from llama_index.core.agent.workflow import AgentWorkflow
from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI
from llama_index.core.workflow import Context

# Import our custom tools from their modules
from tools import get_weather_info_tool, get_hub_stats_tool, search_tool
from retriever import guest_info_tool
import gradio as gr
import asyncio

llm = HuggingFaceInferenceAPI(model_name='Qwen/Qwen2.5-Coder-32B-Instruct')
alfred = AgentWorkflow.from_tools_or_functions(
    tools_or_functions=[
        get_weather_info_tool,
        get_hub_stats_tool,
        search_tool,
        guest_info_tool
    ],
    llm=llm,
    verbose=True,
)

ctx = Context(alfred)
# Define a function to interact with the AgentWorkflow
async def async_interact_with_agent(input_text, ctx=ctx):
    response = await alfred.run(input_text, ctx=ctx)
    return response

def interact_with_agent(input_text):
    # Ensure the async function is run in an event loop
    return asyncio.run(async_interact_with_agent(input_text))

if __name__ == "__main__":
    # Create a Gradio interface
    interface = gr.Interface(
        fn=interact_with_agent,
        inputs=gr.Textbox(label="Enter your query"),
        outputs=gr.Textbox(label="Response"),
        title="Agent Workflow Interface",
        description="Interact with the AgentWorkflow using this Gradio interface."
    )
    interface.launch()