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import sys |
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import os |
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import pandas as pd |
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import json |
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import gradio as gr |
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from typing import List |
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from concurrent.futures import ThreadPoolExecutor |
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import hashlib |
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import shutil |
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import re |
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import psutil |
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import subprocess |
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import logging |
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import torch |
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import gc |
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from diskcache import Cache |
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import time |
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import asyncio |
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try: |
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import pypdfium2 as pdfium |
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import pytesseract |
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from PIL import Image |
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HAS_PYPDFIUM2 = True |
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except ImportError: |
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HAS_PYPDFIUM2 = False |
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import pdfplumber |
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logging.basicConfig(level=logging.INFO) |
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logging.getLogger("pdfminer").setLevel(logging.ERROR) |
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logger = logging.getLogger(__name__) |
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persistent_dir = "/data/hf_cache" |
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os.makedirs(persistent_dir, exist_ok=True) |
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model_cache_dir = os.path.join(persistent_dir, "txagent_models") |
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tool_cache_dir = os.path.join(persistent_dir, "tool_cache") |
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file_cache_dir = os.path.join(persistent_dir, "cache") |
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report_dir = os.path.join(persistent_dir, "reports") |
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vllm_cache_dir = os.path.join(persistent_dir, "vllm_cache") |
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for directory in [model_cache_dir, tool_cache_dir, file_cache_dir, report_dir, vllm_cache_dir]: |
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os.makedirs(directory, exist_ok=True) |
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os.environ["HF_HOME"] = model_cache_dir |
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os.environ["TRANSFORMERS_CACHE"] = model_cache_dir |
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os.environ["VLLM_CACHE_DIR"] = vllm_cache_dir |
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os.environ["TOKENIZERS_PARALLELISM"] = "false" |
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os.environ["CUDA_LAUNCH_BLOCKING"] = "1" |
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current_dir = os.path.dirname(os.path.abspath(__file__)) |
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src_path = os.path.abspath(os.path.join(current_dir, "src")) |
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sys.path.insert(0, src_path) |
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from txagent.txagent import TxAgent |
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cache = Cache(file_cache_dir, size_limit=10 * 1024**3) |
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def sanitize_utf8(text: str) -> str: |
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return text.encode("utf-8", "ignore").decode("utf-8") |
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def file_hash(path: str) -> str: |
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with open(path, "rb") as f: |
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return hashlib.md5(f.read()).hexdigest() |
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async def extract_all_pages_async(file_path: str, progress_callback=None, force_ocr=False) -> str: |
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try: |
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extracted_text = "" |
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total_pages = 0 |
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text_chunks = [] |
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if HAS_PYPDFIUM2: |
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pdf = pdfium.PdfDocument(file_path) |
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total_pages = len(pdf) |
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if total_pages == 0: |
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return "" |
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def extract_page(i): |
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page = pdf[i] |
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text = page.get_textpage().get_text_range() or "" |
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if (not text.strip() or len(text) < 100) and force_ocr and 'pytesseract' in sys.modules: |
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logger.info("Falling back to OCR for page %d", i + 1) |
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bitmap = page.render(scale=2).to_pil() |
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text = pytesseract.image_to_string(bitmap, lang="eng") |
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return (i, f"=== Page {i + 1} ===\n{text.strip()}") |
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with ThreadPoolExecutor(max_workers=4) as executor: |
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futures = [executor.submit(extract_page, i) for i in range(total_pages)] |
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for future in as_completed(futures): |
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page_num, text = future.result() |
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text_chunks.append((page_num, text)) |
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logger.debug("Page %d extracted: %s...", page_num + 1, text[:50]) |
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if progress_callback: |
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progress_callback(page_num + 1, total_pages) |
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text_chunks.sort(key=lambda x: x[0]) |
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extracted_text = "\n\n".join(chunk[1] for chunk in text_chunks if chunk[1].strip()) |
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pdf.close() |
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else: |
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with pdfplumber.open(file_path) as pdf: |
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total_pages = len(pdf.pages) |
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if total_pages == 0: |
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return "" |
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for i, page in enumerate(pdf.pages): |
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text = page.extract_text() or "" |
