File size: 15,602 Bytes
cb3a670 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import operator\n",
"import warnings\n",
"from typing import *\n",
"import traceback\n",
"\n",
"import os\n",
"import torch\n",
"from dotenv import load_dotenv\n",
"from IPython.display import Image\n",
"from langgraph.checkpoint.memory import MemorySaver\n",
"from langgraph.graph import END, StateGraph\n",
"from langchain_core.messages import AnyMessage, HumanMessage, SystemMessage, ToolMessage\n",
"from langchain_openai import ChatOpenAI\n",
"from transformers import logging\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import re\n",
"\n",
"from medrax.agent import *\n",
"from medrax.tools import *\n",
"from medrax.utils import *\n",
"\n",
"import json\n",
"import openai\n",
"import os\n",
"import glob\n",
"import time\n",
"import logging\n",
"from datetime import datetime\n",
"from tenacity import retry, wait_exponential, stop_after_attempt\n",
"\n",
"warnings.filterwarnings(\"ignore\")\n",
"_ = load_dotenv()\n",
"\n",
"\n",
"# Setup directory paths\n",
"ROOT = \"set this directory to where MedRAX is, .e.g /home/MedRAX\"\n",
"PROMPT_FILE = f\"{ROOT}/medrax/docs/system_prompts.txt\"\n",
"BENCHMARK_FILE = f\"{ROOT}/benchmark/questions\"\n",
"MODEL_DIR = f\"set this to where the tool models are, e.g /home/models\"\n",
"FIGURES_DIR = f\"{ROOT}/benchmark/figures\"\n",
"\n",
"model_name = \"medrax\"\n",
"temperature = 0.2\n",
"medrax_logs = f\"{ROOT}/experiments/medrax_logs\"\n",
"log_filename = f\"{medrax_logs}/{model_name}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json\"\n",
"logging.basicConfig(filename=log_filename, level=logging.INFO, format=\"%(message)s\", force=True)\n",
"device = \"cuda\""
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def get_tools():\n",
" report_tool = ChestXRayReportGeneratorTool(cache_dir=MODEL_DIR, device=device)\n",
" xray_classification_tool = ChestXRayClassifierTool(device=device)\n",
" segmentation_tool = ChestXRaySegmentationTool(device=device)\n",
" grounding_tool = XRayPhraseGroundingTool(\n",
" cache_dir=MODEL_DIR, temp_dir=\"temp\", device=device, load_in_8bit=True\n",
" )\n",
" xray_vqa_tool = XRayVQATool(cache_dir=MODEL_DIR, device=device)\n",
" llava_med_tool = LlavaMedTool(cache_dir=MODEL_DIR, device=device, load_in_8bit=True)\n",
"\n",
" return [\n",
" report_tool,\n",
" xray_classification_tool,\n",
" segmentation_tool,\n",
" grounding_tool,\n",
" xray_vqa_tool,\n",
" llava_med_tool,\n",
" ]\n",
"\n",
"\n",
"def get_agent(tools):\n",
" prompts = load_prompts_from_file(PROMPT_FILE)\n",
" prompt = prompts[\"MEDICAL_ASSISTANT\"]\n",
"\n",
" checkpointer = MemorySaver()\n",
" model = ChatOpenAI(model=\"gpt-4o\", temperature=temperature, top_p=0.95)\n",
" agent = Agent(\n",
" model,\n",
" tools=tools,\n",
" log_tools=True,\n",
" log_dir=\"logs\",\n",
" system_prompt=prompt,\n",
" checkpointer=checkpointer,\n",
" )\n",
" thread = {\"configurable\": {\"thread_id\": \"1\"}}\n",
" return agent, thread\n",
"\n",
"\n",
"def run_medrax(agent, thread, prompt, image_urls=[]):\n",
" messages = [\n",
" HumanMessage(\n",
" content=[\n",
" {\"type\": \"text\", \"text\": prompt},\n",
" ]\n",
" + [{\"type\": \"image_url\", \"image_url\": {\"url\": image_url}} for image_url in image_urls]\n",
" )\n",
" ]\n",
"\n",
" final_response = None\n",
" for event in agent.workflow.stream({\"messages\": messages}, thread):\n",
" for v in event.values():\n",
" final_response = v\n",
"\n",
" final_response = final_response[\"messages\"][-1].content.strip()\n",
" agent_state = agent.workflow.get_state(thread)\n",
"\n",
" return final_response, str(agent_state)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def create_multimodal_request(question_data, case_details, case_id, question_id, agent, thread):\n",
" # Parse required figures\n",
" try:\n",
" # Try multiple ways of parsing figures\n",
" if isinstance(question_data[\"figures\"], str):\n",
" try:\n",
