app.py

import random
import datetime
import sys
import os
import torch
import logging
import json
from importlib.resources import files
from txagent import TxAgent
from tooluniverse import ToolUniverse
import gradio as gr

# Set up logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

# Determine the directory where the current file is located
current_dir = os.path.dirname(os.path.abspath(__file__))
os.environ["MKL_THREADING_LAYER"] = "GNU"
os.environ["TOKENIZERS_PARALLELISM"] = "false"

# Configuration
CONFIG = {
    "model_name": "mims-harvard/TxAgent-T1-Llama-3.1-8B",
    "rag_model_name": "mims-harvard/ToolRAG-T1-GTE-Qwen2-1.5B",
    "embedding_filename": "ToolRAG-T1-GTE-Qwen2-1.5Btool_embedding_47dc56b3e3ddeb31af4f19defdd538d984de1500368852a0fab80bc2e826c944.pt",
    "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')),
        "new_tool": os.path.join(current_dir, 'data', 'new_tool.json')
    }
}

DESCRIPTION = '''
<div>
<h1 style="text-align: center;">TxAgent: An AI Agent for Therapeutic Reasoning Across a Universe of Tools</h1>
</div>
'''

INTRO = """
Precision therapeutics require multimodal adaptive models that provide personalized treatment recommendations. 
We introduce TxAgent, an AI agent that leverages multi-step reasoning and real-time biomedical knowledge 
retrieval across a toolbox of 211 expert-curated tools to navigate complex drug interactions, 
contraindications, and patient-specific treatment strategies, delivering evidence-grounded therapeutic decisions.
"""

LICENSE = """
We welcome your feedback and suggestions to enhance your experience with TxAgent, and if you're interested 
in collaboration, please email Marinka Zitnik and Shanghua Gao.

### Medical Advice Disclaimer
DISCLAIMER: THIS WEBSITE DOES NOT PROVIDE MEDICAL ADVICE
The information, including but not limited to, text, graphics, images and other material contained on this 
website are for informational purposes only. No material on this site is intended to be a substitute for 
professional medical advice, diagnosis or treatment.
"""

PLACEHOLDER = """
<div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;">
   <h1 style="font-size: 28px; margin-bottom: 2px; opacity: 0.55;">TxAgent</h1>
   <p style="font-size: 18px; margin-bottom: 2px; opacity: 0.65;">Tips before using TxAgent:</p>
   <p style="font-size: 18px; margin-bottom: 2px; opacity: 0.55;">Please click clear🗑️ (top-right) to remove previous context before submitting a new question.</p>
   <p style="font-size: 18px; margin-bottom: 2px; opacity: 0.55;">Click retry🔄 (below message) to get multiple versions of the answer.</p>
</div>
"""

css = """
h1 {
  text-align: center;
  display: block;
}

#duplicate-button {
  margin: auto;
  color: white;
  background: #1565c0;
  border-radius: 100vh;
}
.small-button button {
    font-size: 12px !important;
    padding: 4px 8px !important;
    height: 6px !important;
    width: 4px !important;
}
.gradio-accordion {
    margin-top: 0px !important;
    margin-bottom: 0px !important;
}
"""

chat_css = """
.gr-button { font-size: 20px !important; }
.gr-button svg { width: 32px !important; height: 32px !important; }
"""

def safe_load_embeddings(filepath: str) -> any:
    """Safely load embeddings with proper weights_only handling"""
    try:
        return torch.load(filepath, weights_only=True)
    except Exception as e:
        logger.warning(f"Secure load failed, trying with weights_only=False: {str(e)}")
        try:
            with torch.serialization.safe_globals([torch.serialization._reconstruct]):
                return torch.load(filepath, weights_only=False)
        except Exception as e:
            logger.error(f"Failed to load embeddings even with safe_globals: {str(e)}")
            return None

def patch_embedding_loading():
    """Monkey-patch the embedding loading functionality"""
    try:
        from txagent.toolrag import ToolRAGModel

        original_load = ToolRAGModel.load_tool_desc_embedding

        def patched_load(self, tooluniverse):
            try:
                if not os.path.exists(CONFIG["embedding_filename"]):
                    logger.error(f"Embedding file not found: {CONFIG['embedding_filename']}")
                    return False

                self.tool_desc_embedding = safe_load_embeddings(CONFIG["embedding_filename"])
                if self.tool_desc_embedding is None:
                    logger.error("Embedding is None, aborting.")
                    return False

