app.py

import sys
import os
import pandas as pd
import pdfplumber
import json
import gradio as gr
from typing import List, Dict, Any
from concurrent.futures import ThreadPoolExecutor, as_completed
import hashlib
import shutil
import re
import psutil
import subprocess
import logging
import traceback
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:
    """Ensure text is UTF-8 encoded and clean."""
    try:
        return text.encode("utf-8", "ignore").decode("utf-8")
    except Exception as e:
        logger.error(f"UTF-8 sanitization failed: {str(e)}")
        return ""

def file_hash(path: str) -> str:
    """Generate MD5 hash of file content."""
    try:
        with open(path, "rb") as f:
            return hashlib.md5(f.read()).hexdigest()
    except Exception as e:
        logger.error(f"File hash generation failed for {path}: {str(e)}")
        return ""

def extract_priority_pages(file_path: str, max_pages: int = 20) -> str:
    """Extract pages from PDF with priority given to pages containing medical keywords."""
    try:
        text_chunks = []
        logger.info(f"Extracting pages from {file_path}")
        
        with pdfplumber.open(file_path) as pdf:
            # Always extract first 3 pages
            for i, page in enumerate(pdf.pages[:3]):
                try:
                    text = page.extract_text() or ""
                    text_chunks.append(f"=== Page {i+1} ===\n{text.strip()}")
                except Exception as page_error:
                    logger.warning(f"Error processing page {i+1}: {str(page_error)}")
                    text_chunks.append(f"=== Page {i+1} ===\n[Error extracting content]")
            
            # Extract remaining pages that contain medical keywords
            for i, page in enumerate(pdf.pages[3:max_pages], start=4):
                try:
                    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()}")
                except Exception as page_error:
                    logger.warning(f"Error processing page {i}: {str(page_error)}")
        
        return "\n\n".join(text_chunks)
    except Exception as e:
        logger.error(f"PDF processing error for {file_path}: {str(e)}")
        return f"PDF processing error: {str(e)}"

def convert_file_to_json(file_path: str, file_type: str) -> str:
    """Convert different file types to JSON format with caching."""
    try:
        h = file_hash(file_path)
        if not h:
            return json.dumps({"error": "Could not generate file hash"})
            
        cache_path = os.path.join(file_cache_dir, f"{h}.json")
        
        # Check cache first
        if os.path.exists(cache_path):
            try:
                with open(cache_path, "r", encoding="utf-8") as f:
                    return f.read()
            except Exception as cache_error:
                logger.error(f"Cache read error for {file_path}: {str(cache_error)}")
        
        result = {}
        try:
            if file_type == "pdf":
                text = extract_priority_pages(file_path)
                result = {
                    "filename": os.path.basename(file_path),
                    "content": text,
                    "status": "initial",
                    "file_type": "pdf"
                }
            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 = {
                    "filename": os.path.basename(file_path),
                    "rows": content,
                    "file_type": "csv"
                }
            elif file_type in ["xls", "xlsx"]:
                try:
                    df = pd.read_excel(file_path, engine="openpyxl", header=None, dtype=str)
                except Exception:
                    try:
                        df = pd.read_excel(file_path, engine="xlrd", header=None, dtype=str)
                    except Exception as excel_error:
                        logger.error(f"Excel read error for {file_path}: {str(excel_error)}")
                        raise
                content = df.fillna("").astype(str).values.tolist()
                result = {
                    "filename": os.path.basename(file_path),
                    "rows": content,
                    "file_type": "excel"
                }
            else:
                result = {"error": f"Unsupported file type: {file_type}"}
            
            json_result = json.dumps(result)
            
