app.py

import sys
import os
import pandas as pd
import pdfplumber
import json
import gradio as gr
from typing import List, Tuple
from concurrent.futures import ThreadPoolExecutor, as_completed
import hashlib
import shutil
import re
import psutil
import subprocess
import logging
from datetime import datetime

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    handlers=[
        logging.StreamHandler(),
        logging.FileHandler('clinical_oversight.log')
    ]
)
logger = logging.getLogger(__name__)

# Persistent directory
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:
        logger.error(f"Error extracting pages from PDF: {str(e)}")
        return f"PDF processing error: {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:
        logger.error(f"Error converting {file_type} file to JSON: {str(e)}")
        return json.dumps({"error": f"Error processing {os.path.basename(file_path)}: {str(e)}"})

def log_system_usage(tag=""):
    try:
        cpu = psutil.cpu_percent(interval=1)
        mem = psutil.virtual_memory()
        logger.info(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(", ")
            logger.info(f"[{tag}] GPU: {used}MB / {total}MB | Utilization: {util}%")
    except Exception as e:
        logger.error(f"[{tag}] GPU/CPU monitor failed: {e}")

def init_agent():
    logger.info("🔁 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")
    logger.info("✅ Agent Ready")
    return agent

def format_response_for_ui(response: str) -> str:
    """Formats the raw response for clean display in the UI"""
    # Remove any tool call metadata
    cleaned = response.split("[TOOL_CALLS]")[0].strip()
    
    # If we have a structured response, format it nicely
    if "Potential missed diagnoses" in cleaned or "Flagged medication conflicts" in cleaned:
        # Add markdown formatting for better readability
        formatted = []
        for line in cleaned.split("\n"):
            if line.startswith("Potential missed diagnoses"):
                formatted.append(f"### 🔍 Potential Missed Diagnoses")
            elif line.startswith("Flagged medication conflicts"):
                formatted.append(f"\n### ⚠️ Flagged Medication Conflicts")
            elif line.startswith("Incomplete assessments"):
                formatted.append(f"\n### 📋 Incomplete Assessments")
            elif line.startswith("Highlighted abnormal results"):
                formatted.append(f"\n### ❗ Abnormal Results Needing Follow-up")
            else:
                formatted.append(line)
        return "\n".join(formatted)
    return cleaned

def analyze(message: str, history: List[Tuple[str, str]], files: list):
    start_time = datetime.now()
    logger.info(f"Starting analysis for message: {message[:100]}...")
    if files:
        logger.info(f"Processing {len(files)} uploaded files")

    # Initialize chat history in the correct format if empty
    if history is None:
        history = []
    
    # Add user message to history
    history.append([message, None])
    yield history, None

    extracted = ""
    file_hash_value = ""
    if files:
        try:
            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 = [sanitize_utf8(f.result()) for f in as_completed(futures)]
                extracted = "\n".join(results)
                file_hash_value = file_hash(files[0].name)
                logger.info(f"Processed {len(files)} files, extracted {len(extracted)} characters")
        except Exception as e:
            logger.error(f"Error processing files: {str(e)}")
            history[-1][1] = f"❌ Error processing files: {str(e)}"
            yield history, None
            return

    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[:12000]}

### Potential Oversights:
"""
    logger.info(f"Generated prompt with {len(prompt)} characters")

    response_chunks = []
    try:
        logger.info("Starting model inference...")
        for chunk in agent.run_gradio_chat(
            message=prompt,
            history=[],
            temperature=0.2,
            max_new_tokens=1024,
            max_token=4096,
            call_agent=False,
            conversation=[]
        ):
            if not chunk:
                continue
            if isinstance(chunk, str):
                response_chunks.append(chunk)
            elif isinstance(chunk, list):
                response_chunks.extend([c.content for c in chunk if hasattr(c, 'content')])
            
            partial_response = "".join(response_chunks)
            formatted_partial = format_response_for_ui(partial_response)
            
            if formatted_partial:
                history[-1][1] = formatted_partial
                yield history, None

        full_response = "".join(response_chunks)
        logger.info(f"Full model response received: {full_response[:500]}...")
        
        final_output = format_response_for_ui(full_response)
        if not final_output or len(final_output) < 20:  # Very short response
            final_output = "No clear oversights identified. Recommend comprehensive review."
            logger.info("No significant findings detected in analysis")
        
        history[-1][1] = final_output

        # Save report
        report_path = None
        if file_hash_value:
            report_path = os.path.join(report_dir, f"{file_hash_value}_report.txt")
            try:
                with open(report_path, "w", encoding="utf-8") as f:
                    f.write(final_output)
                logger.info(f"Saved report to {report_path}")
            except Exception as e:
                logger.error(f"Error saving report: {str(e)}")

        elapsed = (datetime.now() - start_time).total_seconds()
        logger.info(f"Analysis completed in {elapsed:.2f} seconds")
        yield history, report_path if report_path and os.path.exists(report_path) else None

    except Exception as e:
        logger.error(f"Error during analysis: {str(e)}", exc_info=True)
        history[-1][1] = f"❌ Error during analysis: {str(e)}"
        yield history, None

def create_ui(agent):
    with gr.Blocks(theme=gr.themes.Soft(), title="Clinical Oversight Assistant") as demo:
        gr.Markdown("<h1 style='text-align: center;'>🩺 Clinical Oversight Assistant</h1>")
        gr.Markdown("""
        <div style='text-align: center; margin-bottom: 20px;'>
            Upload medical records and receive analysis of potential oversights, including:<br>
            - Missed diagnoses - Medication conflicts - Incomplete assessments - Abnormal results needing follow-up
        </div>
        """)
        
        with gr.Row():
            with gr.Column(scale=2):
                file_upload = gr.File(
                    label="Upload Medical Records",
                    file_types=[".pdf", ".csv", ".xls", ".xlsx"],
                    file_count="multiple",
                    interactive=True
                )
                msg_input = gr.Textbox(
                    placeholder="Ask about potential oversights...",
                    show_label=False,
                    lines=3,
                    max_lines=5
                )
                send_btn = gr.Button("Analyze", variant="primary")
            
            with gr.Column(scale=3):
                chatbot = gr.Chatbot(
                    label="Analysis Results",
                    height=600,
                    bubble_full_width=False,
                    show_copy_button=True
                )
                download_output = gr.File(
                    label="Download Full Report",
                    interactive=False
                )

        # Examples for quick testing
        examples = gr.Examples(
            examples=[
                ["Are there any potential missed diagnoses in these records?"],
                ["What medication conflicts should I be aware of?"],
                ["Are there any incomplete assessments in this case?"]
            ],
            inputs=[msg_input],
            label="Example Questions"
        )

        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]
        )

        # Add some footer text
        gr.Markdown("""
        <div style='text-align: center; margin-top: 20px; color: #666; font-size: 0.9em;'>
            Note: This tool provides preliminary analysis only. Always verify findings with complete clinical evaluation.
        </div>
        """)

    return demo

if __name__ == "__main__":
    logger.info("🚀 Launching Clinical Oversight Assistant...")
    try:
        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:
        logger.error(f"Failed to launch application: {str(e)}", exc_info=True)
        raise