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
import multiprocessing
from functools import partial
import time

# 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

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_page_range(file_path: str, start_page: int, end_page: int) -> str:
    """Extract text from a range of PDF pages."""
    try:
        text_chunks = []
        with pdfplumber.open(file_path) as pdf:
            for page in pdf.pages[start_page:end_page]:
                page_text = page.extract_text() or ""
                text_chunks.append(f"=== Page {start_page + pdf.pages.index(page) + 1} ===\n{page_text.strip()}")
        return "\n\n".join(text_chunks)
    except Exception:
        return ""

def extract_all_pages(file_path: str, progress_callback=None) -> str:
    """Extract text from all pages of a PDF using parallel processing."""
    try:
        with pdfplumber.open(file_path) as pdf:
            total_pages = len(pdf.pages)
        
        if total_pages == 0:
            return ""
        
        # Use 6 processes (adjust based on CPU cores)
        num_processes = min(6, multiprocessing.cpu_count())
        pages_per_process = max(1, total_pages // num_processes)
        
        # Create page ranges for parallel processing
        ranges = [(i * pages_per_process, min((i + 1) * pages_per_process, total_pages))
                  for i in range(num_processes)]
        if ranges[-1][1] != total_pages:
            ranges[-1] = (ranges[-1][0], total_pages)
        
        # Process page ranges in parallel
        with multiprocessing.Pool(processes=num_processes) as pool:
            extract_func = partial(extract_page_range, file_path)
            results = []
            for idx, result in enumerate(pool.starmap(extract_func, ranges)):
                results.append(result)
                if progress_callback:
                    processed_pages = min((idx + 1) * pages_per_process, total_pages)
                    progress_callback(processed_pages, total_pages)
        
        return "\n\n".join(filter(None, results))
    except Exception as e:
        return f"PDF processing error: {str(e)}"

def convert_file_to_json(file_path: str, file_type: str, progress_callback=None) -> 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_all_pages(file_path, progress_callback)
            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:
    """Clean TxAgent response to group findings under tool-derived headings."""
    text = sanitize_utf8(text)
    # Remove tool call artifacts, None, and reasoning
    text = re.sub(r"\[.*?\]|\bNone\b|To analyze the patient record excerpt.*?medications\.|Since the previous attempts.*?\.|I need to.*?medications\.|Retrieving tools.*?\.", "", text, flags=re.DOTALL)
    # Remove extra whitespace and non-markdown content
    text = re.sub(r"\n{3,}", "\n\n", text)
    text = re.sub(r"[^\n#\-\*\w\s\.\,\:\(\)]+", "", text)  # Keep markdown-relevant characters
    
    # Define tool-to-heading mapping
    tool_to_heading = {
        "get_abuse_info_by_drug_name": "Drugs",
        "get_dependence_info_by_drug_name": "Drugs",
        "get_abuse_types_and_related_adverse_reactions_and_controlled_substance_status_by_drug_name": "Drugs",
        "get_info_for_patients_by_drug_name": "Drugs",
        # Add other tools from new_tool.json if applicable
    }
    
    # Parse sections and findings
    sections = {}
    current_section = None
    current_tool = None
    lines = text.splitlines()
    for line in lines:
        line = line.strip()
        if not line:
            continue
        # Detect tool tag
        tool_match = re.match(r"\[TOOL:\s*(\w+)\]", line)
        if tool_match:
            current_tool = tool_match.group(1)
            continue
        # Detect section heading
        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)
            if current_section not in sections:
                sections[current_section] = []
            continue
        # Detect finding
        finding_match = re.match(r"-\s*.+", line)
        if finding_match and current_section and not re.match(r"-\s*No issues identified", line):
            # Assign to tool-derived heading if tool is specified
            if current_tool and current_tool in tool_to_heading:
                heading = tool_to_heading[current_tool]
                if heading not in sections:
                    sections[heading] = []
                sections[heading].append(line)
            else:
                sections[current_section].append(line)
    
