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

# ✅ Fix: Add src to Python path
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "src")))

# ✅ Persist model cache to Hugging Face Space's /data directory
model_cache_dir = "/data/txagent_models"
os.makedirs(model_cache_dir, exist_ok=True)
os.environ["TRANSFORMERS_CACHE"] = model_cache_dir
os.environ["HF_HOME"] = model_cache_dir

from txagent.txagent import TxAgent

def sanitize_utf8(text: str) -> str:
    return text.encode("utf-8", "ignore").decode("utf-8")

def file_hash(path):
    with open(path, "rb") as f:
        return hashlib.md5(f.read()).hexdigest()

def convert_file_to_json(file_path: str, file_type: str) -> str:
    try:
        cache_dir = "/data/cache"
        os.makedirs(cache_dir, exist_ok=True)
        h = file_hash(file_path)
        cache_path = os.path.join(cache_dir, f"{h}.json")

        if os.path.exists(cache_path):
            return open(cache_path, "r", encoding="utf-8").read()

        if file_type == "csv":
            df = pd.read_csv(file_path, encoding_errors="replace", header=None, dtype=str, skip_blank_lines=False, on_bad_lines="skip")
        elif file_type in ["xls", "xlsx"]:
            try:
                df = pd.read_excel(file_path, engine="openpyxl", header=None, dtype=str)
            except:
                df = pd.read_excel(file_path, engine="xlrd", header=None, dtype=str)
        elif file_type == "pdf":
            with pdfplumber.open(file_path) as pdf:
                text = "\n".join([page.extract_text() or "" for page in pdf.pages])
            result = json.dumps({"filename": os.path.basename(file_path), "content": text.strip()})
            open(cache_path, "w", encoding="utf-8").write(result)
            return result
        else:
            return json.dumps({"error": f"Unsupported file type: {file_type}"})

        if df is None or df.empty:
            return json.dumps({"warning": f"No data extracted from: {file_path}"})

        df = df.fillna("")
        content = df.astype(str).values.tolist()
        result = json.dumps({"filename": os.path.basename(file_path), "rows": content})
        open(cache_path, "w", encoding="utf-8").write(result)
        return result
    except Exception as e:
        return json.dumps({"error": f"Error reading {os.path.basename(file_path)}: {str(e)}"})

def create_ui(agent: TxAgent):
    with gr.Blocks(theme=gr.themes.Soft()) as demo:
        gr.Markdown("<h1 style='text-align: center;'>📋 CPS: Clinical Patient Support System</h1>")

        chatbot = gr.Chatbot(label="CPS Assistant", height=600, type="messages")
        file_upload = gr.File(
            label="Upload Medical File",
            file_types=[".pdf", ".txt", ".docx", ".jpg", ".png", ".csv", ".xls", ".xlsx"],
            file_count="multiple"
        )
        message_input = gr.Textbox(placeholder="Ask a biomedical question or just upload the files...", show_label=False)
        send_button = gr.Button("Send", variant="primary")
        conversation_state = gr.State([])

        def handle_chat(message: str, history: list, conversation: list, uploaded_files: list, progress=gr.Progress()):
            try:
                history.append({"role": "user", "content": message})
                history.append({"role": "assistant", "content": "⏳ Processing your request..."})
                yield history

                extracted_text = ""
                if uploaded_files and isinstance(uploaded_files, list):
                    for file in uploaded_files:
                        if not hasattr(file, 'name'):
                            continue
                        path = file.name
                        ext = path.split(".")[-1].lower()
                        json_text = convert_file_to_json(path, ext)
                        extracted_text += sanitize_utf8(json_text) + "\n"

                # Only final chunk will be passed (no split or loop)
                context = (
                    "You are an expert clinical AI assistant. Review this patient's history, medications, and notes, and ONLY provide a final answer summarizing what the doctor might have missed."
                )

                chunked_prompt = f"{context}\n\n--- Patient Record ---\n{extracted_text}\n\n[Final Analysis]"

                generator = agent.run_gradio_chat(
                    message=chunked_prompt,
                    history=[],
                    temperature=0.3,
                    max_new_tokens=1024,
                    max_token=8192,
                    call_agent=False,
                    conversation=conversation,
                    uploaded_files=uploaded_files,
                    max_round=30
                )

                final_response = ""
                for update in generator:
                    if update is None:
                        continue
                    if isinstance(update, str):
                        final_response += update
                    elif isinstance(update, list):
                        for msg in update:
                            if hasattr(msg, 'content'):
                                final_response += msg.content

                history[-1] = {"role": "assistant", "content": final_response.strip() or "❌ No response."}
                yield history

            except Exception as chat_error:
                print(f"Chat handling error: {chat_error}")
                history[-1] = {"role": "assistant", "content": "❌ An error occurred while processing your request."}
                yield history

        inputs = [message_input, chatbot, conversation_state, file_upload]
        send_button.click(fn=handle_chat, inputs=inputs, outputs=chatbot)
        message_input.submit(fn=handle_chat, inputs=inputs, outputs=chatbot)

        gr.Examples([
            ["Upload your medical form and ask what the doctor might've missed."],
            ["This patient was treated with antibiotics for UTI. What else should we check?"],
            ["Is there anything abnormal in the attached blood work report?"]
        ], inputs=message_input)

    return demo