feat: Add heart disease dataset and remove unused Gradio test file.

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.csv filter=lfs diff=lfs merge=lfs -text

agent.py

@@ -3,7 +3,7 @@ import asyncio
 from typing import Any, Optional
 from mcp.client.streamable_http import streamablehttp_client
 from mcp.client.session import ClientSession
-from agents import Agent, Runner
+from agents import Agent, Runner, function_tool
 import openai
 
 # Define the MCP Server URL
@@ -31,20 +31,26 @@ class MCPAgent:
         # 2. Fetch Tools from MCP
         mcp_tools = await self.session.list_tools()
         
-        # 3. Convert MCP Tools to Agent Tools
-        # The Agents SDK expects python functions or specific tool objects.
-        # We'll create dynamic functions that call the MCP tools.
+        # 3. Convert MCP Tools to Agent Tools using function_tool decorator
         agent_tools = []
         for tool in mcp_tools.tools:
-            # Create a wrapper function for each tool
-            async def make_tool_func(t_name=tool.name):
+            # Create a wrapper function for each tool with proper closure
+            def make_tool_func(t_name, t_description):
+                @function_tool(strict_mode=False)
                 async def wrapper(**kwargs):
-                    return await self.session.call_tool(t_name, arguments=kwargs)
-                wrapper.__name__ = t_name
-                wrapper.__doc__ = tool.description
+                    """MCP tool wrapper"""
+                    result = await self.session.call_tool(t_name, arguments=kwargs)
+                    return result
+                
+                # Set function attributes
+                clean_name = t_name.replace("Auto_Deployer_", "")
+                wrapper.__name__ = clean_name
+                wrapper.__doc__ = t_description or "MCP tool"
+                
                 return wrapper
             
-            agent_tools.append(await make_tool_func())
+            tool_func = make_tool_func(tool.name, tool.description)
+            agent_tools.append(tool_func)
 
         # 4. Configure the Agent
         # Configure the client based on provider
@@ -70,7 +76,7 @@ class MCPAgent:
             await self.initialize()
 
         result = await Runner.run(self.agent, input=user_message)
-        return result.output
+        return result.final_output
 
     async def close(self):
         """Closes the MCP connection."""

app.py

@@ -70,7 +70,7 @@ with gr.Blocks(title="MCP MLOps Agent") as demo:
     with gr.Row():
         file_input = gr.File(label="Upload Dataset (CSV)")
     
-    chatbot = gr.Chatbot(height=600, type="messages", allow_tags=False)
+    chatbot = gr.Chatbot(height=600, type="messages")
     msg = gr.Textbox(label="Message", placeholder="Type your message here...")
     clear = gr.ClearButton([msg, chatbot, file_input])
     

heart.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:948420b084d8a3a0ca42b8419fce9aee175879e43f8aedf712377899a67aa49b
+size 35921

test_warning.py

@@ -1,8 +0,0 @@
-import gradio as gr
-
-with gr.Blocks() as demo:
-    chatbot = gr.Chatbot(type="messages", allow_tags=False)
-
-if __name__ == "__main__":
-    print("Running demo...")
-    # demo.launch()