    # Combine non-empty sections
    cleaned = []
    for heading, findings in sections.items():
        if findings:  # Only include sections with findings
            cleaned.append(f"### {heading}\n" + "\n".join(findings))
    
    text = "\n\n".join(cleaned).strip()
    if not text:
        text = ""  # Return empty string if no valid findings
    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=4,
        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": "⏳ Extracting text from files..."})
            yield history, None

            extracted = ""
            file_hash_value = ""
            if files:
                # Progress callback for extraction
                total_pages = 0
                processed_pages = 0
                def update_extraction_progress(current, total):
                    nonlocal processed_pages, total_pages
                    processed_pages = current
                    total_pages = total
                    animation = ["🌀", "🔄", "⚙️", "🔃"][(int(time.time() * 2) % 4)]
                    history[-1] = {"role": "assistant", "content": f"Extracting text... {animation} Page {processed_pages}/{total_pages}"}
                    return history, None

                with ThreadPoolExecutor(max_workers=6) as executor:
                    futures = [executor.submit(convert_file_to_json, f.name, f.name.split(".")[-1].lower(), update_extraction_progress) 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 ""

            history.pop()  # Remove extraction message
            history.append({"role": "assistant", "content": "✅ Text extraction complete."})
            yield history, None

            # Split extracted text into chunks of ~6,000 characters
            chunk_size = 6000
            chunks = [extracted[i:i + chunk_size] for i in range(0, len(extracted), chunk_size)]
            combined_response = ""

            prompt_template = """
You are a medical analysis assistant. Analyze the following patient record excerpt for clinical oversights and provide a concise, evidence-based summary in markdown format. Group findings under appropriate headings based on the tool used (e.g., drug-related findings under 'Drugs'). For each finding, include:
- Clinical context (why the issue was missed or relevant details from the record).
- Potential risks if unaddressed (e.g., disease progression, adverse events).
- Actionable recommendations (e.g., tests, referrals, medication adjustments).
Output ONLY the markdown-formatted findings, with bullet points under each heading. Precede each finding with a tool tag (e.g., [TOOL: get_abuse_info_by_drug_name]) to indicate the tool used. Do NOT include reasoning, tool calls, or intermediate steps. If no issues are found for a tool or category, state "No issues identified" for that section. Ensure the output is specific to the provided text and avoids generic responses.

Example Output:
### Drugs
[TOOL: get_abuse_info_by_drug_name]
- Opioid use disorder not addressed. Missed due to lack of screening. Risks: overdose. Recommend: addiction specialist referral.
### Missed Diagnoses
- Elevated BP noted without diagnosis. Missed due to inconsistent visits. Risks: stroke. Recommend: BP monitoring, antihypertensives.
### Incomplete Assessments
- Chest pain not evaluated. Time constraints likely cause. Risks: cardiac issues. Recommend: ECG, stress test.
### Urgent Follow-up
- Abnormal creatinine not addressed. Delayed lab review. Risks: renal failure. Recommend: nephrology referral.

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

            try:
                # Process each chunk and stream results in real-time
                for chunk_idx, chunk in enumerate(chunks, 1):
                    # Update UI with chunk progress
                    animation = ["🔍", "📊", "🧠", "🔎"][(int(time.time() * 2) % 4)]
                    history.append({"role": "assistant", "content": f"Analyzing records... {animation} Chunk {chunk_idx}/{len(chunks)}"})
                    yield history, None

                    prompt = prompt_template.format(chunk_idx, len(chunks), chunk=chunk[:4000])  # Truncate to avoid token limits
                    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 and re.search(r"###\s*\w+", cleaned):
                                        chunk_response += cleaned + "\n\n"
                                        # Update UI with partial response
                                        if history[-1]["content"].startswith("Analyzing"):
                                            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 and re.search(r"###\s*\w+", cleaned):
                                chunk_response += cleaned + "\n\n"
                                # Update UI with partial response
                                if history[-1]["content"].startswith("Analyzing"):
                                    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"
                    else:
                        combined_response += f"--- Analysis for Chunk {chunk_idx} ---\nNo oversights identified for this chunk.\n\n"

                # Finalize UI with complete response
                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."})

                # Generate report file
                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
    )