Update app.py

app.py

@@ -1,82 +1,76 @@
-import os
-import streamlit as st
-from dotenv import load_dotenv
-from phi.agent 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:
-    raise ValueError("Missing API keys. Check your .env file.")
-print("DeepSeek API Key Loaded")
-print("Qwen API Key Loaded")
-
-# Define the Web Agent using Groq's QWEN model
-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 the Finance Agent using Groq's DeepSeek model
-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,
-)
-
-# Combine agents into a 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,
-)
-
-# Debugging agent_team initialization
-if not agent_team:
-    raise ValueError("Agent team failed to initialize.")
-print("Agent team initialized successfully")
-
-# 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 = ""
-
-        # Ensure agent_team has a valid method to generate responses
-        if hasattr(agent_team, "respond") and callable(agent_team.respond):
-            try:
-                response_text = web_agent.respond(prompt)  # Try this instead
-                response_container.markdown(response_text if response_text else "No response received.")
-            except Exception as e:
-                error_message = f"Error during response: {str(e)}"
-                print(error_message)
-                response_container.markdown(error_message)
-        else:
-            error_message = "Error: Agent does not support valid response methods."
-            print(error_message)
-            response_container.markdown(error_message)
+import os
+import streamlit as st
+from dotenv import load_dotenv
+from phi.agent 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