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import os | |
import streamlit as st | |
from dotenv import load_dotenv | |
from groq import Agent | |
from phi.model.groq import Groq | |
from phi.tools.duckduckgo import DuckDuckGo | |
from phi.tools.yfinance import YFinanceTools | |
# Load environment variables | |
load_dotenv() | |
# Retrieve API keys from the environment | |
deepseek_api_key = os.getenv("GROQ_DEEPSEEK_API_KEY") | |
qwen_api_key = os.getenv("GROQ_QWEN_API_KEY") | |
# Debugging API key loading | |
if not deepseek_api_key or not qwen_api_key: | |
st.error("Missing API keys. Please set them in Hugging Face Secrets.") | |
st.stop() | |
# Define Web Agent | |
web_agent = Agent( | |
name="Web Agent", | |
model=Groq(id="qwen-2.5-coder-32b", api_key=qwen_api_key), | |
tools=[DuckDuckGo()], | |
instructions=["Always include sources"], | |
show_tool_calls=True, | |
markdown=True, | |
) | |
# Define Finance Agent | |
finance_agent = Agent( | |
name="Finance Agent", | |
role="Get financial data", | |
model=Groq(id="qwen-2.5-coder-32b", api_key=qwen_api_key), | |
tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, company_info=True)], | |
instructions=["Use tables to display data"], | |
show_tool_calls=True, | |
markdown=True, | |
) | |
# Create agent team | |
agent_team = Agent( | |
model=Groq(id="deepseek-r1-distill-llama-70b", api_key=deepseek_api_key), | |
team=[web_agent, finance_agent], | |
instructions=["Always include sources", "Use tables to display data"], | |
show_tool_calls=True, | |
markdown=True, | |
) | |
# Streamlit UI | |
st.set_page_config(page_title="AI Chat Assistant", layout="wide") | |
st.title("🤖 AI Chat Assistant") | |
# User input | |
if prompt := st.chat_input("Ask me anything..."): | |
with st.chat_message("user"): | |
st.markdown(prompt) | |
with st.chat_message("assistant"): | |
response_container = st.empty() | |
response_text = "" | |
# Check available response method | |
if hasattr(agent_team, "respond") and callable(agent_team.respond): | |
try: | |
response_text = agent_team.respond(prompt) or "No response received." | |
response_container.markdown(response_text) | |
except Exception as e: | |
error_message = f"Error: {str(e)}" | |
st.error(error_message) | |
response_text = error_message | |
else: | |
error_message = "Error: Agent does not support responses." | |
st.error(error_message) | |
response_text = error_message | |