app.py

import streamlit as st
from langchain_groq import ChatGroq
import yfinance as yf

# Initialize the ChatGroq model
llm = ChatGroq(model_name="Llama3-8b-8192", api_key="groq_api_key")

# Set the page configuration for Streamlit
st.set_page_config(page_title="Stock Chatbot", page_icon="📈")

# Custom CSS for dark blue theme
st.markdown(
    """
    <style>
    .stApp {
        background-color: #1e1e2f;
        color: white;
    }
    .stTextInput>div>input {
        background-color: #2e2e3e;
        color: white;
    }
    .stButton>button {
        background-color: #007bff;
        color: white;
    }
    </style>
    """,
    unsafe_allow_html=True,
)

# 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.write(message["content"])

# 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
        stock_data = yf.Ticker(company_name).info
        
        response = f"Here is the data for {company_name}:\n"
        response += f"Current Price: {stock_data.get('currentPrice', 'N/A')}\n"
        response += f"Market Cap: {stock_data.get('marketCap', 'N/A')}\n"
        response += f"PE Ratio: {stock_data.get('trailingPE', 'N/A')}\n"
        response += f"Dividend Yield: {stock_data.get('dividendYield', 'N/A')}\n"
        
        # Add more insights or advice logic here if needed

    else:
        response = llm.invoke(prompt)  # Use the LLM for general questions

    # Display assistant response in chat message container
    with st.chat_message("assistant"):
        st.write(response)

    # Add assistant response to chat history
    st.session_state.messages.append({"role": "assistant", "content": response})