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import sys |
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import os |
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import pdfplumber |
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import json |
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import gradio as gr |
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from typing import List |
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from concurrent.futures import ThreadPoolExecutor, as_completed |
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import hashlib |
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import re |
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import psutil |
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import subprocess |
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from collections import defaultdict |
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persistent_dir = os.getenv("HF_HOME", "/data/hf_cache") |
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os.makedirs(persistent_dir, exist_ok=True) |
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model_cache_dir = os.path.join(persistent_dir, "txagent_models") |
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file_cache_dir = os.path.join(persistent_dir, "cache") |
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report_dir = os.path.join(persistent_dir, "reports") |
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vllm_cache_dir = os.path.join(persistent_dir, "vllm_cache") |
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for directory in [model_cache_dir, file_cache_dir, report_dir, vllm_cache_dir]: |
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os.makedirs(directory, exist_ok=True) |
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os.environ["HF_HOME"] = model_cache_dir |
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os.environ["TRANSFORMERS_CACHE"] = model_cache_dir |
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os.environ["VLLM_CACHE_DIR"] = vllm_cache_dir |
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os.environ["TOKENIZERS_PARALLELISM"] = "false" |
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os.environ["CUDA_LAUNCH_BLOCKING"] = "1" |
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os.environ["VLLM_NO_TORCH_COMPILE"] = "1" |
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current_dir = os.path.dirname(os.path.abspath(__file__)) |
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src_path = os.path.abspath(os.path.join(current_dir, "src")) |
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sys.path.insert(0, src_path) |
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from txagent.txagent import TxAgent |
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def sanitize_utf8(text: str) -> str: |
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return text.encode("utf-8", "ignore").decode("utf-8") |
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def file_hash(path: str) -> str: |
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with open(path, "rb") as f: |
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return hashlib.md5(f.read()).hexdigest() |
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def extract_all_pages(file_path: str) -> str: |
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try: |
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text_chunks = [] |
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with pdfplumber.open(file_path) as pdf: |
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for page in pdf.pages: |
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page_text = page.extract_text() or "" |
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text_chunks.append(page_text.strip()) |
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return "\n".join(text_chunks) |
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except Exception as e: |
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return f"PDF processing error: {str(e)}" |
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def convert_file_to_json(file_path: str, file_type: str) -> str: |
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try: |
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h = file_hash(file_path) |
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cache_path = os.path.join(file_cache_dir, f"{h}.json") |
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if os.path.exists(cache_path): |
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with open(cache_path, "r", encoding="utf-8") as f: |
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return f.read() |
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if file_type == "pdf": |
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text = extract_all_pages(file_path) |
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result = json.dumps({"filename": os.path.basename(file_path), "content": text, "status": "initial"}) |
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else: |
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result = json.dumps({"error": f"Unsupported file type: {file_type}"}) |
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with open(cache_path, "w", encoding="utf-8") as f: |
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f.write(result) |
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return result |
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except Exception as e: |
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return json.dumps({"error": f"Error processing {os.path.basename(file_path)}: {str(e)}"}) |
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def log_system_usage(tag=""): |
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try: |
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cpu = psutil.cpu_percent(interval=1) |
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mem = psutil.virtual_memory() |
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print(f"[{tag}] CPU: {cpu}% | RAM: {mem.used // (1024**2)}MB / {mem.total // (1024**2)}MB") |
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result = subprocess.run( |
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["nvidia-smi", "--query-gpu=memory.used,memory.total,utilization.gpu", "--format=csv,nounits,noheader"], |
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capture_output=True, text=True |
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) |
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if result.returncode == 0: |
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used, total, util = result.stdout.strip().split(", ") |
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print(f"[{tag}] GPU: {used}MB / {total}MB | Utilization: {util}%") |
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except Exception as e: |
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print(f"[{tag}] GPU/CPU monitor failed: {e}") |
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def clean_response(text: str) -> str: |
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text = sanitize_utf8(text) |
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text = re.sub(r"\[TOOL_CALLS\].*?\n|\[.*?\].*?\n|(?:get_|tool\s|retrieve\s|use\s|rag\s).*?\n", "", text, flags=re.DOTALL | re.IGNORECASE) |
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text = re.sub(r"\{'meta':\s*\{.*?\}\s*,\s*'results':\s*\[.*?\]\}\n?", "", text, flags=re.DOTALL) |
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text = re.sub( |
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r"(?i)(to\s|analyze|will\s|since\s|no\s|none|previous|attempt|involve|check\s|explore|manually|" |
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r"start|look|use|focus|retrieve|tool|based\s|overall|indicate|mention|consider|ensure|need\s|" |
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r"provide|review|assess|identify|potential|records|patient|history|symptoms|medication|" |
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r"conflict|assessment|follow-up|issue|reasoning|step|prompt|address|rag|thought|try|john\sdoe|nkma).*?\n", |
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"", text, flags=re.DOTALL |
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) |
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text = re.sub(r"\n{2,}", "\n", text).strip() |
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lines = [] |
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valid_heading = False |
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for line in text.split("\n"): |
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line = line.strip() |
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if line.lower() in ["missed diagnoses:", "medication conflicts:", "incomplete assessments:", "urgent follow-up:"]: |
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valid_heading = True |
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lines.append(f"**{line[:-1]}**:") |
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elif valid_heading and line.startswith("-"): |
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lines.append(line) |
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else: |
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valid_heading = False |
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return "\n".join(lines).strip() |
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def normalize_text(text: str) -> str: |
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return re.sub(r"\s+", " ", text.lower().strip()) |
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def consolidate_findings(responses: List[str]) -> str: |
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findings = defaultdict(set) |
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headings = ["Missed Diagnoses", "Medication Conflicts", "Incomplete Assessments", "Urgent Follow-up"] |
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for response in responses: |
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if not response: |
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continue |
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current_heading = None |
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for line in response.split("\n"): |
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line = line.strip() |
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if not line: |
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continue |
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if line.lower().startswith(tuple(h.lower() + ":" for h in headings)): |
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current_heading = next(h for h in headings if line.lower().startswith(h.lower() + ":")) |
