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 logging

# Configure logging
logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
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"PDF processing error for {file_path}: {e}")
        return f"PDF processing error: {str(e)}"

def convert_file_to_json(file_path: str, file_type: str) -> str:
    logger.debug(f"Converting file {file_path} (type: {file_type})")
    try:
        h = file_hash(file_path)
        cache_path = os.path.join(file_cache_dir, f"{h}.json")
        if os.path.exists(cache_path):
            logger.debug(f"Using cached JSON for {file_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)
        logger.debug(f"Cached JSON for {file_path}")
        return result
    except Exception as e:
        logger.error(f"Error processing {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(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.warning(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):
        logger.debug(f"Copying tool file from {default_tool_path} to {target_tool_path}")
        shutil.copy(default_tool_path, target_tool_path)

    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 Ready")
        return agent
    except Exception as e:
        logger.error(f"Failed to initialize agent: {e}", exc_info=True)
        raise

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):
            logger.debug(f"Analyze called with message: {message[:100]}, history length: {len(history)}, files: {len(files)}")
            
            # Initialize history if empty
            if not history:
                history = []
            
            # Append user message
            history.append({"role": "user", "content": message})
            history.append({"role": "assistant", "content": "⏳ Analyzing records for potential oversights..."})
            yield history, None
            logger.debug("Yielded initial history with analyzing message")

            extracted = ""
            file_hash_value = ""
            if files:
                logger.debug(f"Processing {len(files)} 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) if files else ""
                    logger.debug(f"Extracted file content: {extracted[:100]}")
                except Exception as e:
                    logger.error(f"File processing failed: {e}")
                    history.append({"role": "assistant", "content": f"❌ File processing error: {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.debug(f"Constructed prompt: {prompt[:100]}")

            try:
                # Remove the temporary "Analyzing..." message
                if history and history[-1]["content"].startswith("⏳"):
                    history.pop()
                    logger.debug("Removed analyzing message")

                # Process agent response
                for chunk in agent.run_gradio_chat(
                    message=prompt,
                    history=history,
                    temperature=0.2,
                    max_new_tokens=2048,
                    max_token=4096,
                    call_agent=False,
                    conversation=[],
                ):
                    logger.debug(f"Received chunk: {chunk}")
                    if chunk is None:
                        logger.warning("Chunk is None, skipping")
                        continue

                    # Handle chunk as a list of ChatMessage objects
                    if isinstance(chunk, list):
                        for m in chunk:
                            if hasattr(m, 'content') and m.content:
                                history.append({"role": m.role, "content": sanitize_utf8(m.content)})
                                logger.debug(f"Appended message: {m.content[:50]}")
                                yield history, None
                    # Handle chunk as a string
                    elif isinstance(chunk, str) and chunk.strip():
                        if history and history[-1]["role"] == "assistant":
                            history[-1]["content"] += "\n" + sanitize_utf8(chunk)
                        else:
                            history.append({"role": "assistant", "content": sanitize_utf8(chunk)})
                        logger.debug(f"Updated history with string chunk: {chunk[:50]}")
                        yield history, None
                    else:
                        logger.warning(f"Unexpected chunk type: {type(chunk)}")

                # Provide report path if available
                report_path = os.path.join(report_dir, f"{file_hash_value}_report.txt") if file_hash_value else None
                logger.debug(f"Report path: {report_path}")
                yield history, report_path if report_path and os.path.exists(report_path) else None

            except Exception as e:
                logger.error(f"Error in analyze: {e}", exc_info=True)
                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__":
    logger.info("🚀 Launching app...")
    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,
            debug=True  # Enable debug mode for better error reporting
        )
    except Exception as e:
        logger.error(f"Failed to launch app: {e}", exc_info=True)
        raise