import sys import os import pandas as pd import pdfplumber import json import gradio as gr from typing import List, Optional from concurrent.futures import ThreadPoolExecutor, as_completed import hashlib import shutil import time from functools import lru_cache # Environment and path setup current_dir = os.path.dirname(os.path.abspath(__file__)) src_path = os.path.abspath(os.path.join(current_dir, "src")) print(f"Adding to path: {src_path}") sys.path.insert(0, src_path) # Configure cache directories 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") os.makedirs(model_cache_dir, exist_ok=True) os.makedirs(tool_cache_dir, exist_ok=True) os.makedirs(file_cache_dir, exist_ok=True) os.environ["TRANSFORMERS_CACHE"] = model_cache_dir os.environ["HF_HOME"] = model_cache_dir os.environ["TOKENIZERS_PARALLELISM"] = "false" os.environ["CUDA_LAUNCH_BLOCKING"] = "1" from txagent.txagent import TxAgent 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() @lru_cache(maxsize=100) def get_cached_response(prompt: str, file_hash: str) -> Optional[str]: return None 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): return open(cache_path, "r", encoding="utf-8").read() if file_type == "csv": df = pd.read_csv(file_path, encoding_errors="replace", header=None, dtype=str, skip_blank_lines=False, on_bad_lines="skip") 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) elif file_type == "pdf": with pdfplumber.open(file_path) as pdf: text = "\n".join([page.extract_text() or "" for page in pdf.pages]) result = json.dumps({"filename": os.path.basename(file_path), "content": text.strip()}) with open(cache_path, "w", encoding="utf-8") as f: f.write(result) return result else: return json.dumps({"error": f"Unsupported file type: {file_type}"}) if df is None or df.empty: return json.dumps({"warning": f"No data extracted from: {file_path}"}) df = df.fillna("") content = df.astype(str).values.tolist() result = json.dumps({"filename": os.path.basename(file_path), "rows": content}) 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 reading {os.path.basename(file_path)}: {str(e)}"}) def convert_files_to_json_parallel(uploaded_files: list) -> str: extracted_text = [] with ThreadPoolExecutor(max_workers=4) as executor: futures = [] for file in uploaded_files: if not hasattr(file, 'name'): continue path = file.name ext = path.split(".")[-1].lower() futures.append(executor.submit(convert_file_to_json, path, ext)) for future in as_completed(futures): extracted_text.append(sanitize_utf8(future.result())) return "\n".join(extracted_text) 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) model_name = "mims-harvard/TxAgent-T1-Llama-3.1-8B" rag_model_name = "mims-harvard/ToolRAG-T1-GTE-Qwen2-1.5B" agent = TxAgent( model_name=model_name, rag_model_name=rag_model_name, 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() return agent def create_ui(agent: TxAgent): with gr.Blocks(theme=gr.themes.Soft()) as demo: gr.Markdown("

📋 CPS: Clinical Patient Support System

") chatbot = gr.Chatbot(label="CPS Assistant", height=600, type="messages") file_upload = gr.File( label="Upload Medical File", file_types=[".pdf", ".txt", ".docx", ".jpg", ".png", ".csv", ".xls", ".xlsx"], file_count="multiple" ) message_input = gr.Textbox(placeholder="Ask a biomedical question or just upload the files...", show_label=False) send_button = gr.Button("Send", variant="primary") conversation_state = gr.State([]) def handle_chat(message: str, history: list, conversation: list, uploaded_files: list, progress=gr.Progress()): start_time = time.time() try: history.append({"role": "user", "content": message}) history.append({"role": "assistant", "content": "⏳ Processing your request..."}) yield history file_process_time = time.time() extracted_text = "" if uploaded_files and isinstance(uploaded_files, list): extracted_text = convert_files_to_json_parallel(uploaded_files) print(f"File processing took: {time.time() - file_process_time:.2f}s") context = ( "You are an expert clinical AI assistant. Review this patient's history, " "medications, and notes, and ONLY provide a final answer summarizing " "what the doctor might have missed." ) chunked_prompt = f"{context}\n\n--- Patient Record ---\n{extracted_text}\n\n[Final Analysis]" model_start = time.time() generator = agent.run_gradio_chat( message=chunked_prompt, history=[], temperature=0.3, max_new_tokens=768, max_token=4096, call_agent=False, conversation=conversation, uploaded_files=uploaded_files, max_round=10 ) final_response = [] for update in generator: if not update: continue if isinstance(update, str): final_response.append(update) elif isinstance(update, list): final_response.extend(msg.content for msg in update if hasattr(msg, 'content')) if len(final_response) % 3 == 0: history[-1] = {"role": "assistant", "content": "".join(final_response).strip()} yield history history[-1] = {"role": "assistant", "content": "".join(final_response).strip() or "❌ No response."} print(f"Model processing took: {time.time() - model_start:.2f}s") yield history except Exception as chat_error: print(f"Chat handling error: {chat_error}") history[-1] = {"role": "assistant", "content": "❌ An error occurred while processing your request."} yield history finally: print(f"Total request time: {time.time() - start_time:.2f}s") inputs = [message_input, chatbot, conversation_state, file_upload] send_button.click(fn=handle_chat, inputs=inputs, outputs=chatbot) message_input.submit(fn=handle_chat, inputs=inputs, outputs=chatbot) gr.Examples([ ["Upload your medical form and ask what the doctor might've missed."], ["This patient was treated with antibiotics for UTI. What else should we check?"], ["Is there anything abnormal in the attached blood work report?"] ], inputs=message_input) return demo if __name__ == "__main__": print("Initializing agent...") agent = init_agent() print("Performing warm-up call...") try: warm_up = agent.run_gradio_chat( message="Warm up", history=[], temperature=0.1, max_new_tokens=10, max_token=100, call_agent=False, conversation=[] ) for _ in warm_up: pass except: pass print("Launching interface...") demo = create_ui(agent) demo.queue().launch( server_name="0.0.0.0", server_port=7860, show_error=True, share=True )