            # Save to cache
            try:
                with open(cache_path, "w", encoding="utf-8") as f:
                    f.write(json_result)
            except Exception as cache_write_error:
                logger.error(f"Cache write error for {file_path}: {str(cache_write_error)}")
            
            return json_result
        except Exception as processing_error:
            logger.error(f"Error processing {file_path}: {str(processing_error)}")
            return json.dumps({"error": f"Error processing {os.path.basename(file_path)}: {str(processing_error)}"})
    except Exception as e:
        logger.error(f"Unexpected error in convert_file_to_json: {str(e)}")
        return json.dumps({"error": f"Unexpected error processing file: {str(e)}"})

def log_system_usage(tag=""):
    """Log system resource usage including CPU, RAM, and GPU."""
    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")
        
        try:
            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 gpu_error:
            logger.warning(f"[{tag}] GPU monitor failed: {gpu_error}")
    except Exception as e:
        logger.error(f"System usage logging failed: {str(e)}")

def init_agent():
    """Initialize the TxAgent with proper configuration."""
    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")
    
    try:
        if not os.path.exists(target_tool_path):
            shutil.copy(default_tool_path, target_tool_path)
            logger.info("Copied default tool configuration")
    except Exception as e:
        logger.error(f"Tool configuration copy failed: {str(e)}")
        raise

    try:
        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 initialization successful")
        return agent
    except Exception as e:
        logger.error(f"Agent initialization failed: {str(e)}")
        raise

def save_report(content: str, file_hash_value: str = "") -> str:
    """Save analysis report to file and return path."""
    try:
        if not file_hash_value:
            file_hash_value = hashlib.md5(content.encode()).hexdigest()
        
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        report_filename = f"report_{timestamp}_{file_hash_value[:8]}.txt"
        report_path = os.path.join(report_dir, report_filename)
        
        with open(report_path, "w", encoding="utf-8") as f:
            f.write(content)
        
        logger.info(f"Report saved to {report_path}")
        return report_path
    except Exception as e:
        logger.error(f"Failed to save report: {str(e)}")
        return ""

def clean_response(content: str) -> str:
    """Clean up model response by removing tool call artifacts."""
    if not content:
        return "⚠️ No content generated."
    
    try:
        # Remove tool call artifacts
        cleaned = re.sub(r"\[TOOL_CALLS\].*?(?=(\[|\Z))", "", content, flags=re.DOTALL).strip()
        # Remove excessive whitespace
        cleaned = re.sub(r"\n{3,}", "\n\n", cleaned)
        return cleaned or "⚠️ Empty response after cleaning."
    except Exception as e:
        logger.error(f"Response cleaning failed: {str(e)}")
        return content

def process_model_response(chunk: Any, history: List[Dict[str, str]]) -> List[Dict[str, str]]:
    """Process model response chunk and update chat history."""
    try:
        if chunk is None:
            return history
            
        if isinstance(chunk, list) and all(hasattr(m, 'role') and hasattr(m, 'content') for m in chunk):
            for m in chunk:
                cleaned_content = clean_response(m.content)
                history.append({"role": m.role, "content": cleaned_content})
        elif isinstance(chunk, str):
            cleaned_chunk = clean_response(chunk)
            if history and history[-1]["role"] == "assistant":
                history[-1]["content"] += cleaned_chunk
            else:
                history.append({"role": "assistant", "content": cleaned_chunk})
        else:
            logger.warning(f"Unexpected response type: {type(chunk)}")
            
        return history
    except Exception as e:
        logger.error(f"Error processing model response: {str(e)}")
        history.append({"role": "assistant", "content": f"⚠️ Error processing response: {str(e)}"})
        return history

def analyze(message: str, history: list, files: list):
    """Main analysis function that processes files and generates responses."""
    try:
        # Initial response
        new_history = history.copy()
        new_history.append({"role": "user", "content": message})
        new_history.append({"role": "assistant", "content": "⏳ Analyzing records for potential oversights..."})
        yield new_history, None
        
