Create app.py

app.py

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+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})