|
|
|
|
|
|
|
|
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 |
|
|
|
|
|
os.environ["VLLM_LOGGING_LEVEL"] = "DEBUG" |
|
|
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_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, 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: |
|
|
print("PDF processing error:", str(e)) |
|
|
traceback.print_exc() |
|
|
return 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: |
|
|
print("Error processing", file_path, str(e)) |
|
|
traceback.print_exc() |
|
|
return json.dumps({"error": 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}") |
|
|
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("<h1 style='text-align: center;'>🩺 Clinical Oversight Assistant</h1>") |
|
|
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: |
|
|
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=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[:8000]} |
|
|
|
|
|
### Potential Oversights: |
|
|
""" |
|
|
print("🔎 Generated prompt:") |
|
|
print(prompt) |
|
|
|
|
|
full_response = "" |
|
|
response_chunks = [] |
|
|
tool_calls_rendered = [] |
|
|
|
|
|
for chunk in agent.run_gradio_chat( |
|
|
message=prompt, |
|
|
history=[], |
|
|
temperature=0.2, |
|
|
max_new_tokens=2048, |
|
|
max_token=4096, |
|
|
call_agent=False, |
|
|
conversation=[] |
|
|
): |
|
|
if chunk is None: |
|
|
continue |
|
|
chunk_content = chunk if isinstance(chunk, str) else getattr(chunk, 'content', '') |
|
|
if not chunk_content: |
|
|
continue |
|
|
response_chunks.append(chunk_content) |
|
|
full_response = "".join(response_chunks) |
|
|
|
|
|
matches = re.findall(r"\[TOOL_CALLS\]\[(.*?)\]", chunk_content, re.DOTALL) |
|
|
for m in matches: |
|
|
tool_calls_rendered.append(f"\n📦 Tool Call: [{m.strip()}]") |
|
|
|
|
|
display_response = re.sub(r"\[TOOL_CALLS\].*?\n*", "", full_response, flags=re.DOTALL) |
|
|
display_response = display_response.replace('[TxAgent]', '').strip() |
|
|
display_response += "\n\n" + "\n".join(tool_calls_rendered) |
|
|
|
|
|
if history and history[-1]["role"] == "assistant": |
|
|
history[-1]["content"] = display_response |
|
|
else: |
|
|
history.append({"role": "assistant", "content": display_response}) |
|
|
|
|
|
yield history, None |
|
|
|
|
|
full_response = re.sub(r"\[TOOL_CALLS\].*?\n*", "", full_response, flags=re.DOTALL).strip() |
|
|
full_response = full_response.replace('[TxAgent]', '').strip() |
|
|
|
|
|
report_path = None |
|
|
if file_hash_value: |
|
|
report_path = os.path.join(report_dir, f"{file_hash_value}_report.txt") |
|
|
with open(report_path, "w", encoding="utf-8") as f: |
|
|
f.write(full_response) |
|
|
|
|
|
if history 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: |
|
|
history.append({"role": "assistant", "content": f"❌ An error occurred in analyze: {str(e)}"}) |
|
|
traceback.print_exc() |
|
|
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() |
