app.py

import sys
import os
import pandas as pd
import json
import gradio as gr
from typing import List
from concurrent.futures import ThreadPoolExecutor, as_completed
import hashlib
import shutil
import re
import psutil
import subprocess
import logging
import torch
import gc
from diskcache import Cache
import time
import asyncio
import pypdfium2 as pdfium
import pytesseract
from PIL import Image
import io

# Configure logging and suppress warnings
logging.basicConfig(level=logging.INFO)
logging.getLogger("pdfminer").setLevel(logging.ERROR)
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

# Initialize cache with 10GB limit
cache = Cache(file_cache_dir, size_limit=10 * 1024**3)

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()

async def extract_all_pages_async(file_path: str, progress_callback=None, use_ocr=False) -> str:
    try:
        pdf = pdfium.PdfDocument(file_path)
        total_pages = len(pdf)
        if total_pages == 0:
            return ""

        batch_size = 5
        batches = [(i, min(i + batch_size, total_pages)) for i in range(0, total_pages, batch_size)]
        text_chunks = [""] * total_pages
        processed_pages = 0

        def extract_batch(start: int, end: int) -> List[tuple]:
            results = []
            for i in range(start, end):
                page = pdf[i]
                text = page.get_textpage().get_text_range() or ""
                if not text.strip() and use_ocr:
                    # Fallback to OCR
                    bitmap = page.render(scale=2).to_pil()
                    text = pytesseract.image_to_string(bitmap, lang="eng")
                results.append((i, f"=== Page {i + 1} ===\n{text.strip()}"))
            return results

        loop = asyncio.get_event_loop()
        with ThreadPoolExecutor(max_workers=4) as executor:
            futures = [loop.run_in_executor(executor, extract_batch, start, end) for start, end in batches]
            for future in await asyncio.gather(*futures):
                for page_num, text in future:
                    text_chunks[page_num] = text
                    logger.debug("Page %d extracted: %s...", page_num + 1, text[:50])
                processed_pages += batch_size
                if progress_callback:
                    progress_callback(min(processed_pages, total_pages), total_pages)

        pdf.close()
        extracted_text = "\n\n".join(filter(None, text_chunks))
        logger.info("Extracted %d pages, total length: %d chars", total_pages, len(extracted_text))
        return extracted_text
    except Exception as e:
        logger.error("PDF processing error: %s", e)
        return f"PDF processing error: {str(e)}"

def convert_file_to_json(file_path: str, file_type: str, progress_callback=None) -> str:
    try:
        file_h = file_hash(file_path)
        cache_key = f"{file_h}_{file_type}"
        if cache_key in cache:
            logger.info("Using cached extraction for %s", file_path)
            return cache[cache_key]

        if file_type == "pdf":
            # Try without OCR first, fallback to OCR if empty
            text = asyncio.run(extract_all_pages_async(file_path, progress_callback, use_ocr=False))
            if not text.strip() or "PDF processing error" in text:
                logger.info("Retrying extraction with OCR for %s", file_path)
                text = asyncio.run(extract_all_pages_async(file_path, progress_callback, use_ocr=True))
            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}"})

        cache[cache_key] = result
        logger.info("Cached extraction for %s, size: %d bytes", file_path, len(result))
        return result
    except Exception as e:
        logger.error("Error processing %s: %s", os.path.basename(file_path), 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("[%s] CPU: %.1f%% | RAM: %dMB / %dMB", tag, cpu, mem.used // (1024**2), mem.total // (1024**2))
        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("[%s] GPU: %sMB / %sMB | Utilization: %s%%", tag, used, total, util)
    except Exception as e:
        logger.error("[%s] GPU/CPU monitor failed: %s", tag, e)

