app.py

import sys
import os
import pandas as pd
import pdfplumber
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

# 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) -> str:
    try:
        text_chunks = []
        with pdfplumber.open(file_path) as pdf:
            for i, page in enumerate(pdf.pages):
                page_text = page.extract_text() or ""
                if i < 3 or any(re.search(rf'\b{kw}\b', page_text.lower()) for kw in MEDICAL_KEYWORDS):
                    text_chunks.append(f"=== Page {i+1} ===\n{page_text.strip()}")
        return "\n\n".join(text_chunks)
    except Exception as 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:
        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()
        print(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(", ")
            print(f"[{tag}] GPU: {used}MB / {total}MB | Utilization: {util}%")
    except Exception as e:
        print(f"[{tag}] GPU/CPU monitor failed: {e}")

def clean_response(text: str) -> str:
    text = sanitize_utf8(text)
    # Remove tool calls, JSON data, and repetitive phrases
    text = re.sub(r"\[TOOL_CALLS\].*", "", text, flags=re.DOTALL)
    text = re.sub(r"\['get_[^\]]+\']\n?", "", text)  # Remove tool names
    text = re.sub(r"\{'meta':\s*\{.*?\}\s*,\s*'results':\s*\[.*?\]\}\n?", "", text, flags=re.DOTALL)  # Remove JSON
    text = re.sub(r"To analyze the medical records for clinical oversights.*?begin by reviewing.*?\n", "", text, flags=re.DOTALL)
    text = re.sub(r"\n{3,}", "\n\n", text).strip()
    # Only keep text under analysis headings or relevant content
    if not re.search(r"(Missed Diagnoses|Medication Conflicts|Incomplete Assessments|Urgent Follow-up)", text):
        return ""
    return text

def init_agent():
    print("🔁 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=2,
        seed=100,
        additional_default_tools=[],
    )
    agent.init_model()
    log_system_usage("After Load")
    print("✅ 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="Analysis", height=600, type="messages")
        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")

        def analyze(message: str, history: List[dict], files: List):
            history.append({"role": "user", "content": message})
            history.append({"role": "assistant", "content": "⏳ Analyzing records for potential oversights..."})
            yield history, None

            extracted = ""
            file_hash_value = ""
            if files:
                with ThreadPoolExecutor(max_workers=6) 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) if files else ""

            # Split extracted text into chunks of ~4,000 characters
            chunk_size = 4000
            chunks = [extracted[i:i + chunk_size] for i in range(0, len(extracted), chunk_size)]
            combined_response = ""

            prompt_template = f"""
Analyze the medical records for clinical oversights. Provide a concise, evidence-based summary under these headings:

1. **Missed Diagnoses**:
   - Identify inconsistencies in history, symptoms, or tests.
   - Consider psychiatric, neurological, infectious, autoimmune, genetic conditions, family history, trauma, and developmental factors.

2. **Medication Conflicts**:
   - Check for contraindications, interactions, or unjustified off-label use.
   - Assess if medications worsen diagnoses or cause adverse effects.

3. **Incomplete Assessments**:
   - Note missing or superficial cognitive, psychiatric, social, or family assessments.
   - Highlight gaps in medical history, substance use, or lab/imaging documentation.

4. **Urgent Follow-up**:
   - Flag abnormal lab results, imaging, behaviors, or legal history needing immediate reassessment or referral.

Medical Records (Chunk {0} of {1}):
{{chunk}}

Begin analysis:
"""

            try:
                if history and history[-1]["content"].startswith("⏳"):
                    history.pop()

                # Process each chunk and stream cleaned results
                for chunk_idx, chunk in enumerate(chunks, 1):
                    # Update UI with progress
                    history.append({"role": "assistant", "content": f"🔄 Processing Chunk {chunk_idx} of {len(chunks)}..."})
                    yield history, None

                    prompt = prompt_template.format(chunk_idx, len(chunks), chunk=chunk)
                    chunk_response = ""
                    for chunk_output in agent.run_gradio_chat(
                        message=prompt,
                        history=[],
                        temperature=0.2,
                        max_new_tokens=1024,
                        max_token=4096,
                        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:
                                        chunk_response += cleaned + "\n"
                                        # Stream partial response to UI
                                        if history[-1]["content"].startswith("🔄"):
                                            history[-1] = {"role": "assistant", "content": f"--- Analysis for Chunk {chunk_idx} ---\n{chunk_response.strip()}"}
                                        else:
                                            history[-1]["content"] = f"--- Analysis for Chunk {chunk_idx} ---\n{chunk_response.strip()}"
                                        yield history, None
                        elif isinstance(chunk_output, str) and chunk_output.strip():
                            cleaned = clean_response(chunk_output)
                            if cleaned:
                                chunk_response += cleaned + "\n"
                                # Stream partial response to UI
                                if history[-1]["content"].startswith("🔄"):
                                    history[-1] = {"role": "assistant", "content": f"--- Analysis for Chunk {chunk_idx} ---\n{chunk_response.strip()}"}
                                else:
                                    history[-1]["content"] = f"--- Analysis for Chunk {chunk_idx} ---\n{chunk_response.strip()}"
                                yield history, None

                    # Append completed chunk response to combined response
                    if chunk_response:
                        combined_response += f"--- Analysis for Chunk {chunk_idx} ---\n{chunk_response}\n"

                # Finalize UI with complete response
                if combined_response:
                    history[-1]["content"] = combined_response.strip()
                else:
                    history.append({"role": "assistant", "content": "No oversights identified."})

                # Generate report file with cleaned 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)
                yield history, report_path if report_path and os.path.exists(report_path) else None

            except Exception as e:
                print("🚨 ERROR:", e)
                history.append({"role": "assistant", "content": f"❌ Error occurred: {str(e)}"})
                yield history, None

        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])
    return demo

if __name__ == "__main__":
    print("🚀 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
    )