#import sys, os, json, gradio as gr, pandas as pd, pdfplumber, hashlib, shutil, re, time from concurrent.futures import ThreadPoolExecutor, as_completed from threading import Thread # Setup current_dir = os.path.dirname(os.path.abspath(__file__)) src_path = os.path.join(current_dir, "src") sys.path.insert(0, src_path) base_dir = "/data" model_cache_dir = os.path.join(base_dir, "txagent_models") tool_cache_dir = os.path.join(base_dir, "tool_cache") file_cache_dir = os.path.join(base_dir, "cache") report_dir = os.path.join(base_dir, "reports") vllm_cache_dir = os.path.join(base_dir, "vllm_cache") for d in [model_cache_dir, tool_cache_dir, file_cache_dir, report_dir, vllm_cache_dir]: os.makedirs(d, exist_ok=True) # Hugging Face & Transformers cache os.environ.update({ "HF_HOME": model_cache_dir, "TRANSFORMERS_CACHE": model_cache_dir, "VLLM_CACHE_DIR": vllm_cache_dir, "TOKENIZERS_PARALLELISM": "false", "CUDA_LAUNCH_BLOCKING": "1" }) from txagent.txagent import TxAgent MEDICAL_KEYWORDS = {'diagnosis', 'assessment', 'plan', 'results', 'medications', 'allergies', 'summary', 'impression', 'findings', 'recommendations'} def sanitize_utf8(text): return text.encode("utf-8", "ignore").decode("utf-8") def file_hash(path): return hashlib.md5(open(path, "rb").read()).hexdigest() def extract_priority_pages(file_path, max_pages=20): try: with pdfplumber.open(file_path) as pdf: pages = [] for i, page in enumerate(pdf.pages[:3]): pages.append(f"=== Page {i+1} ===\n{(page.extract_text() or '').strip()}") for i, page in enumerate(pdf.pages[3:max_pages], start=4): text = page.extract_text() or "" if any(re.search(rf'\b{kw}\b', text.lower()) for kw in MEDICAL_KEYWORDS): pages.append(f"=== Page {i} ===\n{text.strip()}") return "\n\n".join(pages) except Exception as e: return f"PDF processing error: {str(e)}" def convert_file_to_json(file_path, file_type): try: h = file_hash(file_path) cache_path = os.path.join(file_cache_dir, f"{h}.json") if os.path.exists(cache_path): return open(cache_path, "r", encoding="utf-8").read() if file_type == "pdf": text = extract_priority_pages(file_path) result = json.dumps({"filename": os.path.basename(file_path), "content": text, "status": "initial"}) Thread(target=full_pdf_processing, args=(file_path, h)).start() 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") result = json.dumps({"filename": os.path.basename(file_path), "rows": df.fillna("").astype(str).values.tolist()}) elif file_type in ["xls", "xlsx"]: try: df = pd.read_excel(file_path, engine="openpyxl", header=None, dtype=str) except: df = pd.read_excel(file_path, engine="xlrd", header=None, dtype=str) result = json.dumps({"filename": os.path.basename(file_path), "rows": df.fillna("").astype(str).values.tolist()}) else: return 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 full_pdf_processing(file_path, file_hash_value): try: cache_path = os.path.join(file_cache_dir, f"{file_hash_value}_full.json") if os.path.exists(cache_path): return with pdfplumber.open(file_path) as pdf: full_text = "\n".join([f"=== Page {i+1} ===\n{(page.extract_text() or '').strip()}" for i, page in enumerate(pdf.pages)]) result = json.dumps({"filename": os.path.basename(file_path), "content": full_text, "status": "complete"}) with open(cache_path, "w", encoding="utf-8") as f: f.write(result) with open(os.path.join(report_dir, f"{file_hash_value}_report.txt"), "w", encoding="utf-8") as out: out.write(full_text) except Exception as e: print("PDF processing error:", e) def init_agent(): 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 ) agent.init_model() return agent # Lazy load agent only on first use agent_container = {"agent": None} def get_agent(): if agent_container["agent"] is None: agent_container["agent"] = init_agent() return agent_container["agent"] def create_ui(get_agent_func): with gr.Blocks(theme=gr.themes.Soft()) as demo: gr.Markdown("

🩺 Clinical Oversight Assistant

Identify potential oversights in patient care

") 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...") send_btn = gr.Button("Analyze", variant="primary") state = gr.State([]) download_output = gr.File(label="Download Report") def analyze(message, history, conversation, files): try: extracted_data, file_hash_value = "", "" if files: with ThreadPoolExecutor(max_workers=4) as pool: futures = [pool.submit(convert_file_to_json, f.name, f.name.split(".")[-1].lower()) for f in files] extracted_data = "\n".join([sanitize_utf8(f.result()) for f in as_completed(futures)]) 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:\n{extracted_data[:15000]} ### Potential Oversights:\n""" final_response = "" for chunk in get_agent_func().run_gradio_chat( message=prompt, history=[], temperature=0.2, max_new_tokens=1024, max_token=4096, call_agent=False, conversation=conversation ): if isinstance(chunk, str): final_response += chunk elif isinstance(chunk, list): final_response += "".join([c.content for c in chunk if hasattr(c, "content")]) cleaned = final_response.replace("[TOOL_CALLS]", "").strip() if not cleaned: cleaned = "No oversights found. Consider further review." updated_history = history + [{"role": "user", "content": message}, {"role": "assistant", "content": cleaned}] report_path = os.path.join(report_dir, f"{file_hash_value}_report.txt") if file_hash_value and os.path.exists(os.path.join(report_dir, f"{file_hash_value}_report.txt")) else None yield updated_history, report_path except Exception as e: updated_history = history + [{"role": "user", "content": message}, {"role": "assistant", "content": f"❌ Error: {str(e)}"}] yield updated_history, None send_btn.click(analyze, inputs=[msg_input, chatbot, state, file_upload], outputs=[chatbot, download_output]) msg_input.submit(analyze, inputs=[msg_input, chatbot, state, file_upload], outputs=[chatbot, download_output]) return demo if __name__ == "__main__": print("Launching interface...") ui = create_ui(get_agent) ui.queue(api_open=False).launch( server_name="0.0.0.0", server_port=7860, show_error=True, allowed_paths=["/data/reports"], share=False )