def clean_response(text: str) -> str:
    text = sanitize_utf8(text)
    text = text.replace("[", "").replace("]", "").replace("None", "")  # Faster string ops
    text = text.replace("\n\n\n", "\n\n")
    sections = {}
    current_section = None
    for line in text.splitlines():
        line = line.strip()
        if not line:
            continue
        section_match = re.match(r"###\s*(Missed Diagnoses|Medication Conflicts|Incomplete Assessments|Urgent Follow-up)", line)
        if section_match:
            current_section = section_match.group(1)
            sections.setdefault(current_section, [])
            continue
        if current_section and line.startswith("- ") and "No issues identified" not in line:
            sections[current_section].append(line)
    cleaned = [f"### {heading}\n" + "\n".join(findings) for heading, findings in sections.items() if findings]
    result = "\n\n".join(cleaned).strip()
    logger.debug("Cleaned response length: %d chars", len(result))
    return result or ""

def summarize_findings(combined_response: str) -> str:
    if not combined_response or all("No oversights identified" in chunk for chunk in combined_response.split("--- Analysis for Chunk")):
        return "### Summary of Clinical Oversights\nNo critical oversights identified in the provided records."
    sections = {}
    current_section = None
    for line in combined_response.splitlines():
        line = line.strip()
        if not line:
            continue
        section_match = re.match(r"###\s*(Missed Diagnoses|Medication Conflicts|Incomplete Assessments|Urgent Follow-up)", line)
        if section_match:
            current_section = section_match.group(1)
            sections.setdefault(current_section, [])
            continue
        if current_section and line.startswith("- "):
            sections[current_section].append(line[2:])
    summary_lines = [
        f"- **{heading}**: {'; '.join(findings[:1])}. Risks: potential adverse outcomes. Recommend: urgent review."
        for heading, findings in sections.items() if findings
    ]
    result = "### Summary of Clinical Oversights\n" + "\n".join(summary_lines) if summary_lines else "### Summary of Clinical Oversights\nNo critical oversights identified."
    logger.debug("Summary length: %d chars", len(result))
    return result

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=False,
        enable_rag=False,
        init_rag_num=0,
        step_rag_num=0,
        seed=100,
        additional_default_tools=[],
    )
    agent.init_model()
    log_system_usage("After Load")
    logger.info("Agent Ready")
    return agent

def create_ui(agent):
    with gr.Blocks(theme=gr.themes.Soft()) as demo:
        gr.Markdown("<h1 style='text-align: center;'>🩺 Clinical Oversight Assistant</h1>")
        chatbot = gr.Chatbot(label="Detailed Analysis", height=600, type="messages")
        final_summary = gr.Markdown(label="Summary of Clinical Oversights")
        file_upload = gr.File(file_types=[".pdf", ".csv", ".xls", ".xlsx"], file_count="multiple")
        msg_input = gr.Textbox(placeholder="Ask about potential oversights...", show_label=False)
        send_btn = gr.Button("Analyze", variant="primary")
        download_output = gr.File(label="Download Full Report")
        progress_bar = gr.Progress()

        prompt_template = """
Analyze the patient record excerpt for clinical oversights. Provide a concise, evidence-based summary in markdown with findings grouped under headings (e.g., 'Missed Diagnoses'). For each finding, include clinical context, risks, and recommendations. Output only markdown bullet points under headings. If no issues, state "No issues identified".

Patient Record Excerpt (Chunk {0} of {1}):
{chunk}
"""

        async def analyze(message: str, history: List[dict], files: List, progress=gr.Progress()):
            history.append({"role": "user", "content": message})
            yield history, None, ""

            extracted = ""
            file_hash_value = ""
            if files:
                def update_extraction_progress(current, total):
                    progress(current / total, desc=f"Extracting text... Page {current}/{total}")
                    return history, None, ""

                futures = [convert_file_to_json(f.name, f.name.split(".")[-1].lower(), update_extraction_progress) for f in files]
                results = [sanitize_utf8(future) for future in futures]
                extracted = "\n".join(results)
                file_hash_value = file_hash(files[0].name) if files else ""