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elif current_heading and line.startswith("-"): |
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findings[current_heading].add(normalize_text(line)) |
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output = [] |
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for heading in headings: |
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if findings[heading]: |
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output.append(f"**{heading}**:") |
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original_lines = {normalize_text(r): r for r in sum([r.split("\n") for r in responses], []) if r.startswith("-")} |
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output.extend(sorted(original_lines.get(n, "- " + n) for n in findings[heading])) |
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return "\n".join(output).strip() if output else "No oversights identified." |
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def init_agent(): |
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print("🔁 Initializing model...") |
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log_system_usage("Before Load") |
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agent = TxAgent( |
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model_name="mims-harvard/TxAgent-T1-Llama-3.1-8B", |
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rag_model_name="mims-harvard/ToolRAG-T1-GTE-Qwen2-1.5B", |
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force_finish=True, |
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enable_checker=False, |
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enable_rag=False, |
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tool_files_dict=None, |
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step_rag_num=0, |
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seed=100, |
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) |
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agent.init_model() |
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log_system_usage("After Load") |
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print("✅ Agent Ready") |
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return agent |
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def create_ui(agent): |
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with gr.Blocks(theme=gr.themes.Soft()) as demo: |
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gr.Markdown("<h1 style='text-align: center;'>🩺 Clinical Oversight Assistant</h1>") |
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chatbot = gr.Chatbot(label="Analysis", height=600, type="messages") |
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file_upload = gr.File(file_types=[".pdf"], file_count="multiple") |
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msg_input = gr.Textbox(placeholder="Ask about potential oversights...", show_label=False) |
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send_btn = gr.Button("Analyze", variant="primary") |
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download_output = gr.File(label="Download Report") |
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def analyze(message: str, history: List[dict], files: List): |
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history.append({"role": "user", "content": message}) |
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history.append({"role": "assistant", "content": "🔄 Analyzing..."}) |
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yield history, None |
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extracted = "" |
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file_hash_value = "" |
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if files: |
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with ThreadPoolExecutor(max_workers=6) as executor: |
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futures = [executor.submit(convert_file_to_json, f.name, f.name.split(".")[-1].lower()) for f in files] |
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results = [sanitize_utf8(f.result()) for f in as_completed(futures)] |
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extracted = "\n".join([json.loads(r).get("content", "") for r in results if "content" in json.loads(r)]) |
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file_hash_value = file_hash(files[0].name) if files else "" |
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chunk_size = 800 |
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chunks = [extracted[i:i + chunk_size] for i in range(0, len(extracted), chunk_size)] |
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chunk_responses = [] |
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batch_size = 4 |
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total_chunks = len(chunks) |
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prompt_template = """ |
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Output only oversights under these headings, one point each. No tools, reasoning, or extra text. |
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**Missed Diagnoses**: |
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**Medication Conflicts**: |
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**Incomplete Assessments**: |
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**Urgent Follow-up**: |
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Records: |
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{chunk} |
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""" |
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try: |
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for i in range(0, len(chunks), batch_size): |
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batch = chunks[i:i + batch_size] |
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batch_responses = [] |
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for j, chunk in enumerate(batch): |
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prompt = prompt_template.format(chunk=chunk) |
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chunk_response = "" |
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for output in agent.run_gradio_chat( |
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message=prompt, |
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history=[], |
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temperature=0.1, |
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max_new_tokens=96, |
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max_token=4096, |
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call_agent=False, |
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conversation=[], |
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): |
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if output is None: |
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continue |
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if isinstance(output, list): |
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for m in output: |
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if hasattr(m, 'content') and m.content: |
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cleaned = clean_response(m.content) |
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if cleaned: |
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chunk_response += cleaned + "\n" |
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elif isinstance(output, str) and output.strip(): |
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cleaned = clean_response(output) |
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if cleaned: |
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chunk_response += cleaned + "\n" |
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if chunk_response: |
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batch_responses.append(chunk_response) |
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processed = min(i + j + 1, total_chunks) |
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history[-1]["content"] = f"🔄 Analyzing... ({processed}/{total_chunks} chunks)" |
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yield history, None |
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chunk_responses.extend(batch_responses) |
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final_response = consolidate_findings(chunk_responses) |
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history[-1]["content"] = final_response |
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yield history, None |
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report_path = os.path.join(report_dir, f"{file_hash_value}_report.txt") if file_hash_value else None |
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if report_path and final_response != "No oversights identified.": |
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with open(report_path, "w", encoding="utf-8") as f: |
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f.write(final_response) |
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yield history, report_path if report_path and os.path.exists(report_path) else None |
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except Exception as e: |
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print("🚨 ERROR:", e) |
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history[-1]["content"] = f"❌ Error: {str(e)}" |
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yield history, None |
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send_btn.click(analyze, inputs=[msg_input, gr.State([]), file_upload], outputs=[chatbot, download_output]) |
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msg_input.submit(analyze, inputs=[msg_input, gr.State([]), file_upload], outputs=[chatbot, download_output]) |
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return demo |
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if __name__ == "__main__": |
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print("🚀 Launching app...") |
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agent = init_agent() |
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demo = create_ui(agent) |
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demo.queue(api_open=False).launch( |
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server_name="0.0.0.0", |
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server_port=7860, |
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show_error=True, |
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allowed_paths=[report_dir], |
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share=False |
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) |
