app.py

import os
import sys
import gradio as gr
from multiprocessing import freeze_support
import importlib
import inspect
import json

# === Fix path to include src/txagent
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "src"))

# === Import and reload to ensure correct file
import txagent.txagent
importlib.reload(txagent.txagent)
from txagent.txagent import TxAgent

# === Debug print
print(">>> TxAgent loaded from:", inspect.getfile(TxAgent))
print(">>> TxAgent has run_gradio_chat:", hasattr(TxAgent, "run_gradio_chat"))

# === Environment
current_dir = os.path.abspath(os.path.dirname(__file__))
os.environ["MKL_THREADING_LAYER"] = "GNU"
os.environ["TOKENIZERS_PARALLELISM"] = "false"

# === Model config
model_name = "mims-harvard/TxAgent-T1-Llama-3.1-8B"
rag_model_name = "mims-harvard/ToolRAG-T1-GTE-Qwen2-1.5B"
new_tool_files = {
    "new_tool": os.path.join(current_dir, "data", "new_tool.json")
}

# === Example prompts
question_examples = [
    ["Given a patient with WHIM syndrome on prophylactic antibiotics, is it advisable to co-administer Xolremdi with fluconazole?"],
    ["What treatment options exist for HER2+ breast cancer resistant to trastuzumab?"]
]

# === Helper: extract tool name from content
def extract_tool_name_and_clean_content(message_obj):
    import logging
    logging.basicConfig(level=logging.INFO)

    tool_name = "Tool Result"
    content = ""

    if isinstance(message_obj, dict):
        role = message_obj.get("role", "assistant")
        content = message_obj.get("content", "")
        tool_calls = message_obj.get("tool_calls", None)
    else:
        role = getattr(message_obj, "role", "assistant")
        content = getattr(message_obj, "content", "")
        tool_calls = getattr(message_obj, "tool_calls", None)

    # Try to extract tool name from `tool_calls`
    if tool_calls:
        try:
            if isinstance(tool_calls, str):
                import json
                tool_calls = json.loads(tool_calls)
            tool_name = tool_calls[0].get("name", "Tool Result")
            logging.info(f"[extract_tool_name] Extracted from tool_calls: {tool_name}")
        except Exception as e:
            logging.warning(f"[extract_tool_name] Failed tool_calls parsing: {e}")

    # Format clean output
    if isinstance(content, (dict, list)):
        formatted = json.dumps(content, indent=2)
    else:
        formatted = str(content)

    return f"Tool: {tool_name}", formatted


# === Helper: formatted collapsible output
def format_collapsible(content, title="Answer"):
    return (
        f"<details style='border: 1px solid #ccc; padding: 8px; margin-top: 8px;'>"
        f"<summary style='font-weight: bold;'>{title}</summary>"
        f"<div style='margin-top: 8px; white-space: pre-wrap;'>{content}</div></details>"
    )

# === UI creation
def create_ui(agent):
    with gr.Blocks(theme=gr.themes.Soft()) as demo:
        gr.Markdown("<h1 style='text-align: center;'>TxAgent: Therapeutic Reasoning</h1>")
        gr.Markdown("Ask biomedical or therapeutic questions. Powered by step-by-step reasoning and tools.")

        chatbot = gr.Chatbot(label="TxAgent", height=600, type="messages")
        message_input = gr.Textbox(placeholder="Ask your biomedical question...", show_label=False)
        send_button = gr.Button("Send", variant="primary")
        conversation_state = gr.State([])

        # === Core handler (streaming generator)
        def handle_chat(message, history, conversation):
            generator = agent.run_gradio_chat(
                message=message,
                history=history,
                temperature=0.3,
                max_new_tokens=1024,
                max_token=8192,
                call_agent=False,
                conversation=conversation,
                max_round=30
            )

            for update in generator:
                formatted_messages = []
                for m in update:
                    role = m["role"] if isinstance(m, dict) else getattr(m, "role", "assistant")
                    content = m["content"] if isinstance(m, dict) else getattr(m, "content", "")
                    if role == "assistant":
                        title, clean = extract_tool_name_and_clean_content(content)
                        content = format_collapsible(clean, title)
                    formatted_messages.append({"role": role, "content": content})
                yield formatted_messages

        # === Trigger handlers
        inputs = [message_input, chatbot, conversation_state]
        send_button.click(fn=handle_chat, inputs=inputs, outputs=chatbot)
        message_input.submit(fn=handle_chat, inputs=inputs, outputs=chatbot)

        gr.Examples(examples=question_examples, inputs=message_input)
        gr.Markdown("**DISCLAIMER**: This demo is for research purposes only and does not provide medical advice.")

    return demo

# === Startup
if __name__ == "__main__":
    freeze_support()

    try:
        agent = TxAgent(
            model_name=model_name,
            rag_model_name=rag_model_name,
            tool_files_dict=new_tool_files,
            force_finish=True,
            enable_checker=True,
            step_rag_num=10,
            seed=100,
            additional_default_tools=[]
        )
        agent.init_model()

        if not hasattr(agent, "run_gradio_chat"):
            raise AttributeError("❌ TxAgent is missing `run_gradio_chat`.")

        demo = create_ui(agent)
        demo.queue().launch(
            server_name="0.0.0.0",
            server_port=7860,
            show_error=True,
            share=True
        )

    except Exception as e:
        print(f"❌ App failed to start: {e}")
        raise