" required_figures = json.loads(question_data[\"figures\"])\n",
" except json.JSONDecodeError:\n",
" required_figures = [question_data[\"figures\"]]\n",
" elif isinstance(question_data[\"figures\"], list):\n",
" required_figures = question_data[\"figures\"]\n",
" else:\n",
" required_figures = [str(question_data[\"figures\"])]\n",
" except Exception as e:\n",
" print(f\"Error parsing figures: {e}\")\n",
" required_figures = []\n",
"\n",
" # Ensure each figure starts with \"Figure \"\n",
" required_figures = [\n",
" fig if fig.startswith(\"Figure \") else f\"Figure {fig}\" for fig in required_figures\n",
" ]\n",
"\n",
" subfigures = []\n",
" for figure in required_figures:\n",
" # Handle both regular figures and those with letter suffixes\n",
" base_figure_num = \"\".join(filter(str.isdigit, figure))\n",
" figure_letter = \"\".join(filter(str.isalpha, figure.split()[-1])) or None\n",
"\n",
" # Find matching figures in case details\n",
" matching_figures = [\n",
" case_figure\n",
" for case_figure in case_details.get(\"figures\", [])\n",
" if case_figure[\"number\"] == f\"Figure {base_figure_num}\"\n",
" ]\n",
"\n",
" if not matching_figures:\n",
" print(f\"No matching figure found for {figure} in case {case_id}\")\n",
" continue\n",
"\n",
" for case_figure in matching_figures:\n",
" # If a specific letter is specified, filter subfigures\n",
" if figure_letter:\n",
" matching_subfigures = [\n",
" subfig\n",
" for subfig in case_figure.get(\"subfigures\", [])\n",
" if subfig.get(\"number\", \"\").lower().endswith(figure_letter.lower())\n",
" or subfig.get(\"label\", \"\").lower() == figure_letter.lower()\n",
" ]\n",
" subfigures.extend(matching_subfigures)\n",
" else:\n",
" # If no letter specified, add all subfigures\n",
" subfigures.extend(case_figure.get(\"subfigures\", []))\n",
"\n",
" # Add images to content\n",
" figure_prompt = \"\"\n",
" image_urls = []\n",
"\n",
" for subfig in subfigures:\n",
" if \"number\" in subfig:\n",
" subfig_number = subfig[\"number\"].lower().strip().replace(\" \", \"_\") + \".jpg\"\n",
" subfig_path = os.path.join(FIGURES_DIR, case_id, subfig_number)\n",
" figure_prompt += f\"{subfig_number} located at {subfig_path}\\n\"\n",
" if \"url\" in subfig:\n",
" image_urls.append(subfig[\"url\"])\n",
" else:\n",
" print(f\"Subfigure missing URL: {subfig}\")\n",
"\n",
" prompt = (\n",
" f\"Answer this question correctly using chain of thought reasoning and \"\n",
" \"carefully evaluating choices. Solve using our own vision and reasoning and then\"\n",
" \"use tools to complement your reasoning. Trust your own judgement over any tools.\\n\"\n",
" f\"{question_data['question']}\\n{figure_prompt}\"\n",
" )\n",
"\n",
" try:\n",
" start_time = time.time()\n",
"\n",
" final_response, agent_state = run_medrax(\n",
" agent=agent, thread=thread, prompt=prompt, image_urls=image_urls\n",
" )\n",
" model_answer, agent_state = run_medrax(\n",
" agent=agent,\n",
" thread=thread,\n",
" prompt=\"If you had to choose the best option, only respond with the letter of choice (only one of A, B, C, D, E, F)\",\n",
" )\n",
" duration = time.time() - start_time\n",
"\n",
" log_entry = {\n",
" \"case_id\": case_id,\n",
" \"question_id\": question_id,\n",
" \"timestamp\": datetime.now().isoformat(),\n",
" \"model\": model_name,\n",
" \"temperature\": temperature,\n",
" \"duration\": round(duration, 2),\n",
" \"usage\": \"\",\n",
" \"cost\": 0,\n",
" \"raw_response\": final_response,\n",
" \"model_answer\": model_answer.strip(),\n",
" \"correct_answer\": question_data[\"answer\"][0],\n",
" \"input\": {\n",
" \"messages\": prompt,\n",
" \"question_data\": {\n",
" \"question\": question_data[\"question\"],\n",
" \"explanation\": question_data[\"explanation\"],\n",
" \"metadata\": question_data.get(\"metadata\", {}),\n",
" \"figures\": question_data[\"figures\"],\n",
" },\n",
" \"image_urls\": [subfig[\"url\"] for subfig in subfigures if \"url\" in subfig],\n",
" \"image_captions\": [subfig.get(\"caption\", \"\") for subfig in subfigures],\n",
