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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>")}) | |