                history.append({"role": "assistant", "content": "✅ Text extraction complete."})
                yield history, None, ""
                logger.info("Extracted text length: %d chars", len(extracted))

            chunk_size = 4000  # Increased slightly
            chunks = [extracted[i:i + chunk_size] for i in range(0, len(extracted), chunk_size)]
            logger.info("Created %d chunks", len(chunks))
            combined_response = ""
            batch_size = 2

            try:
                for batch_idx in range(0, len(chunks), batch_size):
                    batch_chunks = chunks[batch_idx:batch_idx + batch_size]
                    batch_prompts = [prompt_template.format(i + 1, len(chunks), chunk=chunk[:2000]) for i, chunk in enumerate(batch_chunks)]
                    batch_responses = []

                    progress((batch_idx + 1) / len(chunks), desc=f"Analyzing chunks {batch_idx + 1}-{min(batch_idx + batch_size, len(chunks))}/{len(chunks)}")

                    async def process_chunk(prompt):
                        chunk_response = ""
                        for chunk_output in agent.run_gradio_chat(
                            message=prompt, history=[], temperature=0.2, max_new_tokens=128, max_token=768, call_agent=False, conversation=[]
                        ):
                            if chunk_output is None:
                                continue
                            if isinstance(chunk_output, list):
                                for m in chunk_output:
                                    if hasattr(m, 'content') and m.content:
                                        cleaned = clean_response(m.content)
                                        if cleaned and re.search(r"###\s*\w+", cleaned):
                                            chunk_response += cleaned + "\n\n"
                            elif isinstance(chunk_output, str) and chunk_output.strip():
                                cleaned = clean_response(chunk_output)
                                if cleaned and re.search(r"###\s*\w+", cleaned):
                                    chunk_response += cleaned + "\n\n"
                        logger.debug("Chunk response length: %d chars", len(chunk_response))
                        return chunk_response

                    futures = [process_chunk(prompt) for prompt in batch_prompts]
                    batch_responses = await asyncio.gather(*futures)
                    torch.cuda.empty_cache()
                    gc.collect()

                    for chunk_idx, chunk_response in enumerate(batch_responses, batch_idx + 1):
                        if chunk_response:
                            combined_response += f"--- Analysis for Chunk {chunk_idx} ---\n{chunk_response}\n"
                        else:
                            combined_response += f"--- Analysis for Chunk {chunk_idx} ---\nNo oversights identified for this chunk.\n\n"
                    history[-1] = {"role": "assistant", "content": combined_response.strip()}
                    yield history, None, ""

                if combined_response.strip() and not all("No oversights identified" in chunk for chunk in combined_response.split("--- Analysis for Chunk")):
                    history[-1]["content"] = combined_response.strip()
                else:
                    history.append({"role": "assistant", "content": "No oversights identified in the provided records."})

                summary = summarize_findings(combined_response)
                report_path = os.path.join(report_dir, f"{file_hash_value}_report.txt") if file_hash_value else None
                if report_path:
                    with open(report_path, "w", encoding="utf-8") as f:
                        f.write(combined_response + "\n\n" + summary)
                yield history, report_path if report_path and os.path.exists(report_path) else None, summary

            except Exception as e:
                logger.error("Analysis error: %s", e)
                history.append({"role": "assistant", "content": f"❌ Error occurred: {str(e)}"})
                yield history, None, f"### Summary of Clinical Oversights\nError occurred during analysis: {str(e)}"

        send_btn.click(analyze, inputs=[msg_input, gr.State([]), file_upload], outputs=[chatbot, download_output, final_summary])
        msg_input.submit(analyze, inputs=[msg_input, gr.State([]), file_upload], outputs=[chatbot, download_output, final_summary])
    return demo

if __name__ == "__main__":
    try:
        logger.info("Launching app...")
        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
        )
    finally:
        if torch.distributed.is_initialized():
            torch.distributed.destroy_process_group()