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text_chunks.append((i, f"=== Page {i + 1} ===\n{text.strip()}")) |
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logger.debug("Page %d extracted: %s...", i + 1, text[:50]) |
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if progress_callback: |
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progress_callback(i + 1, total_pages) |
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extracted_text = "\n\n".join(chunk[1] for chunk in text_chunks if chunk[1].strip()) |
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logger.info("Extracted %d pages, total length: %d chars", total_pages, len(extracted_text)) |
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if len(extracted_text) < 1000 and not force_ocr and HAS_PYPDFIUM2 and 'pytesseract' in sys.modules: |
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logger.info("Text too short, retrying with OCR") |
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return await extract_all_pages_async(file_path, progress_callback, force_ocr=True) |
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return extracted_text |
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except Exception as e: |
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logger.error("PDF processing error: %s", e) |
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return f"PDF processing error: {str(e)}" |
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def convert_file_to_json(file_path: str, file_type: str, progress_callback=None) -> str: |
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try: |
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file_h = file_hash(file_path) |
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cache_key = f"{file_h}_{file_type}" |
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if cache_key in cache: |
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logger.info("Using cached extraction for %s", file_path) |
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return cache[cache_key] |
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if file_type == "pdf": |
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text = asyncio.run(extract_all_pages_async(file_path, progress_callback, force_ocr=False)) |
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result = json.dumps({"filename": os.path.basename(file_path), "content": text, "status": "initial"}) |
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elif file_type == "csv": |
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df = pd.read_csv(file_path, encoding_errors="replace", header=None, dtype=str, |
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skip_blank_lines=False, on_bad_lines="skip") |
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content = df.fillna("").astype(str).values.tolist() |
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result = json.dumps({"filename": os.path.basename(file_path), "rows": content}) |
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elif file_type in ["xls", "xlsx"]: |
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try: |
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df = pd.read_excel(file_path, engine="openpyxl", header=None, dtype=str) |
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except Exception: |
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df = pd.read_excel(file_path, engine="xlrd", header=None, dtype=str) |
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content = df.fillna("").astype(str).values.tolist() |
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result = json.dumps({"filename": os.path.basename(file_path), "rows": content}) |
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else: |
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result = json.dumps({"error": f"Unsupported file type: {file_type}"}) |
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cache[cache_key] = result |
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logger.info("Cached extraction for %s, size: %d bytes", file_path, len(result)) |
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return result |
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except Exception as e: |
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logger.error("Error processing %s: %s", os.path.basename(file_path), e) |
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return json.dumps({"error": f"Error processing {os.path.basename(file_path)}: {str(e)}"}) |
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def log_system_usage(tag=""): |
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try: |
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cpu = psutil.cpu_percent(interval=1) |
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mem = psutil.virtual_memory() |
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logger.info("[%s] CPU: %.1f%% | RAM: %dMB / %dMB", tag, cpu, mem.used // (1024**2), mem.total // (1024**2)) |
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result = subprocess.run( |
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["nvidia-smi", "--query-gpu=memory.used,memory.total,utilization.gpu", "--format=csv,nounits,noheader"], |
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capture_output=True, text=True |
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) |
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if result.returncode == 0: |
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used, total, util = result.stdout.strip().split(", ") |
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logger.info("[%s] GPU: %sMB / %sMB | Utilization: %s%%", tag, used, total, util) |
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except Exception as e: |
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logger.error("[%s] GPU/CPU monitor failed: %s", tag, e) |
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def clean_response(text: str) -> str: |
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text = sanitize_utf8(text) |
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text = text.replace("[", "").replace("]", "").replace("None", "") |
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text = text.replace("\n\n\n", "\n\n") |
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sections = {} |
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current_section = None |
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seen_lines = set() |
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for line in text.splitlines(): |
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line = line.strip() |
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if not line or line in seen_lines: |
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continue |
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seen_lines.add(line) |
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section_match = re.match(r"###\s*(Missed Diagnoses|Medication Conflicts|Incomplete Assessments|Urgent Follow-up)", line) |
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if section_match: |
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current_section = section_match.group(1) |
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sections.setdefault(current_section, []) |
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continue |
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if current_section and line.startswith("- "): |
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sections[current_section].append(line) |
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cleaned = [f"### {heading}\n" + "\n".join(findings) for heading, findings in sections.items() if findings] |
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result = "\n\n".join(cleaned).strip() |
