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# Import necessary libraries to use their functions
import os
import streamlit as st
from openai import OpenAI
# Get the API key from the environment settings
from dotenv import load_dotenv
# Load environment variables from .env file
load_dotenv()
# Now retrieve the API key
api_key_nvidia = os.getenv("api_key_nvidia")
api_key_nvidia = os.environ.get("api_key_nvidia")
# If there is no API key, show an error message and stop the program
if not api_key_nvidia:
st.error("NVIDIA API key not found. Please set the `api_key_nvidia` environment variable.")
st.stop()
# Add custom design for a finance theme
st.markdown("""
<style>
/* Set background color for the main section */
.main {
background-color: #f4f9f9; /* Light teal for a professional look */
color: #000000; /* Black text for readability */
}
/* Set background color for the sidebar */
.sidebar .sidebar-content {
background-color: #d1e7dd; /* Slightly darker teal */
}
/* Set text color for input fields */
.stTextInput textarea {
color: #000000 !important;
}
/* Change styles for dropdown menu */
.stSelectbox div[data-baseweb="select"] {
color: black !important;
background-color: #d1e7dd !important;
}
/* Change color of dropdown icons */
.stSelectbox svg {
fill: black !important;
}
/* Change background and text color for dropdown options */
.stSelectbox option {
background-color: #d1e7dd !important;
color: black !important;
}
/* Change background and text color for dropdown items */
div[role="listbox"] div {
background-color: #d1e7dd !important;
color: black !important;
}
</style>
""", unsafe_allow_html=True)
# Set the title of the app
st.title("💰 Financial Assistant")
# Add a small description under the title
st.caption("🌟 Your AI-Powered Financial Advisor")
# Create the sidebar with options
with st.sidebar:
# Add a dividing line
st.divider()
# Display a section for assistant features
st.markdown("### Assistant Capabilities")
st.markdown("""
- 📊 Investment Analysis
- 💳 Budgeting Advice
- 🏦 Loan Guidance
- 💡 Retirement Planning
""")
# Add another dividing line
st.divider()
# Show a small footer message
st.markdown("Built with NVIDIA API | LangChain ")
st.markdown("Created with ❤ by Nikhil Kumar")
# Start the AI client using the API key
client = OpenAI(
base_url="https://integrate.api.nvidia.com/v1",
api_key=api_key_nvidia
)
# Define a message that tells the AI how to respond
system_prompt_template = (
"I am an expert AI financial assistant. Provide accurate, concise, and empathetic responses "
"to user queries related to investments, budgeting, loans, retirement planning, and other financial matters. "
"Always respond in English."
)
# Initialize chat history if it doesn't exist
if "message_log" not in st.session_state:
st.session_state.message_log = [
{"role": "assistant", "content": "Hi! I'm your Finance Assistant. How can I assist you today? 💰"}
]
# Create a container to display chat messages
chat_container = st.container()
# Show chat messages inside the container
with chat_container:
for message in st.session_state.message_log:
with st.chat_message(message["role"]):
st.markdown(message["content"])
# Input field for user to type questions
user_query = st.chat_input("Type your finance-related question here...")
# Function to get a response from the AI
def generate_ai_response(messages):
"""
Sends the conversation to the AI model and processes the response.
"""
completion = client.chat.completions.create(
model="deepseek-ai/deepseek-r1",
messages=messages,
temperature=0.5, # Controls randomness of responses
top_p=0.5, # Helps control diversity of responses
max_tokens=1000, # Maximum length of response
stream=True # Enables streaming of responses
)
# Process the AI response piece by piece
response = ""
for chunk in completion:
content = chunk.choices[0].delta.content or ""
response += content
return response
# Handle user input and generate AI responses
if user_query:
# Save the user's message in chat history
st.session_state.message_log.append({"role": "user", "content": user_query})
# Create a list of messages to send to AI
messages = [
{"role": "system", "content": system_prompt_template}, # First message that tells AI how to behave
]
# Add all previous messages to the conversation
for msg in st.session_state.message_log:
role = msg["role"]
if role == "ai":
role = "assistant"
messages.append({"role": role, "content": msg["content"]})
# Show a loading spinner while AI is thinking
with st.spinner("🧠 Processing..."):
# Get the AI response
ai_response = generate_ai_response(messages)
# Save the AI's response in chat history
st.session_state.message_log.append({"role": "assistant", "content": ai_response})
# Refresh the page to show the new chat messages
st.rerun() |