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# Integrates all components into an agent
# import gradio as gr
import asyncio
from llama_index.core.workflow import Context
from llama_index.core.agent.workflow import AgentWorkflow, ToolCallResult, AgentStream
from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI
from retriever import guest_info_retriever
from tools import get_weather_info, get_hub_stats, google_search
from dotenv import load_dotenv
llm = HuggingFaceInferenceAPI(model_name="Qwen/Qwen2.5-Coder-32B-Instruct")
alfred = AgentWorkflow.from_tools_or_functions(
[guest_info_retriever, get_weather_info, get_hub_stats, google_search], llm=llm
)
ctx = Context(alfred)
async def main():
handler = alfred.run(
"Tell me about Lady Ada Lovelace.",
ctx=ctx,
)
async for ev in handler.stream_events():
if isinstance(ev, ToolCallResult):
print("")
print("Called tool: ", ev.tool_name, ev.tool_kwargs, "=>", ev.tool_output)
elif isinstance(ev, AgentStream): # showing the thought process
print(ev.delta, end="", flush=True)
print("🎩 Alfred's Response:")
response = await handler
print(response)
handler2 = alfred.run("What projects is she currently working on?", ctx=ctx)
async for ev in handler2.stream_events():
if isinstance(ev, ToolCallResult):
print("")
print("Called tool: ", ev.tool_name, ev.tool_kwargs, "=>", ev.tool_output)
elif isinstance(ev, AgentStream): # showing the thought process
print(ev.delta, end="", flush=True)
print("🎩 Alfred's Second Response:")
response2 = await handler2
print(response2)
# demo = gr.Interface(fn=greet, inputs="text", outputs="text")
# demo.launch()
if __name__ == "__main__":
load_dotenv()
asyncio.run(main())
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