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logger.debug("Cleaned response length: %d chars", len(result)) |
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return result or "No oversights identified" |
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def summarize_findings(all_responses: List[str]) -> str: |
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combined_response = "\n\n".join(all_responses) |
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if not combined_response or all("No oversights identified" in resp.lower() for resp in all_responses): |
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return "### Comprehensive Clinical Oversight Summary\nNo critical oversights were identified across the provided patient records after thorough analysis." |
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sections = { |
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"Missed Diagnoses": [], |
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"Medication Conflicts": [], |
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"Incomplete Assessments": [], |
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"Urgent Follow-up": [] |
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} |
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current_section = None |
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seen_findings = set() |
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for line in combined_response.splitlines(): |
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line = line.strip() |
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if not line: |
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continue |
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section_match = re.match(r"###\s*(Missed Diagnoses|Medication Conflicts|Incomplete Assessments|Urgent Follow-up)", line) |
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if section_match: |
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current_section = section_match.group(1) |
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continue |
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if current_section and line.startswith("- ") and line not in seen_findings: |
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sections[current_section].append(line) |
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seen_findings.add(line) |
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summary_lines = [] |
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for heading, findings in sections.items(): |
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if findings: |
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summary_lines.append(f"### {heading}") |
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for finding in findings: |
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summary_lines.append(f"{finding}\n - **Risks**: Potential adverse outcomes if not addressed.\n - **Recommendation**: Immediate clinical review and follow-up.") |
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result = "### Comprehensive Clinical Oversight Summary\n" + "\n".join(summary_lines) if summary_lines else "### Comprehensive Clinical Oversight Summary\nNo critical oversights identified." |
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logger.debug("Summary length: %d chars", len(result)) |
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return result |
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def init_agent(): |
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logger.info("Initializing model...") |
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log_system_usage("Before Load") |
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default_tool_path = os.path.abspath("data/new_tool.json") |
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target_tool_path = os.path.join(tool_cache_dir, "new_tool.json") |
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if not os.path.exists(target_tool_path): |
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shutil.copy(default_tool_path, target_tool_path) |
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agent = TxAgent( |
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model_name="mims-harvard/TxAgent-T1-Llama-3.1-8B", |
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rag_model_name="mims-harvard/ToolRAG-T1-GTE-Qwen2-1.5B", |
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tool_files_dict={"new_tool": target_tool_path}, |
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force_finish=True, |
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enable_checker=False, |
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enable_rag=False, |
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init_rag_num=0, |
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step_rag_num=0, |
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seed=100, |
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additional_default_tools=[], |
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) |
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agent.init_model() |
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log_system_usage("After Load") |
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logger.info("Agent Ready") |
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return agent |
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def create_ui(agent): |
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with gr.Blocks(theme=gr.themes.Soft()) as demo: |
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gr.Markdown("<h1 style='text-align: center;'>🩺 Clinical Oversight Assistant</h1>") |
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chatbot = gr.Chatbot(label="Detailed Analysis", height=600, type="messages", visible=False) |
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final_summary = gr.Markdown(label="Comprehensive Clinical Oversight Summary") |
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file_upload = gr.File(file_types=[".pdf", ".csv", ".xls", ".xlsx"], file_count="multiple") |
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msg_input = gr.Textbox(placeholder="Ask about potential oversights...", show_label=False) |
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send_btn = gr.Button("Analyze", variant="primary") |
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download_output = gr.File(label="Download Full Report") |
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progress_bar = gr.Progress() |
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prompt_template = """ |
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Analyze the patient record excerpt for clinical oversights. Provide a detailed, evidence-based summary in markdown with findings grouped under headings: Missed Diagnoses, Medication Conflicts, Incomplete Assessments, Urgent Follow-up. For each finding, include clinical context, risks, and recommendations. Output only markdown bullet points under headings. If no issues, state "No oversights identified" once. |
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Patient Record Excerpt: |
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{chunk} |
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""" |
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async def analyze(message: str, history: List[dict], files: List, progress=gr.Progress()): |
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history.append({"role": "user", "content": message}) |
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yield history, None, "" |
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extracted = "" |
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file_hash_value = "" |
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if files: |
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def update_extraction_progress(current, total): |
