Create app.py

app.py

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+import os
+from dotenv import load_dotenv
+from agno.agent import Agent
+from agno.models.nebius import Nebius
+from agno.tools.yfinance import YFinanceTools
+from agno.tools.duckduckgo import DuckDuckGoTools
+import gradio as gr
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Create the AI finance agent
+agent = Agent(
+    name="AI Finance Agent",
+    model=Nebius(
+        id="meta-llama/Llama-3.3-70B-Instruct",
+        api_key=os.getenv("NEBIUS_API_KEY")
+    ),
+    tools=[
+        DuckDuckGoTools(),
+        YFinanceTools(
+            stock_price=True,
+            analyst_recommendations=True,
+            stock_fundamentals=True
+        )
+    ],
+    instructions=[
+        "Always use tables to display financial/numerical data.",
+        "For text data use bullet points and small paragraphs."
+    ],
+    show_tool_calls=True,
+    markdown=True,
+)
+
+def respond(user_query: str) -> str:
+    """Run the agent on the user's query and return markdown."""
+    try:
+        return agent.run(user_query)
+    except Exception as e:
+        return f"Error: {e}"
+
+# Build a Gradio interface
+interface = gr.Interface(
+    fn=respond,
+    inputs=gr.components.Textbox(lines=2, placeholder="Ask a finance question..."),
+    outputs=gr.components.Markdown(),
+    title="Finance Agent",
+    description="Ask questions about stocks, analyst recommendations, fundamentals and recent news."
+)
+
+# If running locally, launch Gradio
+if __name__ == "__main__":
+    interface.launch()