# import gradio as gr

# from mcp.client.stdio import StdioServerParameters
# from smolagents import InferenceClientModel, CodeAgent
# from smolagents.mcp_client import MCPClient
# from transformers import pipeline
# from transformers import AutoModelForCausalLM, AutoTokenizer
# import torch

# # Initialize the MCP client correctly
# try:
#     mcp_client = MCPClient(
#         ## Try this working example on the hub:
#         # {"url": "https://abidlabs-mcp-tools.hf.space/gradio_api/mcp/sse"}
#         {"url": "https://captain-awesome-alquranchapters.hf.space/gradio_api/mcp/sse"}
#     )
    
#     tools = mcp_client.get_tools()

#     # model = InferenceClientModel()
#     # model = TransformersModel(
#     # model_id="Qwen/Qwen2.5-Coder-32B-Instruct",
#     # device="cuda",
#     # max_new_tokens=5000,
#     # )
#     model_id = "unsloth/Llama-3.2-1B"

#     model = AutoModelForCausalLM.from_pretrained(
#       model_id,
#       torch_dtype=torch.bfloat16,
#       device_map="auto"
#     )


#     agent = CodeAgent(tools=[*tools], model=model)



#     # Define Gradio ChatInterface
#     demo = gr.ChatInterface(
#         fn=lambda message, history: str(agent.run(message)),
#         type="messages",
#         title="Agent with MCP Tools",
#         description="This is a simple agent that uses MCP tools to get chapters of the Quran.",
#     )

#     demo.launch(share=True)

# finally:
#     # Properly close the MCP client connection
#     # if 'mcp_client' in locals():
#         # mcp_client.disconnect()
#     mcp_client.disconnect()




import gradio as gr
import asyncio
from smolagents.mcp_client import MCPClient
from transformers import AutoModelForCausalLM
import torch
from mcp.client.stdio import StdioServerParameters
from smolagents import InferenceClientModel, CodeAgent, ToolCollection
import os

try:
    mcp_client = MCPClient(
        ## Try this working example on the hub:
        # {"url": "https://abidlabs-mcp-tools.hf.space/gradio_api/mcp/sse"}
        {"url": "https://captain-awesome-alquranchapters.hf.space/gradio_api/mcp/sse"}
    )
    tools = mcp_client.get_tools()

    model = InferenceClientModel(token=os.getenv("HUGGINGFACE_API_TOKEN"))
    agent = CodeAgent(tools=[*tools], model=model, additional_authorized_imports=["json", "ast", "urllib", "base64"])

    # model_id = "unsloth/Llama-3.2-1B"
    # model = AutoModelForCausalLM.from_pretrained(
    #     model_id,
    #     torch_dtype=torch.bfloat16,
    #     device_map="auto"
    # )

    # agent = CodeAgent(tools=tools, model=model)

    demo = gr.ChatInterface(
        fn=lambda message, history: str(agent.run(message)),
        type="messages",
        title="Agent with MCP Tools",
        description="This is a simple agent that uses MCP tools to get chapters of the Quran.",
    )

    
    demo.launch()

    # demo.launch(share=True)

finally:
    mcp_client.disconnect()




# import gradio as gr
# import os

# from smolagents import InferenceClientModel, CodeAgent, MCPClient


# try:
#     mcp_client = MCPClient(
#         # {"url": "https://abidlabs-mcp-tool-http.hf.space/gradio_api/mcp/sse"}
#         {"url":"https://captain-awesome-alquranchapters.hf.space/gradio_api/mcp/sse"}
#     )
#     tools = mcp_client.get_tools()

#     model = InferenceClientModel(token=os.getenv("HUGGINGFACE_API_TOKEN"))
#     agent = CodeAgent(tools=[*tools], model=model, additional_authorized_imports=["json", "ast", "urllib", "base64"])

#     demo = gr.ChatInterface(
#         fn=lambda message, history: str(agent.run(message)),
#         type="messages",
#         examples=["Analyze the sentiment of the following text 'This is awesome'"],
#         title="Agent with MCP Tools",
#         description="This is a simple agent that uses MCP tools to answer questions.",
#     )

#     demo.launch()
# finally:
#     mcp_client.disconnect()