        # Process files
        extracted = ""
        file_hash_value = ""
        if files:
            logger.info(f"Processing {len(files)} files...")
            with ThreadPoolExecutor(max_workers=4) as executor:
                futures = []
                for f in files:
                    try:
                        file_type = f.name.split(".")[-1].lower()
                        futures.append(executor.submit(convert_file_to_json, f.name, file_type))
                    except Exception as e:
                        logger.error(f"Error submitting file {f.name} for processing: {str(e)}")
                        new_history.append({"role": "system", "content": f"⚠️ Error processing {f.name}: {str(e)}"})
                
                results = []
                for f in as_completed(futures):
                    try:
                        results.append(sanitize_utf8(f.result()))
                    except Exception as e:
                        logger.error(f"Error getting file processing result: {str(e)}")
                        results.append(json.dumps({"error": "File processing failed"}))
                
                extracted = "\n".join(results)
                try:
                    file_hash_value = file_hash(files[0].name) if files else ""
                except Exception as e:
                    logger.error(f"Error generating file hash: {str(e)}")
                    file_hash_value = ""
        
        # Prepare prompt
        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"Prompt length: {len(prompt)} characters")
        
        # Initialize agent response
        agent = init_agent()
        response_content = ""
        report_path = ""
        
        # Process agent response
        for chunk in agent.run_gradio_chat(
            message=prompt,
            history=[],
            temperature=0.2,
            max_new_tokens=2048,
            max_token=4096,
            call_agent=False,
            conversation=[],
        ):
            try:
                new_history = process_model_response(chunk, new_history)
                if isinstance(chunk, str):
                    response_content += clean_response(chunk)
                
                yield new_history, None
            except Exception as chunk_error:
                logger.error(f"Error processing chunk: {str(chunk_error)}")
                new_history.append({"role": "assistant", "content": f"⚠️ Error processing response chunk: {str(chunk_error)}"})
                yield new_history, None
        
        # Save final report
        if response_content:
            try:
                report_path = save_report(response_content, file_hash_value)
            except Exception as report_error:
                logger.error(f"Error saving report: {str(report_error)}")
                new_history.append({"role": "system", "content": "⚠️ Failed to save full report"})
        
        yield new_history, report_path if report_path and os.path.exists(report_path) else None
        
    except Exception as e:
        logger.error(f"Analysis error: {str(e)}\n{traceback.format_exc()}")
        error_history = history.copy()
        error_history.append({"role": "assistant", "content": f"❌ Critical error occurred: {str(e)}"})
        yield error_history, None

def create_ui(agent):
    """Create Gradio UI interface."""
    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 ask about potential oversights or missed diagnoses.
        </div>
        """)
        
        with gr.Row():
            with gr.Column(scale=2):
                chatbot = gr.Chatbot(
                    label="Analysis Conversation",
                    height=600,
                    bubble_full_width=False,
                    show_copy_button=True
                )
                msg_input = gr.Textbox(
                    placeholder="Ask about potential oversights...",
                    show_label=False,
                    container=False
                )
                with gr.Row():
                    send_btn = gr.Button("Analyze", variant="primary")
                    clear_btn = gr.Button("Clear")
                
            with gr.Column(scale=1):
                file_upload = gr.File(
                    file_types=[".pdf", ".csv", ".xls", ".xlsx"],
                    file_count="multiple",
                    label="Upload Medical Records"
                )
                download_output = gr.File(
                    label="Download Full Report",
                    interactive=False
                )
                gr.Markdown("""
                <div style='margin-top: 20px; font-size: 0.9em; color: #666;'>
                    <b>Note:</b> The system analyzes PDFs, CSVs, and Excel files for potential clinical oversights.
                </div>
                """)
        
        # Event handlers
        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]
        )
        
        clear_btn.click(
            lambda: ([], None),
            inputs=[],
            outputs=[chatbot, download_output]
        )
        
        # Add some examples
        gr.Examples(
            examples=[
                ["What potential diagnoses might have been missed in these records?"],
                ["Are there any medication conflicts I should be aware of?"],
                ["What abnormal results need follow-up in these reports?"]
            ],
            inputs=msg_input,
            label="Example Questions"
        )
    
    return demo

if __name__ == "__main__":
    try:
        logger.info("🚀 Launching Clinical Oversight Assistant...")
        agent = init_agent()
        demo = create_ui(agent)
        
        demo.queue(
            api_open=False,
            concurrency_count=2
        ).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"Application failed to start: {str(e)}\n{traceback.format_exc()}")
        raise