                # Ensure tools is a list (in case it's a generator)
                tools = list(tooluniverse.get_all_tools()) if hasattr(tooluniverse, 'get_all_tools') else []
                current_count = len(tools)
                embedding_count = len(self.tool_desc_embedding)

                if current_count != embedding_count:
                    logger.warning(f"Tool count mismatch (tools: {current_count}, embeddings: {embedding_count})")

                    if current_count < embedding_count:
                        self.tool_desc_embedding = self.tool_desc_embedding[:current_count]
                        logger.info(f"Truncated embeddings to match {current_count} tools")
                    else:
                        last_embedding = self.tool_desc_embedding[-1]
                        padding = [last_embedding] * (current_count - embedding_count)
                        self.tool_desc_embedding = torch.cat([self.tool_desc_embedding] + padding)
                        logger.info(f"Padded embeddings to match {current_count} tools")

                return True

            except Exception as e:
                logger.error(f"Failed to load embeddings: {str(e)}")
                return False

        ToolRAGModel.load_tool_desc_embedding = patched_load
        logger.info("Successfully patched embedding loading")

    except Exception as e:
        logger.error(f"Failed to patch embedding loading: {str(e)}")
        raise

def update_model_parameters(agent, enable_finish, enable_rag, enable_summary,
                          init_rag_num, step_rag_num, skip_last_k,
                          summary_mode, summary_skip_last_k, summary_context_length, 
                          force_finish, seed):
    """Update model parameters"""
    updated_params = agent.update_parameters(
        enable_finish=enable_finish,
        enable_rag=enable_rag,
        enable_summary=enable_summary,
        init_rag_num=init_rag_num,
        step_rag_num=step_rag_num,
        skip_last_k=skip_last_k,
        summary_mode=summary_mode,
        summary_skip_last_k=summary_skip_last_k,
        summary_context_length=summary_context_length,
        force_finish=force_finish,
        seed=seed,
    )
    return updated_params

def update_seed(agent):
    """Update random seed"""
    seed = random.randint(0, 10000)
    updated_params = agent.update_parameters(seed=seed)
    return updated_params

def handle_retry(agent, history, retry_data: gr.RetryData, temperature, max_new_tokens, max_tokens, multi_agent, conversation, max_round):
    """Handle retry functionality"""
    print("Updated seed:", update_seed(agent))
    new_history = history[:retry_data.index]
    previous_prompt = history[retry_data.index]['content']
    print("previous_prompt", previous_prompt)
    yield from agent.run_gradio_chat(new_history + [{"role": "user", "content": previous_prompt}], 
                                   temperature, max_new_tokens, max_tokens, multi_agent, conversation, max_round)

PASSWORD = "mypassword"

def check_password(input_password):
    """Check password for protected settings"""
    if input_password == PASSWORD:
        return gr.update(visible=True), ""
    else:
        return gr.update(visible=False), "Incorrect password, try again!"

def create_demo(agent):
    """Create the Gradio interface"""
    default_temperature = 0.3
    default_max_new_tokens = 1024
    default_max_tokens = 81920
    default_max_round = 30
    
    question_examples = [
        ['Given a 50-year-old patient experiencing severe acute pain and considering the use of the newly approved medication, Journavx, how should the dosage be adjusted considering the presence of moderate hepatic impairment?'],
        ['Given a 50-year-old patient experiencing severe acute pain and considering the use of the newly approved medication, Journavx, how should the dosage be adjusted considering the presence of severe hepatic impairment?'],
        ['A 30-year-old patient is taking Prozac to treat their depression. They were recently diagnosed with WHIM syndrome and require a treatment for that condition as well. Is Xolremdi suitable for this patient, considering contraindications?'],
    ]

    chatbot = gr.Chatbot(height=800, placeholder=PLACEHOLDER,
                        label='TxAgent', type="messages", show_copy_button=True)
    
    with gr.Blocks(css=css) as demo:
        gr.Markdown(DESCRIPTION)
        gr.Markdown(INTRO)
        
        temperature_state = gr.State(value=default_temperature)
        max_new_tokens_state = gr.State(value=default_max_new_tokens)
        max_tokens_state = gr.State(value=default_max_tokens)
        max_round_state = gr.State(value=default_max_round)
        
        chatbot.retry(
            lambda *args: handle_retry(agent, *args), 
            inputs=[chatbot, chatbot, temperature_state, max_new_tokens_state,
                   max_tokens_state, gr.Checkbox(value=False, render=False), 
                   gr.State([]), max_round_state]
        )