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progress(current / total, desc=f"Extracting text... Page {current}/{total}") |
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return history, None, "" |
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futures = [convert_file_to_json(f.name, f.name.split(".")[-1].lower(), update_extraction_progress) for f in files] |
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results = [sanitize_utf8(future) for future in futures] |
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extracted = "\n".join(results) |
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file_hash_value = file_hash(files[0].name) if files else "" |
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history.append({"role": "assistant", "content": "✅ Text extraction complete."}) |
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yield history, None, "" |
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logger.info("Extracted text length: %d chars", len(extracted)) |
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chunk_size = 3000 |
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chunks = [extracted[i:i + chunk_size] for i in range(0, max(len(extracted), 1), chunk_size)] or [""] |
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logger.info("Created %d chunks", len(chunks)) |
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for i, chunk in enumerate(chunks): |
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logger.debug("Chunk %d content: %s...", i + 1, chunk[:100]) |
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all_responses = [] |
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batch_size = 2 |
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try: |
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for batch_idx in range(0, len(chunks), batch_size): |
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batch_chunks = chunks[batch_idx:batch_idx + batch_size] |
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batch_prompts = [prompt_template.format(chunk=chunk[:2000]) for chunk in batch_chunks] |
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batch_responses = [] |
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progress((batch_idx + 1) / len(chunks), desc=f"Analyzing chunks {batch_idx + 1}-{min(batch_idx + batch_size, len(chunks))}/{len(chunks)}") |
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async def process_chunk(prompt): |
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chunk_response = "" |
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raw_outputs = [] |
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for chunk_output in agent.run_gradio_chat( |
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message=prompt, history=[], temperature=0.2, max_new_tokens=512, max_token=1024, call_agent=False, conversation=[] |
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): |
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if chunk_output is None: |
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continue |
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if isinstance(chunk_output, list): |
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for m in chunk_output: |
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if hasattr(m, 'content') and m.content: |
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raw_outputs.append(m.content) |
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cleaned = clean_response(m.content) |
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chunk_response += cleaned + "\n\n" |
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elif isinstance(chunk_output, str) and chunk_output.strip(): |
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raw_outputs.append(chunk_output) |
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cleaned = clean_response(chunk_output) |
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chunk_response += cleaned + "\n\n" |
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logger.debug("Raw outputs: %s", raw_outputs[:100]) |
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logger.debug("Chunk response length: %d chars", len(chunk_response)) |
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return chunk_response |
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futures = [process_chunk(prompt) for prompt in batch_prompts] |
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batch_responses = await asyncio.gather(*futures) |
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all_responses.extend([resp.strip() for resp in batch_responses if resp.strip()]) |
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torch.cuda.empty_cache() |
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gc.collect() |
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summary = summarize_findings(all_responses) |
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history.append({"role": "assistant", "content": "Analysis complete. See summary below."}) |
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yield history, None, summary |
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|
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report_path = os.path.join(report_dir, f"{file_hash_value}_report.txt") if file_hash_value else None |
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if report_path: |
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with open(report_path, "w", encoding="utf-8") as f: |
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f.write(summary) |
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yield history, report_path if report_path and os.path.exists(report_path) else None, summary |
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|
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except Exception as e: |
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logger.error("Analysis error: %s", e) |
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history.append({"role": "assistant", "content": f"❌ Error occurred: {str(e)}"}) |
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yield history, None, f"### Comprehensive Clinical Oversight Summary\nError occurred during analysis: {str(e)}" |
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send_btn.click(analyze, inputs=[msg_input, gr.State([]), file_upload], outputs=[chatbot, download_output, final_summary]) |
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msg_input.submit(analyze, inputs=[msg_input, gr.State([]), file_upload], outputs=[chatbot, download_output, final_summary]) |
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return demo |
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|
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if __name__ == "__main__": |
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try: |
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logger.info("Launching app...") |
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agent = init_agent() |
|
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demo = create_ui(agent) |
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demo.queue(api_open=False).launch( |
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server_name="0.0.0.0", |
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server_port=7860, |
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show_error=True, |
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allowed_paths=[report_dir], |
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share=False |
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) |
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finally: |
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if torch.distributed.is_initialized(): |
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torch.distributed.destroy_process_group() |
