import sys import os import pandas as pd import pdfplumber import json import gradio as gr from typing import List from concurrent.futures import ThreadPoolExecutor, as_completed import hashlib import shutil import re import psutil import subprocess import traceback import torch # For checking CUDA availability # Set VLLM logging level to DEBUG for detailed output os.environ["VLLM_LOGGING_LEVEL"] = "DEBUG" # If no GPU is available, force CPU usage by hiding CUDA devices if not torch.cuda.is_available(): print("No GPU detected. Forcing CPU mode by setting CUDA_VISIBLE_DEVICES to an empty string.") os.environ["CUDA_VISIBLE_DEVICES"] = "" # Persistent directory setup persistent_dir = "/data/hf_cache" os.makedirs(persistent_dir, exist_ok=True) model_cache_dir = os.path.join(persistent_dir, "txagent_models") tool_cache_dir = os.path.join(persistent_dir, "tool_cache") file_cache_dir = os.path.join(persistent_dir, "cache") report_dir = os.path.join(persistent_dir, "reports") vllm_cache_dir = os.path.join(persistent_dir, "vllm_cache") for directory in [model_cache_dir, tool_cache_dir, file_cache_dir, report_dir, vllm_cache_dir]: os.makedirs(directory, exist_ok=True) # Update environment variables to use HF_HOME os.environ["HF_HOME"] = model_cache_dir os.environ["TRANSFORMERS_CACHE"] = model_cache_dir os.environ["VLLM_CACHE_DIR"] = vllm_cache_dir os.environ["TOKENIZERS_PARALLELISM"] = "false" os.environ["CUDA_LAUNCH_BLOCKING"] = "1" current_dir = os.path.dirname(os.path.abspath(__file__)) src_path = os.path.abspath(os.path.join(current_dir, "src")) sys.path.insert(0, src_path) from txagent.txagent import TxAgent # Medical keywords for processing PDF files MEDICAL_KEYWORDS = {'diagnosis', 'assessment', 'plan', 'results', 'medications', 'allergies', 'summary', 'impression', 'findings', 'recommendations'} def sanitize_utf8(text: str) -> str: return text.encode("utf-8", "ignore").decode("utf-8") def file_hash(path: str) -> str: with open(path, "rb") as f: return hashlib.md5(f.read()).hexdigest() def extract_priority_pages(file_path: str, max_pages: int = 20) -> str: try: text_chunks = [] with pdfplumber.open(file_path) as pdf: for i, page in enumerate(pdf.pages[:3]): text = page.extract_text() or "" text_chunks.append(f"=== Page {i+1} ===\n{text.strip()}") for i, page in enumerate(pdf.pages[3:max_pages], start=4): page_text = page.extract_text() or "" if any(re.search(rf'\b{kw}\b', page_text.lower()) for kw in MEDICAL_KEYWORDS): text_chunks.append(f"=== Page {i} ===\n{page_text.strip()}") return "\n\n".join(text_chunks) except Exception as e: debug_msg = f"PDF processing error: {str(e)}" print(debug_msg) traceback.print_exc() return debug_msg def convert_file_to_json(file_path: str, file_type: str) -> str: try: h = file_hash(file_path) cache_path = os.path.join(file_cache_dir, f"{h}.json") if os.path.exists(cache_path): with open(cache_path, "r", encoding="utf-8") as f: return f.read() if file_type == "pdf": text = extract_priority_pages(file_path) result = json.dumps({"filename": os.path.basename(file_path), "content": text, "status": "initial"}) elif file_type == "csv": df = pd.read_csv(file_path, encoding_errors="replace", header=None, dtype=str, skip_blank_lines=False, on_bad_lines="skip") content = df.fillna("").astype(str).values.tolist() result = json.dumps({"filename": os.path.basename(file_path), "rows": content}) elif file_type in ["xls", "xlsx"]: try: df = pd.read_excel(file_path, engine="openpyxl", header=None, dtype=str) except Exception: df = pd.read_excel(file_path, engine="xlrd", header=None, dtype=str) content = df.fillna("").astype(str).values.tolist() result = json.dumps({"filename": os.path.basename(file_path), "rows": content}) else: result = json.dumps({"error": f"Unsupported file type: {file_type}"}) with open(cache_path, "w", encoding="utf-8") as f: f.write(result) return result except Exception as e: error_msg = f"Error processing {os.path.basename(file_path)}: {str(e)}" print(error_msg) traceback.print_exc() return json.dumps({"error": error_msg}) def log_system_usage(tag=""): try: cpu = psutil.cpu_percent(interval=1) mem = psutil.virtual_memory() print(f"[{tag}] CPU: {cpu}% | RAM: {mem.used // (1024**2)}MB / {mem.total // (1024**2)}MB") result = subprocess.run( ["nvidia-smi", "--query-gpu=memory.used,memory.total,utilization.gpu", "--format=csv,nounits,noheader"], capture_output=True, text=True ) if result.returncode == 0: used, total, util = result.stdout.strip().split(", ") print(f"[{tag}] GPU: {used}MB / {total}MB | Utilization: {util}%") except Exception as e: print(f"[{tag}] GPU/CPU monitor failed: {e}") traceback.print_exc() def init_agent(): try: print("🔁 Initializing model...") log_system_usage("Before Load") default_tool_path = os.path.abspath("data/new_tool.json") target_tool_path = os.path.join(tool_cache_dir, "new_tool.json") if not os.path.exists(target_tool_path): shutil.copy(default_tool_path, target_tool_path) agent = TxAgent( model_name="mims-harvard/TxAgent-T1-Llama-3.1-8B", rag_model_name="mims-harvard/ToolRAG-T1-GTE-Qwen2-1.5B", tool_files_dict={"new_tool": target_tool_path}, force_finish=True, enable_checker=True, step_rag_num=8, seed=100, additional_default_tools=[], ) agent.init_model() log_system_usage("After Load") print("✅ Agent Ready") return agent except Exception as e: print("❌ Error initializing agent:", str(e)) traceback.print_exc() raise e def create_ui(agent): with gr.Blocks(theme=gr.themes.Soft()) as demo: gr.Markdown("

