app.py

import gradio as gr
import logging
import multiprocessing
from txagent import TxAgent
from tooluniverse import ToolUniverse
from importlib.resources import files

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

tx_app = None  # Global holder for app instance (for Gradio to use)

def init_txagent():
    """Initialize the TxAgent with proper tool file paths"""
    try:
        multiprocessing.set_start_method("spawn", force=True)
        logger.info("Initializing TxAgent...")

        tool_files = {
            "opentarget": str(files('tooluniverse.data').joinpath('opentarget_tools.json')),
            "fda_drug_label": str(files('tooluniverse.data').joinpath('fda_drug_labeling_tools.json')),
            "special_tools": str(files('tooluniverse.data').joinpath('special_tools.json')),
            "monarch": str(files('tooluniverse.data').joinpath('monarch_tools.json'))
        }

        logger.info(f"Using tool files at: {tool_files}")

        agent = TxAgent(
            model_name="mims-harvard/TxAgent-T1-Llama-3.1-8B",
            rag_model_name="mims-harvard/ToolRAG-T1-GTE-Qwen2-1.5B",
            tool_files_dict=tool_files,
            enable_finish=True,
            enable_rag=True,
            enable_summary=False,
            init_rag_num=0,
            step_rag_num=10,
            summary_mode='step',
            summary_skip_last_k=0,
            summary_context_length=None,
            force_finish=True,
            avoid_repeat=True,
            seed=42,
            enable_checker=True,
            enable_chat=False,
            additional_default_tools=["DirectResponse", "RequireClarification"]
        )

        agent.init_model()
        logger.info("Model loading complete")
        return agent

    except Exception as e:
        logger.error(f"Initialization failed: {str(e)}")
        raise

class TxAgentApp:
    def __init__(self):
        self.agent = init_txagent()

    def respond(self, message, chat_history, temperature, max_new_tokens, max_tokens, multi_agent, conversation_state, max_round):
        """Handle streaming responses with Gradio"""
        try:
            if not isinstance(message, str) or len(message.strip()) <= 10:
                return chat_history + [("", "Please provide a valid message longer than 10 characters.")]

            if chat_history and isinstance(chat_history[0], dict):
                chat_history = [(h["role"], h["content"]) for h in chat_history if "role" in h and "content" in h]

            response = ""
            for chunk in self.agent.run_gradio_chat(
                message=message.strip(),
                history=chat_history,
                temperature=temperature,
                max_new_tokens=max_new_tokens,
                max_token=max_tokens,
                call_agent=multi_agent,
                conversation=conversation_state,
                max_round=max_round,
                seed=42
            ):
                if isinstance(chunk, dict):
                    response += chunk.get("content", "")
                elif isinstance(chunk, str):
                    response += chunk
                else:
                    response += str(chunk)

                yield chat_history + [("user", message), ("assistant", response)]

        except Exception as e:
            logger.error(f"Error in respond function: {str(e)}")
            yield chat_history + [("", f"⚠️ Error: {str(e)}")]

# Initialize the agent safely
tx_app = TxAgentApp()

# Define Gradio UI interface
with gr.Blocks(title="TxAgent Biomedical Assistant") as app:
    gr.Markdown("# 🧠 TxAgent Biomedical Assistant")

    chatbot = gr.Chatbot(
        label="Conversation",
        height=600
    )

    msg = gr.Textbox(
        label="Your medical query",
        placeholder="Enter your biomedical question...",
        lines=3
    )

    with gr.Row():
        temp = gr.Slider(0, 1, value=0.3, label="Temperature")
        max_new_tokens = gr.Slider(128, 4096, value=1024, label="Max New Tokens")
        max_tokens = gr.Slider(128, 81920, value=81920, label="Max Total Tokens")
        max_rounds = gr.Slider(1, 30, value=10, label="Max Rounds")
        multi_agent = gr.Checkbox(label="Multi-Agent Mode")

    submit = gr.Button("Submit")
    clear = gr.Button("Clear")

    conversation_state = gr.State([])

    submit.click(
        tx_app.respond,
        [msg, chatbot, temp, max_new_tokens, max_tokens, multi_agent, conversation_state, max_rounds],
        chatbot
    )

    clear.click(lambda: [], None, chatbot)

    msg.submit(
        tx_app.respond,
        [msg, chatbot, temp, max_new_tokens, max_tokens, multi_agent, conversation_state, max_rounds],
        chatbot
    )

# This `app` will be served by Hugging Face automatically