main.py

import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

import warnings
from typing import *
from dotenv import load_dotenv
from transformers import logging

from langgraph.checkpoint.memory import MemorySaver
from langchain_openai import ChatOpenAI

from interface import create_demo
from medrax.agent import *
from medrax.tools import *
from medrax.utils import *

warnings.filterwarnings("ignore")
logging.set_verbosity_error()
_ = load_dotenv()


def initialize_agent(
    prompt_file, tools_to_use=None, model_dir="/model-weights", temp_dir="temp", device=device
):
    """Initialize the MedRAX agent with specified tools and configuration.

    Args:
        prompt_file (str): Path to file containing system prompts
        tools_to_use (List[str], optional): List of tool names to initialize. If None, all tools are initialized.
        model_dir (str, optional): Directory containing model weights. Defaults to "/model-weights".
        temp_dir (str, optional): Directory for temporary files. Defaults to "temp".
        device (torch.device, optional): Device to run models on. Defaults to CUDA if available.

    Returns:
        Tuple[Agent, Dict[str, BaseTool]]: Initialized agent and dictionary of tool instances
    """
    prompts = load_prompts_from_file(prompt_file)
    prompt = prompts["MEDICAL_ASSISTANT"]

    all_tools = {
        "ChestXRayClassifierTool": lambda: ChestXRayClassifierTool(device=device),
        "ChestXRaySegmentationTool": lambda: ChestXRaySegmentationTool(device=device),
        "LlavaMedTool": lambda: LlavaMedTool(cache_dir=model_dir, device=device, load_in_8bit=True),
        "XRayVQATool": lambda: XRayVQATool(cache_dir=model_dir, device=device),
        "ChestXRayReportGeneratorTool": lambda: ChestXRayReportGeneratorTool(
            cache_dir=model_dir, device=device
        ),
        "XRayPhraseGroundingTool": lambda: XRayPhraseGroundingTool(
            cache_dir=model_dir, temp_dir=temp_dir, load_in_8bit=True, device=device
        ),
        "ChestXRayGeneratorTool": lambda: ChestXRayGeneratorTool(
            model_path=f"{model_dir}/roentgen", temp_dir=temp_dir, device=device
        ),
        "ImageVisualizerTool": lambda: ImageVisualizerTool(),
        "DicomProcessorTool": lambda: DicomProcessorTool(temp_dir=temp_dir),
    }

    # Initialize only selected tools or all if none specified
    tools_dict = {}
    tools_to_use = tools_to_use or all_tools.keys()
    for tool_name in tools_to_use:
        if tool_name in all_tools:
            tools_dict[tool_name] = all_tools[tool_name]()

    checkpointer = MemorySaver()
    model = ChatOpenAI(model="gpt-4o", temperature=0.7, top_p=0.95)
    agent = Agent(
        model,
        tools=list(tools_dict.values()),
        log_tools=True,
        log_dir="logs",
        system_prompt=prompt,
        checkpointer=checkpointer,
    )

    print("Agent initialized")
    return agent, tools_dict


if __name__ == "__main__":
    """
    This is the main entry point for the MedRAX application.
    It initializes the agent with the selected tools and creates the demo.
    """
    print("Starting server...")

    # Example: initialize with only specific tools
    selected_tools = [
        "ImageVisualizerTool",
        "DicomProcessorTool",
        "ChestXRayClassifierTool",
        "ChestXRaySegmentationTool",
        "ChestXRayReportGeneratorTool",
        "XRayVQATool",
        # "LlavaMedTool",
        # "XRayPhraseGroundingTool",
        # "ChestXRayGeneratorTool",
    ]

    agent, tools_dict = initialize_agent(
        "medrax/docs/system_prompts.txt", tools_to_use=selected_tools, model_dir="/model-weights"
    )
    demo = create_demo(agent, tools_dict)

    demo.launch(server_name="0.0.0.0", server_port=8585, share=True)