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 # Persistent directory 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) 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 = {'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) -> str: try: text_chunks = [] with pdfplumber.open(file_path) as pdf: for i, page in enumerate(pdf.pages): page_text = page.extract_text() or "" if i < 3 or any(re.search(rf'\b{kw}\b', page_text.lower()) for kw in MEDICAL_KEYWORDS): text_chunks.append(f"=== Page {i+1} ===\n{page_text.strip()}") return "\n\n".join(text_chunks) except Exception as e: return f"PDF processing error: {str(e)}" 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: return json.dumps({"error": f"Error processing {os.path.basename(file_path)}: {str(e)}"}) 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}") def clean_response(text: str) -> str: text = sanitize_utf8(text) text = re.sub(r"\[TOOL_CALLS\].*", "", text, flags=re.DOTALL) text = re.sub(r"\n{3,}", "\n\n", text).strip() return text def init_agent(): 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=4, seed=100, additional_default_tools=[], ) agent.init_model() log_system_usage("After Load") print("✅ Agent Ready") return agent 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[dict], files: List): history.append({"role": "user", "content": message}) history.append({"role": "assistant", "content": "⏳ Analyzing records for potential oversights..."}) yield history, None extracted = "" file_hash_value = "" if files: with ThreadPoolExecutor(max_workers=6) as executor: futures = [executor.submit(convert_file_to_json, f.name, f.name.split(".")[-1].lower()) for f in files] results = [sanitize_utf8(f.result()) for f in as_completed(futures)] extracted = "\n".join(results) file_hash_value = file_hash(files[0].name) if files else "" # Split extracted text into chunks of ~6,000 characters chunk_size = 6000 chunks = [extracted[i:i + chunk_size] for i in range(0, len(extracted), chunk_size)] combined_response = "" prompt_template = f""" Analyze the medical records for clinical oversights. Provide a concise, evidence-based summary under these headings: 1. **Missed Diagnoses**: - Identify inconsistencies in history, symptoms, or tests. - Consider psychiatric, neurological, infectious, autoimmune, genetic conditions, family history, trauma, and developmental factors. 2. **Medication Conflicts**: - Check for contraindications, interactions, or unjustified off-label use. - Assess if medications worsen diagnoses or cause adverse effects. 3. **Incomplete Assessments**: - Note missing or superficial cognitive, psychiatric, social, or family assessments. - Highlight gaps in medical history, substance use, or lab/imaging documentation. 4. **Urgent Follow-up**: - Flag abnormal lab results, imaging, behaviors, or legal history needing immediate reassessment or referral. Medical Records (Chunk {0} of {1}): {{chunk}} Begin analysis: """ try: if history and history[-1]["content"].startswith("⏳"): history.pop() # Process each chunk and stream results in real-time for chunk_idx, chunk in enumerate(chunks, 1): # Update UI with progress history.append({"role": "assistant", "content": f"🔄 Processing Chunk {chunk_idx} of {len(chunks)}..."}) yield history, None prompt = prompt_template.format(chunk_idx, len(chunks), chunk=chunk) chunk_response = "" for chunk_output in agent.run_gradio_chat( message=prompt, history=[], temperature=0.2, max_new_tokens=1024, max_token=4096, call_agent=False, conversation=[], ): if chunk_output is None: continue if isinstance(chunk_output, list): for m in chunk_output: if hasattr(m, 'content') and m.content: cleaned = clean_response(m.content) if cleaned: chunk_response += cleaned + "\n" # Update UI with partial response if history[-1]["content"].startswith("🔄"): history[-1] = {"role": "assistant", "content": f"--- Analysis for Chunk {chunk_idx} ---\n{chunk_response.strip()}"} else: history[-1]["content"] = f"--- Analysis for Chunk {chunk_idx} ---\n{chunk_response.strip()}" yield history, None elif isinstance(chunk_output, str) and chunk_output.strip(): cleaned = clean_response(chunk_output) if cleaned: chunk_response += cleaned + "\n" # Update UI with partial response if history[-1]["content"].startswith("🔄"): history[-1] = {"role": "assistant", "content": f"--- Analysis for Chunk {chunk_idx} ---\n{chunk_response.strip()}"} else: history[-1]["content"] = f"--- Analysis for Chunk {chunk_idx} ---\n{chunk_response.strip()}" yield history, None # Append completed chunk response to combined response combined_response += f"--- Analysis for Chunk {chunk_idx} ---\n{chunk_response}\n" # Finalize UI with complete response if combined_response: history[-1]["content"] = combined_response.strip() else: history.append({"role": "assistant", "content": "No oversights identified."}) # Generate report file report_path = os.path.join(report_dir, f"{file_hash_value}_report.txt") if file_hash_value else None if report_path: with open(report_path, "w", encoding="utf-8") as f: f.write(combined_response) yield history, report_path if report_path and os.path.exists(report_path) else None except Exception as e: print("🚨 ERROR:", e) history.append({"role": "assistant", "content": f"❌ Error occurred: {str(e)}"}) 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__": 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 )