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import sys |
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import os |
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import pandas as pd |
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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 shutil |
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import re |
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import psutil |
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import subprocess |
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persistent_dir = "/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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tool_cache_dir = os.path.join(persistent_dir, "tool_cache") |
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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, tool_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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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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MEDICAL_KEYWORDS = {'diagnosis', 'assessment', 'plan', 'results', 'medications', |
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'allergies', 'summary', 'impression', 'findings', 'recommendations'} |
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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_priority_pages(file_path: str, max_pages: int = 20) -> 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 i, page in enumerate(pdf.pages[:3]): |
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text = page.extract_text() or "" |
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text_chunks.append(f"=== Page {i+1} ===\n{text.strip()}") |
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for i, page in enumerate(pdf.pages[3:max_pages], start=4): |
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page_text = page.extract_text() or "" |
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if any(re.search(rf'\b{kw}\b', page_text.lower()) for kw in MEDICAL_KEYWORDS): |
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text_chunks.append(f"=== Page {i} ===\n{page_text.strip()}") |
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return "\n\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_priority_pages(file_path) |
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result = json.dumps({"filename": os.path.basename(file_path), "content": text, "status": "initial"}) |
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elif file_type == "csv": |
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df = pd.read_csv(file_path, encoding_errors="replace", header=None, dtype=str, |
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skip_blank_lines=False, on_bad_lines="skip") |
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content = df.fillna("").astype(str).values.tolist() |
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result = json.dumps({"filename": os.path.basename(file_path), "rows": content}) |
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elif file_type in ["xls", "xlsx"]: |
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try: |
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df = pd.read_excel(file_path, engine="openpyxl", header=None, dtype=str) |
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except Exception: |
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df = pd.read_excel(file_path, engine="xlrd", header=None, dtype=str) |
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content = df.fillna("").astype(str).values.tolist() |
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result = json.dumps({"filename": os.path.basename(file_path), "rows": content}) |
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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"\[TOOL_CALLS\].*", "", text, flags=re.DOTALL) |
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text = re.sub(r"\n{3,}", "\n\n", text).strip() |
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return text |
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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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default_tool_path = os.path.abspath("data/new_tool.json") |
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target_tool_path = os.path.join(tool_cache_dir, "new_tool.json") |
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if not os.path.exists(target_tool_path): |
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shutil.copy(default_tool_path, target_tool_path) |
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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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tool_files_dict={"new_tool": target_tool_path}, |
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force_finish=True, |
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enable_checker=True, |
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step_rag_num=8, |
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seed=100, |
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additional_default_tools=[], |
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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", ".csv", ".xls", ".xlsx"], 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 Full 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 records for potential oversights..."}) |
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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=4) 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(results) |
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file_hash_value = file_hash(files[0].name) if files else "" |
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prompt = f""" |
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You are a clinical reasoning assistant. Analyze the following medical records with a focus on identifying potential clinical oversights. Your task is to provide a comprehensive and structured summary addressing the following: |
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1. **Potential Missed Diagnoses**: |
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- Consider any contradictory or missing evidence regarding the patient’s history, symptoms, or test results. |
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- Take into account psychiatric, neurological, infectious, autoimmune, and genetic conditions. |
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- Consider family history, trauma history, and childhood developmental information. |
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2. **Flagged Medication Conflicts**: |
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- Cross-check all prescribed medications for contraindications, interactions, or off-label use without justification. |
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- Verify if any medications may worsen known diagnoses or contribute to adverse effects. |
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3. **Incomplete or Missing Assessments**: |
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- Identify any domains of assessment that are completely missing or only superficially addressed (e.g., cognitive, psychiatric, social, family). |
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- Point out gaps in documentation of prior medical history, substance use, or lab/imaging results. |
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4. **Abnormal Results Needing Urgent Follow-up**: |
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- Extract and highlight lab results, imaging, behavioral observations, or legal history that warrant immediate reassessment or specialist referral. |
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Make the answer precise, evidence-based, and structured clearly under each heading. Avoid repeating input and remove tool-call formatting. |
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--- |
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Medical Records Input (Truncated to 12k chars): |
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{extracted[:12000]} |
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--- |
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Begin your analysis below: |
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""" |
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try: |
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if history and history[-1]["content"].startswith("⏳"): |
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history.pop() |
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for chunk in agent.run_gradio_chat( |
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message=prompt, |
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history=[], |
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temperature=0.2, |
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max_new_tokens=2048, |
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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 chunk is None: |
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continue |
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if isinstance(chunk, list): |
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for m in chunk: |
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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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history.append({"role": m.role, "content": cleaned}) |
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yield history, None |
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elif isinstance(chunk, str) and chunk.strip(): |
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cleaned = clean_response(chunk) |
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if cleaned: |
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if history and history[-1]["role"] == "assistant": |
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history[-1]["content"] += cleaned |
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else: |
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history.append({"role": "assistant", "content": cleaned}) |
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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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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.append({"role": "assistant", "content": f"❌ Error occurred: {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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) |
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