_name="Llama3-8b-8192", api_key=st.secrets['groq_api_key'])
# Initialize chat history in session state
if "messages" not in st.session_state:
st.session_state.messages = [{"role": "assistant", "content": "Hello! How can I assist you with stock information today?"}]
# Display chat messages from history
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"], unsafe_allow_html=True)
# Accept user input
if prompt := st.chat_input("Ask me about stocks..."):
# Display user message in chat message container
with st.chat_message("user"):
st.write(prompt)
# Add user message to chat history
st.session_state.messages.append({"role": "user", "content": prompt})
# Fetch stock data or generate response based on user input
if "invest" in prompt.lower() or "should I invest" in prompt.lower():
company_name = prompt.split()[-1] # Assuming the last word is the ticker symbol or company name
try:
stock_data = yf.Ticker(company_name).info
# Check if stock_data contains valid information
if 'currentPrice' in stock_data and stock_data['currentPrice'] is not None:
# Extract relevant information into a structured format
stock_info = {
"Company": stock_data.get("longName", "N/A"),
"Current Price": stock_data.get("currentPrice", "N/A"),
"Market Cap": stock_data.get("marketCap", "N/A"),
"PE Ratio": stock_data.get("trailingPE", "N/A"),
"Dividend Yield": stock_data.get("dividendYield", "N/A"),
"52 Week High": stock_data.get("fiftyTwoWeekHigh", "N/A"),
"52 Week Low": stock_data.get("fiftyTwoWeekLow", "N/A"),
"Sector": stock_data.get("sector", "N/A"),
"Industry": stock_data.get("industry", "N/A")
}
# Prepare response string with line breaks for readability
response = f"Here is the data for {company_name}:\n"
for key, value in stock_info.items():
response += f"{key}: {value}\n"
# Simple investment recommendation logic (this can be improved)
if stock_info["PE Ratio"] != "N/A" and float(stock_info["PE Ratio"]) < 20: # Example condition for recommendation
response += "\n**Recommendation:** Yes, consider investing!"
else:
response += "\n**Recommendation:** No, it might not be a good time to invest."
else:
response = f"Sorry, I couldn't find valid data for {company_name}. Please check the ticker symbol."
except Exception as e:
response = f"An error occurred while fetching data: {str(e)}"
else:
try:
# Use the LLM for general questions or topics not related to stocks
response = llm.invoke(prompt)
except Exception as e:
response = f"An error occurred while processing your request: {str(e)}"
# Display assistant response in chat message container with line breaks for readability
with st.chat_message("assistant"):
st.markdown(response.replace("\n", "<br>"), unsafe_allow_html=True)
# Add assistant response to chat history, preserving line breaks
st.session_state.messages.append({"role": "assistant", "content": response.replace("\n", "<br>")})
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