Added source code.

app.py

@@ -1,64 +1,94 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+from agno.agent import Agent
+from agno.models.openai import OpenAIChat
+from agno.tools.decorator import tool
+from datetime import datetime, timedelta
+from dotenv import load_dotenv
+load_dotenv()
+
+# ✅ Tool definition
+@tool(name="treasury_data", description="Returns detailed account balances, FX exposure, and liabilities")
+def get_treasury_data(input: str) -> str:
+    today = datetime.now().strftime("%Y-%m-%d")
+    due_date = (datetime.now() + timedelta(days=5)).strftime("%Y-%m-%d")
+    return f"""
+    As of {today}, here is the treasury snapshot:
+
+    ✅ Cash Balances:
+    - USD Operating Account: $2,800,000
+    - EUR Revenue Account: €1,500,000
+    - GBP Payroll Account: £600,000
+
+    📉 FX Market Rates:
+    - EUR/USD: 1.08
+    - GBP/USD: 1.27
+
+    🧾 Upcoming Liabilities:
+    - USD Vendor Payments: $2,200,000 due on {due_date}
+    - EUR Convertible Bond Maturity: €900,000 due on {due_date}
+    - GBP Payroll Run: £500,000 due on {due_date}
+
+    📊 Policy Thresholds:
+    - Minimum USD liquidity buffer: $500,000
+    - FX hedge threshold for EUR: 70% exposure
+    """
+
+# ✅ Agent definition
+agent = Agent(
+    model=OpenAIChat(id="gpt-4o"),
+    tools=[get_treasury_data],
+    instructions=[
+        "You are a Treasury Analyst AI.",
+        "Analyze liquidity and FX exposure.",
+        "Recommend actions such as FX hedging, internal fund transfers, or delay of liabilities.",
+        "Always ensure minimum liquidity buffers are met.",
+        "Convert foreign currency exposures to USD for a consolidated view.",
+        "Provide your analysis in bullet points with numbers.",
     ],
+    markdown=True,
 )
 
+# ✅ Chat function
+def ask_agent(messages, history):
+    print("====== DEBUG: ask_agent START ======")
+    print("📥 Incoming messages:", messages)
+    print("📚 Chat history:", history)
+
+    if isinstance(messages, str):
+        print("🧪 Detected string instead of message list. Wrapping as message.")
+        messages = [{"role": "user", "content": messages}]
+
+    if not isinstance(messages, list) or not messages:
+        print("⚠️ Invalid or empty messages list.")
+        return {"role": "assistant", "content": "Hi! How can I help you with treasury insights today?"}
+
+    try:
+        latest_user_message = messages[-1]["content"]
+        print(f"💬 Latest user message: {latest_user_message}")
+    except Exception as e:
+        print(f"❌ Error extracting latest message: {e}")
+        return {"role": "assistant", "content": "Sorry, I couldn't read your message."}
+
+    try:
+        response = agent.run(latest_user_message)
+        print(f"✅ Agent response:\n{response}")
+        return {"role": "assistant", "content": response.content}  # ✅ FIXED HERE
+    except Exception as e:
+        print(f"❌ Agent failed: {e}")
+        return {"role": "assistant", "content": "Something went wrong on my end. Try again?"}
+
+    print("====== DEBUG: ask_agent END ======")
+
+
+def yes(message, history):
+    return "yes"
+
+# ✅ Launch Gradio UI
+gr.ChatInterface(
+    fn=ask_agent,
+    title="AI Treasury Assistant",
+    description="Ask about cash positions, FX risk, or liquidity outlook.",
+    chatbot=gr.Chatbot(show_copy_button=True),
+    type="messages"  # ✅ Fixes the warning
+).launch()
 
-if __name__ == "__main__":
-    demo.launch()