app.py

import os
import json
import logging
import torch
from txagent import TxAgent
import gradio as gr
from huggingface_hub import hf_hub_download, snapshot_download
from tooluniverse import ToolUniverse
from tqdm import tqdm
import time

# 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",
    "local_dir": "./models",
    "tool_files": {
        "new_tool": "./data/new_tool.json"
    },
    "download_timeout": 300,  # Increased timeout to 5 minutes
    "max_retries": 3
}

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

def prepare_tool_files():
    os.makedirs("./data", exist_ok=True)
    if not os.path.exists(CONFIG["tool_files"]["new_tool"]):
        logger.info("Generating tool list using ToolUniverse...")
        tu = ToolUniverse()
        tools = tu.get_all_tools()
        with open(CONFIG["tool_files"]["new_tool"], "w") as f:
            json.dump(tools, f, indent=2)
        logger.info(f"Saved {len(tools)} tools to {CONFIG['tool_files']['new_tool']}")

def download_with_retry(repo_id, local_dir):
    retry_count = 0
    while retry_count < CONFIG["max_retries"]:
        try:
            snapshot_download(
                repo_id=repo_id,
                local_dir=local_dir,
                resume_download=True,
                local_dir_use_symlinks=False,
                timeout=CONFIG["download_timeout"]
            )
            return True
        except Exception as e:
            retry_count += 1
            logger.error(f"Attempt {retry_count} failed for {repo_id}: {str(e)}")
            if retry_count < CONFIG["max_retries"]:
                wait_time = 10 * retry_count
                logger.info(f"Waiting {wait_time} seconds before retry...")
                time.sleep(wait_time)
    return False

def download_model_files():
    os.makedirs(CONFIG["local_dir"], exist_ok=True)
    logger.info("Downloading model files...")

    # Download main model
    logger.info(f"Downloading {CONFIG['model_name']}...")
    if not download_with_retry(
        CONFIG["model_name"],
        os.path.join(CONFIG["local_dir"], CONFIG["model_name"])
    ):
        raise RuntimeError(f"Failed to download {CONFIG['model_name']} after {CONFIG['max_retries']} attempts")

    # Download RAG model
    logger.info(f"Downloading {CONFIG['rag_model_name']}...")
    if not download_with_retry(
        CONFIG["rag_model_name"],
        os.path.join(CONFIG["local_dir"], CONFIG["rag_model_name"])
    ):
        raise RuntimeError(f"Failed to download {CONFIG['rag_model_name']} after {CONFIG['max_retries']} attempts")

    logger.info("All model files downloaded successfully")

def load_embeddings(agent):
    embedding_path = CONFIG["embedding_filename"]
    if os.path.exists(embedding_path):
        logger.info("✅ Loading pre-generated embeddings file")
        try:
            embeddings = torch.load(embedding_path)
            agent.rag_model.tool_desc_embedding = embeddings
            return
        except Exception as e:
            logger.error(f"Failed to load embeddings: {e}")
            # Fall through to generate new embeddings

    logger.info("Generating tool embeddings...")
    try:
        tools = agent.tooluniverse.get_all_tools()
        descriptions = [tool["description"] for tool in tools]
        embeddings = agent.rag_model.generate_embeddings(descriptions)
        torch.save(embeddings, embedding_path)
        agent.rag_model.tool_desc_embedding = embeddings
        logger.info(f"Embeddings saved to {embedding_path}")
    except Exception as e:
        logger.error(f"Failed to generate embeddings: {e}")
        raise

class TxAgentApp:
    def __init__(self):
        self.agent = None
        self.is_initialized = False

    def initialize(self):
        if self.is_initialized:
            return "Already initialized"

        try:
            logger.info("Initializing TxAgent...")
            self.agent = TxAgent(
                CONFIG["model_name"],
                CONFIG["rag_model_name"],
                tool_files_dict=CONFIG["tool_files"],
                force_finish=True,
                enable_checker=True,
                step_rag_num=10,
                seed=100,
                additional_default_tools=["DirectResponse", "RequireClarification"]
            )
            logger.info("Initializing models...")
            self.agent.init_model()
            logger.info("Loading embeddings...")
            load_embeddings(self.agent)
            self.is_initialized = True
            logger.info("✅ TxAgent initialized successfully")
            return "✅ TxAgent initialized successfully"
        except Exception as e:
            logger.error(f"Initialization failed: {str(e)}")
            return f"❌ Initialization failed: {str(e)}"

    def chat(self, message, history):
        if not self.is_initialized:
            return history + [(message, "⚠️ Error: Model not initialized. Please click 'Initialize Model' first.")]

        try:
            response = ""
            for chunk in self.agent.run_gradio_chat(
                message=message,
                history=history,
                temperature=0.3,
                max_new_tokens=1024,
                max_tokens=8192,
                multi_agent=False,
                conversation=[],
                max_round=30
            ):
                response += chunk

            return history + [(message, response)]
        except Exception as e:
            logger.error(f"Chat error: {str(e)}")
            return history + [(message, f"Error: {str(e)}")]

def create_interface():
    app = TxAgentApp()
    with gr.Blocks(title="TxAgent", css=".gradio-container {max-width: 900px !important}") as demo:
        gr.Markdown("""
        # 🧠 TxAgent: Therapeutic Reasoning AI
        ### A specialized AI for clinical decision support and therapeutic reasoning
        """)

        with gr.Row():
            init_btn = gr.Button("Initialize Model", variant="primary")
            init_status = gr.Textbox(label="Initialization Status", interactive=False)

        with gr.Row():
            with gr.Column(scale=3):
                chatbot = gr.Chatbot(height=600, label="Conversation", bubble_full_width=False)
                msg = gr.Textbox(label="Your Question", placeholder="Enter your clinical question here...")
                submit_btn = gr.Button("Submit", variant="primary")
            with gr.Column(scale=1):
                gr.Markdown("### Example Questions:")
                gr.Examples(
                    examples=[
                        "How to adjust Journavx dosage for hepatic impairment?",
                        "Is Xolremdi safe with Prozac for WHIM syndrome?",
                        "Warfarin-Amiodarone contraindications?",
                        "Alternative treatments for EGFR-positive NSCLC?"
                    ],
                    inputs=msg,
                    label="Click to try"
                )

        init_btn.click(
            fn=app.initialize,
            outputs=init_status,
            api_name="initialize"
        )
        msg.submit(
            fn=app.chat,
            inputs=[msg, chatbot],
            outputs=chatbot,
            api_name="chat"
        )
        submit_btn.click(
            fn=app.chat,
            inputs=[msg, chatbot],
            outputs=chatbot
        )

    return demo

if __name__ == "__main__":
    try:
        logger.info("Preparing tool files...")
        prepare_tool_files()
        
        logger.info("Downloading model files (if needed)...")
        download_model_files()
        
        logger.info("Launching interface...")
        interface = create_interface()
        interface.launch(
            server_name="0.0.0.0",
            server_port=7860,
            share=False,
            show_error=True
        )
    except Exception as e:
        logger.error(f"Application failed to start: {str(e)}")
        raise