🩺 Clinical Oversight Assistant

") chatbot = gr.Chatbot(label="Analysis", height=600, type="messages") file_upload = gr.File(file_types=[".pdf", ".csv", ".xls", ".xlsx"], file_count="multiple") msg_input = gr.Textbox(placeholder="Ask about potential oversights...", show_label=False) send_btn = gr.Button("Analyze", variant="primary") download_output = gr.File(label="Download Full Report") def analyze(message: str, history: list, files: list): try: # Initialize response with loading message history.append({"role": "user", "content": message}) history.append({"role": "assistant", "content": "⏳ Analyzing records for potential oversights..."}) yield history, None # Process files in parallel extracted = "" file_hash_value = "" if files: with ThreadPoolExecutor(max_workers=4) as executor: futures = [ executor.submit(convert_file_to_json, f.name, f.name.split(".")[-1].lower()) for f in files ] results = [] for future in as_completed(futures): try: res = future.result() results.append(sanitize_utf8(res)) except Exception as e: print("❌ Error in file processing:", str(e)) traceback.print_exc() extracted = "\n".join(results) file_hash_value = file_hash(files[0].name) prompt = f"""Review these medical records and identify EXACTLY what might have been missed: 1. List potential missed diagnoses 2. Flag any medication conflicts 3. Note incomplete assessments 4. Highlight abnormal results needing follow-up Medical Records: {extracted[:12000]} ### Potential Oversights: """ print("🔎 Generated prompt:") print(prompt) # Initialize response tracking full_response = "" last_update_time = 0 response_chunks = [] # Process streaming response for chunk in agent.run_gradio_chat( message=prompt, history=[], temperature=0.2, max_new_tokens=2048, max_token=4096, call_agent=False, conversation=[] ): try: if chunk is None: continue # Handle different chunk types if isinstance(chunk, str): chunk_content = chunk elif isinstance(chunk, list): chunk_content = "".join([c.content for c in chunk if hasattr(c, "content") and c.content]) else: print("DEBUG: Received unknown type chunk", type(chunk)) continue if not chunk_content: continue response_chunks.append(chunk_content) full_response = "".join(response_chunks) # Update the chat history with the latest response if len(history) > 0 and history[-1]["role"] == "assistant": history[-1]["content"] = full_response else: history.append({"role": "assistant", "content": full_response}) yield history, None except Exception as e: print("❌ Error processing chunk:", str(e)) traceback.print_exc() continue # Final response handling if not full_response: full_response = "⚠️ No clear oversights identified or model output was invalid." # Save report if we have files report_path = None if file_hash_value: report_path = os.path.join(report_dir, f"{file_hash_value}_report.txt") try: with open(report_path, "w", encoding="utf-8") as f: f.write(full_response) except Exception as e: print("❌ Error saving report:", str(e)) traceback.print_exc() # Ensure the final response is in the history if len(history) > 0 and history[-1]["role"] == "assistant": history[-1]["content"] = full_response else: history.append({"role": "assistant", "content": full_response}) yield history, report_path if report_path and os.path.exists(report_path) else None except Exception as e: error_message = f"❌ An error occurred in analyze: {str(e)}" print(error_message) traceback.print_exc() history.append({"role": "assistant", "content": error_message}) yield history, None send_btn.click(analyze, inputs=[msg_input, gr.State([]), file_upload], outputs=[chatbot, download_output]) msg_input.submit(analyze, inputs=[msg_input, gr.State([]), file_upload], outputs=[chatbot, download_output]) return demo if __name__ == "__main__": try: print("🚀 Launching app...") agent = init_agent() demo = create_ui(agent) demo.queue(api_open=False).launch( server_name="0.0.0.0", server_port=7860, show_error=True, allowed_paths=[report_dir], share=False ) except Exception as e: print("❌ Fatal error during launch:", str(e)) traceback.print_exc()