File size: 2,678 Bytes
f9f4d6d 16a729a f9f4d6d 16a729a f9f4d6d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 |
import asyncio
import gradio as gr
import nest_asyncio
from huggingface_hub import login
from src.agent_hackathon.consts import PROJECT_ROOT_DIR
from src.agent_hackathon.logger import get_logger
from src.agent_hackathon.multiagent import MultiAgentWorkflow
nest_asyncio.apply()
logger = get_logger(log_name="multiagent", log_dir=PROJECT_ROOT_DIR / "logs")
PRIMARY_HEADING = """# ML Topics Deep Research"""
SECONDARY_HEADING = """### This multi agent framework queries a DB containing arxiv ML research papers from Jan 2020 - Jun 6th 2025 for select categories, and finds events/conferences related to the user's query.
For more details on the filtered arxiv ds refer [here](https://huggingface.co/datasets/Shamik/arxiv_cs_2020_07_2025)
"""
workflow = MultiAgentWorkflow()
_login_done = False
def run(
query: str, api_key: str, chat_history: list[dict[str, str | None]]
) -> tuple[str,list[dict[str, str | None]]] | None:
global _login_done
if not api_key or not api_key.startswith("hf"):
raise ValueError("Incorrect HuggingFace Inference API Key")
if not _login_done:
login(token=api_key)
_login_done = True
try:
result = asyncio.run(workflow.run(user_query=query))
chat_history.append({"role": "user", "content": query})
chat_history.append({"role": "assistant", "content": result})
return "", chat_history
except Exception as err:
logger.error(f"Error during workflow execution: {err}")
return None
with gr.Blocks(fill_height=True) as demo:
gr.Markdown(value=PRIMARY_HEADING)
gr.Markdown(value=SECONDARY_HEADING)
gr.Markdown(
value="""<span style="color:red"> Please use a 🤗 Inference API Key </span>"""
)
api_key = gr.Textbox(
placeholder="Enter your HuggingFace Inference API KEY HERE",
label="🤗 Inference API Key",
show_label=True,
type="password",
)
chatbot = gr.Chatbot(
type="messages", label="DeepResearch", show_label=True, height=500,
show_copy_all_button=True, show_copy_button=True
)
msg = gr.Textbox(
placeholder="Type your message here and press enter...",
show_label=True,
label="Input",
submit_btn=True,
stop_btn=True,
)
clear = gr.ClearButton(components=[msg, chatbot])
msg.submit(fn=run, inputs=[msg, api_key, chatbot], outputs=[msg, chatbot])
demo.queue(max_size=1).launch(share=False)
# if __name__ == "__main__":
# demo.queue(max_size=1).launch(share=False)
# example queries
# tell me about reinforcement learning in robotics
# give me event details on reinforcement learning & robotics |