Spaces:
Sleeping
Sleeping
File size: 2,416 Bytes
2285a24 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 |
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})
|