Spaces:
Running
Running
File size: 2,400 Bytes
7303dec 11f1ed0 7303dec |
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 73 74 75 76 77 |
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
|