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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, Optional |
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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 time |
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from functools import lru_cache |
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from threading import Thread |
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import re |
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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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base_dir = "/data" |
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os.makedirs(base_dir, exist_ok=True) |
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model_cache_dir = os.path.join(base_dir, "txagent_models") |
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tool_cache_dir = os.path.join(base_dir, "tool_cache") |
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file_cache_dir = os.path.join(base_dir, "cache") |
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os.makedirs(model_cache_dir, exist_ok=True) |
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os.makedirs(tool_cache_dir, exist_ok=True) |
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os.makedirs(file_cache_dir, exist_ok=True) |
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os.environ.update({ |
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"TRANSFORMERS_CACHE": model_cache_dir, |
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"HF_HOME": model_cache_dir, |
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"TOKENIZERS_PARALLELISM": "false", |
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"CUDA_LAUNCH_BLOCKING": "1" |
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}) |
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from txagent.txagent import TxAgent |
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MEDICAL_KEYWORDS = { |
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'diagnosis', 'assessment', 'plan', 'results', 'medications', |
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'allergies', 'summary', 'impression', 'findings', 'recommendations' |
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} |
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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_chunks.append(f"=== Page {i+1} ===\n{(page.extract_text() or '').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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return open(cache_path, "r", encoding="utf-8").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({ |
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"filename": os.path.basename(file_path), |
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"content": text, |
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"status": "initial" |
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}) |
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Thread(target=full_pdf_processing, args=(file_path, h)).start() |
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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, 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: |
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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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return 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 full_pdf_processing(file_path: str, file_hash: str): |
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try: |
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cache_path = os.path.join(file_cache_dir, f"{file_hash}_full.json") |
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if os.path.exists(cache_path): |
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return |
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with pdfplumber.open(file_path) as pdf: |
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full_text = "\n".join([f"=== Page {i+1} ===\n{(page.extract_text() or '').strip()}" for i, page in enumerate(pdf.pages)]) |
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result = json.dumps({ |
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"filename": os.path.basename(file_path), |
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"content": full_text, |
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"status": "complete" |
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}) |
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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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except Exception as e: |
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print(f"Background processing failed: {str(e)}") |
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def init_agent(): |
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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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return agent |
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def create_ui(agent: TxAgent): |
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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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gr.Markdown("<h3 style='text-align: center;'>Identify potential oversights in patient care</h3>") |
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chatbot = gr.Chatbot(label="Analysis", height=600, type="messages") |
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file_upload = gr.File( |
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label="Upload Medical Records", |
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file_types=[".pdf", ".csv", ".xls", ".xlsx"], |
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file_count="multiple" |
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) |
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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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conversation_state = gr.State([]) |
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def analyze_potential_oversights(message: str, history: list, conversation: list, files: list): |
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start_time = time.time() |
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try: |
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history.append((message, "Analyzing records for potential oversights...")) |
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yield history |
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extracted_data = "" |
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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 if hasattr(f, 'name')] |
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extracted_data = "\n".join([sanitize_utf8(f.result()) for f in as_completed(futures)]) |
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analysis_prompt = """Review these medical records and identify EXACTLY what might have been missed: |
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1. List potential missed diagnoses |
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2. Flag any medication conflicts |
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3. Note incomplete assessments |
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4. Highlight abnormal results needing follow-up |
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Medical Records: |
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{records} |
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Provide ONLY the potential oversights in this format: |
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### Potential Oversights: |
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1. [Missed diagnosis] - [Evidence from records] |
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2. [Medication issue] - [Supporting data] |
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3. [Assessment gap] - [Relevant findings]""".format(records=extracted_data[:15000]) |
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response = [] |
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for chunk in agent.run_gradio_chat( |
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message=analysis_prompt, |
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history=[], |
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temperature=0.2, |
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max_new_tokens=1024, |
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max_token=4096, |
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call_agent=False, |
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conversation=conversation |
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): |
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if isinstance(chunk, str): |
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response.append(chunk) |
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elif isinstance(chunk, list): |
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response.extend([c.content for c in chunk if hasattr(c, 'content')]) |
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if len(response) % 3 == 0: |
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history[-1] = (message, "".join(response).strip()) |
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yield history |
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final_output = "".join(response).strip() |
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if not final_output: |
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final_output = "No clear oversights identified. Recommend comprehensive review." |
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if not final_output.startswith(("1.", "-", "*", "#")): |
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final_output = "• " + final_output.replace("\n", "\n• ") |
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history[-1] = (message, f"### Potential Clinical Oversights:\n{final_output}") |
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print(f"Analysis completed in {time.time()-start_time:.2f}s") |
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yield history |
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except Exception as e: |
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history.append((message, f"❌ Analysis failed: {str(e)}")) |
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yield history |
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inputs = [msg_input, chatbot, conversation_state, file_upload] |
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send_btn.click(analyze_potential_oversights, inputs=inputs, outputs=chatbot) |
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msg_input.submit(analyze_potential_oversights, inputs=inputs, outputs=chatbot) |
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gr.Examples([ |
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["What might have been missed in this patient's treatment?"], |
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["Are there any medication conflicts in these records?"], |
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["What abnormal results require follow-up?"] |
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], inputs=msg_input) |
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return demo |
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if __name__ == "__main__": |
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print("Initializing medical analysis agent...") |
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agent = init_agent() |
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print("Launching interface...") |
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demo = create_ui(agent) |
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demo.queue().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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share=True |
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) |