        gr.ChatInterface(
            fn=lambda *args: agent.run_gradio_chat(*args),
            chatbot=chatbot,
            fill_height=True, 
            fill_width=True, 
            stop_btn=True,
            additional_inputs_accordion=gr.Accordion(
                label="⚙️ Inference Parameters", open=False, render=False),
            additional_inputs=[
                temperature_state, max_new_tokens_state, max_tokens_state,
                gr.Checkbox(
                    label="Activate multi-agent reasoning mode", 
                    value=False, 
                    render=False),
                gr.State([]),
                max_round_state,
                gr.Number(label="Seed", value=100, render=False)
            ],
            examples=question_examples,
            cache_examples=False,
            css=chat_css,
        )

        with gr.Accordion("Settings", open=False):
            temperature_slider = gr.Slider(
                minimum=0,
                maximum=1,
                step=0.1,
                value=default_temperature,
                label="Temperature"
            )
            max_new_tokens_slider = gr.Slider(
                minimum=128,
                maximum=4096,
                step=1,
                value=default_max_new_tokens,
                label="Max new tokens"
            )
            max_tokens_slider = gr.Slider(
                minimum=128,
                maximum=32000,
                step=1,
                value=default_max_tokens,
                label="Max tokens"
            )
            max_round_slider = gr.Slider(
                minimum=0,
                maximum=50,
                step=1,
                value=default_max_round,
                label="Max round")

            temperature_slider.change(
                lambda x: x, inputs=temperature_slider, outputs=temperature_state)
            max_new_tokens_slider.change(
                lambda x: x, inputs=max_new_tokens_slider, outputs=max_new_tokens_state)
            max_tokens_slider.change(
                lambda x: x, inputs=max_tokens_slider, outputs=max_tokens_state)
            max_round_slider.change(
                lambda x: x, inputs=max_round_slider, outputs=max_round_state)

            password_input = gr.Textbox(
                label="Enter Password for More Settings", type="password")
            incorrect_message = gr.Textbox(visible=False, interactive=False)
            
            with gr.Accordion("⚙️ Settings", open=False, visible=False) as protected_accordion:
                with gr.Row():
                    with gr.Column(scale=1):
                        with gr.Accordion("⚙️ Model Loading", open=False):
                            model_name_input = gr.Textbox(
                                label="Enter model path", value=CONFIG["model_name"])
                            load_model_btn = gr.Button(value="Load Model")
                            load_model_btn.click(
                                agent.load_models, 
                                inputs=model_name_input, 
                                outputs=gr.Textbox(label="Status"))
                    with gr.Column(scale=1):
                        with gr.Accordion("⚙️ Functional Parameters", open=False):
                            enable_finish = gr.Checkbox(label="Enable Finish", value=True)
                            enable_rag = gr.Checkbox(label="Enable RAG", value=True)
                            enable_summary = gr.Checkbox(label="Enable Summary", value=False)
                            init_rag_num = gr.Number(label="Initial RAG Num", value=0)
                            step_rag_num = gr.Number(label="Step RAG Num", value=10)
                            skip_last_k = gr.Number(label="Skip Last K", value=0)
                            summary_mode = gr.Textbox(label="Summary Mode", value='step')
                            summary_skip_last_k = gr.Number(label="Summary Skip Last K", value=0)
                            summary_context_length = gr.Number(label="Summary Context Length", value=None)
                            force_finish = gr.Checkbox(label="Force FinalAnswer", value=True)
                            seed = gr.Number(label="Seed", value=100)
                            submit_btn = gr.Button("Update Parameters")
                            updated_parameters_output = gr.JSON()
                            submit_btn.click(
                                lambda *args: update_model_parameters(agent, *args),
                                inputs=[enable_finish, enable_rag, enable_summary, 
                                      init_rag_num, step_rag_num, skip_last_k,
                                      summary_mode, summary_skip_last_k, 
                                      summary_context_length, force_finish, seed],
                                outputs=updated_parameters_output
                            )
            
            submit_button = gr.Button("Submit")
            submit_button.click(
                check_password,
                inputs=password_input,
                outputs=[protected_accordion, incorrect_message]
            )
        
        gr.Markdown(LICENSE)
    
    return demo

def main():
    """Main function to run the application"""
    agent = create_agent()
    demo = create_demo(agent)
    demo.launch(share=True)

if __name__ == "__main__":
    main()