import os import sys import random import gradio as gr from multiprocessing import freeze_support import importlib import inspect # === Path fix sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), "src")) # === Reload to avoid stale module import txagent.txagent importlib.reload(txagent.txagent) from txagent.txagent import TxAgent from gradio import ChatMessage # === Debug print(">>> TxAgent loaded from:", inspect.getfile(TxAgent)) print(">>> TxAgent has run_gradio_chat:", hasattr(TxAgent, "run_gradio_chat")) # === Env vars current_dir = os.path.dirname(os.path.abspath(__file__)) os.environ["MKL_THREADING_LAYER"] = "GNU" os.environ["TOKENIZERS_PARALLELISM"] = "false" # === UI text DESCRIPTION = '''

TxAgent: AI for Therapeutic Reasoning

''' INTRO = "Ask biomedical or therapeutic questions. Results are powered by tools and reasoning." LICENSE = "DISCLAIMER: THIS WEBSITE DOES NOT PROVIDE MEDICAL ADVICE." # === Model & tool 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") } 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?"] ] # === Gradio UI def create_ui(agent): with gr.Blocks() as demo: gr.Markdown(DESCRIPTION) gr.Markdown(INTRO) temperature = gr.Slider(0, 1, step=0.1, value=0.3, label="Temperature") max_new_tokens = gr.Slider(128, 4096, step=1, value=1024, label="Max New Tokens") max_tokens = gr.Slider(128, 32000, step=1, value=8192, label="Max Total Tokens") max_round = gr.Slider(1, 50, step=1, value=30, label="Max Rounds") multi_agent = gr.Checkbox(label="Enable Multi-agent Reasoning", value=False) conversation_state = gr.State([]) chatbot = gr.Chatbot(label="TxAgent", height=600, type="messages") message_input = gr.Textbox(placeholder="Ask a biomedical question...", show_label=False) send_btn = gr.Button("Send", variant="primary") def handle_chat(message, history, temperature, max_new_tokens, max_tokens, multi_agent, conversation, max_round): # Ensure response is a generator that yields list of {role, content} dictionaries return agent.run_gradio_chat( message=message, history=history, temperature=temperature, max_new_tokens=max_new_tokens, max_token=max_tokens, call_agent=multi_agent, conversation=conversation, max_round=max_round ) send_btn.click( fn=handle_chat, inputs=[message_input, chatbot, temperature, max_new_tokens, max_tokens, multi_agent, conversation_state, max_round], outputs=chatbot ) message_input.submit( fn=handle_chat, inputs=[message_input, chatbot, temperature, max_new_tokens, max_tokens, multi_agent, conversation_state, max_round], outputs=chatbot ) gr.Examples( examples=question_examples, inputs=message_input ) gr.Markdown(LICENSE) return demo # === App 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=["DirectResponse", "RequireClarification"] ) agent.init_model() if not hasattr(agent, "run_gradio_chat"): raise AttributeError("❌ TxAgent is missing `run_gradio_chat`.") demo = create_ui(agent) demo.launch(show_error=True) except Exception as e: print(f"🚨 Startup error: {e}") raise