" },\n",
" \"agent_state\": agent_state,\n",
" }\n",
" logging.info(json.dumps(log_entry))\n",
" return final_response, model_answer.strip()\n",
"\n",
" except Exception as e:\n",
" log_entry = {\n",
" \"case_id\": case_id,\n",
" \"question_id\": question_id,\n",
" \"timestamp\": datetime.now().isoformat(),\n",
" \"model\": model_name,\n",
" \"temperature\": temperature,\n",
" \"status\": \"error\",\n",
" \"error\": str(e),\n",
" \"cost\": 0,\n",
" \"input\": {\n",
" \"messages\": prompt,\n",
" \"question_data\": {\n",
" \"question\": question_data[\"question\"],\n",
" \"explanation\": question_data[\"explanation\"],\n",
" \"metadata\": question_data.get(\"metadata\", {}),\n",
" \"figures\": question_data[\"figures\"],\n",
" },\n",
" \"image_urls\": [subfig[\"url\"] for subfig in subfigures if \"url\" in subfig],\n",
" \"image_captions\": [subfig.get(\"caption\", \"\") for subfig in subfigures],\n",
" },\n",
" }\n",
" logging.info(json.dumps(log_entry))\n",
" print(f\"Error processing case {case_id}, question {question_id}: {str(e)}\")\n",
" return \"\", \"\"\n",
"\n",
"\n",
"def load_benchmark_questions(case_id):\n",
" benchmark_dir = \"../benchmark/questions\"\n",
" return glob.glob(f\"{benchmark_dir}/{case_id}/{case_id}_*.json\")\n",
"\n",
"\n",
"def count_total_questions():\n",
" total_cases = len(glob.glob(\"../benchmark/questions/*\"))\n",
" total_questions = sum(\n",
" len(glob.glob(f\"../benchmark/questions/{case_id}/*.json\"))\n",
" for case_id in os.listdir(\"../benchmark/questions\")\n",
" )\n",
" return total_cases, total_questions\n",
"\n",
"\n",
"def main(tools):\n",
" with open(\"../data/eurorad_metadata.json\", \"r\") as file:\n",
" data = json.load(file)\n",
"\n",
" total_cases, total_questions = count_total_questions()\n",
" cases_processed = 0\n",
" questions_processed = 0\n",
" skipped_questions = 0\n",
"\n",
" print(f\"Beginning benchmark evaluation for model {model_name} with temperature {temperature}\\n\")\n",
"\n",
" for case_id, case_details in data.items():\n",
" if int(case_details[\"case_id\"]) <= 17158:\n",
" continue\n",
"\n",
" print(f\"----------------------------------------------------------------\")\n",
" agent, thread = get_agent(tools)\n",
"\n",
" question_files = load_benchmark_questions(case_id)\n",
" if not question_files:\n",
" continue\n",
"\n",
" cases_processed += 1\n",
" for question_file in question_files:\n",
" with open(question_file, \"r\") as file:\n",
" question_data = json.load(file)\n",
" question_id = os.path.basename(question_file).split(\".\")[0]\n",
"\n",
" # agent, thread = get_agent(tools)\n",
" questions_processed += 1\n",
" final_response, model_answer = create_multimodal_request(\n",
" question_data, case_details, case_id, question_id, agent, thread\n",
" )\n",
"\n",
" # Handle cases where response is None\n",
" if final_response is None:\n",
" skipped_questions += 1\n",
" print(f\"Skipped question: Case ID {case_id}, Question ID {question_id}\")\n",
" continue\n",
"\n",
" print(\n",
" f\"Progress: Case {cases_processed}/{total_cases}, Question {questions_processed}/{total_questions}\"\n",
" )\n",
" print(f\"Case ID: {case_id}\")\n",
" print(f\"Question ID: {question_id}\")\n",
" print(f\"Final Response: {final_response}\")\n",
" print(f\"Model Answer: {model_answer}\")\n",
" print(f\"Correct Answer: {question_data['answer']}\")\n",
" print(f\"----------------------------------------------------------------\\n\")\n",
"\n",
" print(f\"\\nBenchmark Summary:\")\n",
" print(f\"Total Cases Processed: {cases_processed}\")\n",
" print(f\"Total Questions Processed: {questions_processed}\")\n",
" print(f\"Total Questions Skipped: {skipped_questions}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tools = get_tools()\n",
"main(tools)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "medmax",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.16"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|