import os
import re
import time
import threading
import torch
import pandas as pd
import gradio as gr
from difflib import SequenceMatcher
from peft import PeftModel
from huggingface_hub import snapshot_download
from transformers import AutoModelForCausalLM, AutoTokenizer

# --- 1. Configuration ---
BASE_MODEL_ID = "unsloth/Meta-Llama-3.1-8B-Instruct"
MAX_OPTIONS = 8

# Retained models after the literature-agent assessment:
# Base, DPO-only, DA-DPO, and TuluCore.
ADAPTER_REPO_ID = "starfriend/WaterScopeAI-Adapters"
ADAPTER_SUBFOLDERS = {
    "da_it": "DA-IT",
    "dpo": "DPO",
    "da_dpo": "DA-DPO",
    "tulucore": "DA/Tulucore",
}

MODEL_DISPLAY_NAMES = {
    "base": "Base",
    "da_it": "DA-IT",
    "dpo": "DPO-only",
    "da_dpo": "DA-DPO",
    "tulucore": "TuluCore",
}

MCQA_MODEL_ORDER = ["da_it", "da_dpo"]
CHAT_MODEL_ORDER = ["base", "dpo", "da_dpo", "tulucore"]
AGENT_MODEL_ORDER = ["base", "dpo", "da_dpo", "tulucore"]

DEFAULT_CHAT_MODEL = "da_dpo"
DEFAULT_AGENT_MODEL = "base"

DATA_PATH = os.path.join("Testing MCQA data", "Decarbonization_MCQA.csv")

# --- 2. Load dataset ---
try:
    MCQA_DF = pd.read_csv(DATA_PATH, encoding="utf-8")
except UnicodeDecodeError:
    MCQA_DF = pd.read_csv(DATA_PATH, encoding="latin1")

# Ensure only Question + A-D columns
MCQA_DF = MCQA_DF[["Question", "A", "B", "C", "D"]]

# --- 3. Lazy Loading for Models ---
_model = None
_tokenizer = None

# Generation is serialized because PEFT adapters are model-global state.
_generation_lock = threading.Lock()


def download_required_adapters():
    """Download only the four required PEFT adapters from the Hub."""
    hf_token = os.getenv("HF_TOKEN")

    allow_patterns = []
    for subfolder in ADAPTER_SUBFOLDERS.values():
        allow_patterns.extend(
            [
                f"{subfolder}/adapter_config.json",
                f"{subfolder}/adapter_model.safetensors",
            ]
        )

    local_repo_path = snapshot_download(
        repo_id=ADAPTER_REPO_ID,
        repo_type="model",
        revision="main",
        token=hf_token,
        allow_patterns=allow_patterns,
    )

    adapter_paths = {}
    for adapter_name, subfolder in ADAPTER_SUBFOLDERS.items():
        adapter_path = os.path.join(local_repo_path, *subfolder.split("/"))
        config_path = os.path.join(adapter_path, "adapter_config.json")
        weights_path = os.path.join(adapter_path, "adapter_model.safetensors")

        if not os.path.isfile(config_path):
            raise FileNotFoundError(
                f"{adapter_name} adapter_config.json was not found at "
                f"{config_path}. Check the repository structure and HF_TOKEN."
            )
        if not os.path.isfile(weights_path):
            raise FileNotFoundError(
                f"{adapter_name} adapter weights were not found at "
                f"{weights_path}."
            )

        adapter_paths[adapter_name] = adapter_path

    return adapter_paths


def load_model_and_tokenizer():
    """Load the base model and the three retained adapters."""
    global _model, _tokenizer

    if _model is not None and _tokenizer is not None:
        return _model, _tokenizer

    print("Initializing WaterScope-AI models...")

    if not torch.cuda.is_available():
        raise RuntimeError("No CUDA GPU detected. This Space requires a GPU.")

    _tokenizer = AutoTokenizer.from_pretrained(
        BASE_MODEL_ID,
        use_fast=True,
    )

    if _tokenizer.pad_token_id is None:
        _tokenizer.pad_token = _tokenizer.eos_token

    base_model = AutoModelForCausalLM.from_pretrained(
        BASE_MODEL_ID,
        torch_dtype=torch.bfloat16,
        device_map="auto",
        low_cpu_mem_usage=True,
    )
    base_model.eval()
    print("Base model loaded.")

    adapter_paths = download_required_adapters()

    first_adapter = "da_it"
    _model = PeftModel.from_pretrained(
        base_model,
        adapter_paths[first_adapter],
        adapter_name=first_adapter,
        is_trainable=False,
    )
    print(f"DA-IT adapter loaded from: {adapter_paths[first_adapter]}")

    for adapter_name in ("dpo", "da_dpo", "tulucore"):
        _model.load_adapter(
            adapter_paths[adapter_name],
            adapter_name=adapter_name,
            is_trainable=False,
        )
        print(
            f"{MODEL_DISPLAY_NAMES[adapter_name]} adapter loaded from: "
            f"{adapter_paths[adapter_name]}"
        )

    _model.set_adapter(DEFAULT_CHAT_MODEL)
    _model.eval()

    print("Available adapters:", list(_model.peft_config.keys()))
    print("MCQA models:", [MODEL_DISPLAY_NAMES[name] for name in MCQA_MODEL_ORDER])
    print("Chat models:", [MODEL_DISPLAY_NAMES[name] for name in CHAT_MODEL_ORDER])
    print("Agent models:", [MODEL_DISPLAY_NAMES[name] for name in AGENT_MODEL_ORDER])
    return _model, _tokenizer


def activate_model(model_name):
    """Activate a retained adapter or temporarily disable adapters for Base."""
    all_models = ["base"] + list(ADAPTER_SUBFOLDERS.keys())
    if model_name not in all_models:
        raise ValueError(
            f"Unknown model '{model_name}'. Choose from: {', '.join(all_models)}"
        )

    if model_name == "base":
        return _model.disable_adapter()

    _model.set_adapter(model_name)
    return None


# --- 4. Utility Functions ---
def extract_letter(raw_answer: str) -> str:
    """Extract predicted option letter from model output"""
    # Priority 1: Look for explicit phrases like "answer is B"
    match = re.search(r"(?:answer|option) is\s+([A-H])", raw_answer, re.IGNORECASE)
    if match:
        return match.group(1).upper()

    # Priority 2: Look for formats like "B." or "B)" at the start
    match = re.search(r"^\s*([A-H])[\.\):]", raw_answer)
    if match:
        return match.group(1).upper()

    # Priority 3: Look for the first standalone letter in the text
    match = re.search(r"\b([A-H])\b", raw_answer)
    if match:
        return match.group(1).upper()

    return "N/A"


def clean_repetitions(text: str) -> str:
    lines = [l.strip() for l in text.strip().splitlines() if l.strip()]
    if not lines:
        return ""

    # split into words (keep punctuation as part of word)
    def tokenize(line):
        return re.findall(r"\S+", line)

    result = tokenize(lines[0])

    for line in lines[1:]:
        tokens = tokenize(line)

        # find overlap
        i = 0
        while i < len(result) and i < len(tokens) and result[i].rstrip(".,!?") == tokens[i].rstrip(".,!?"):
            i += 1

        # append only the non-overlapping part
        result.extend(tokens[i:])

    return " ".join(result)
    

# Global variable to track cancellation
cancellation_requested = False

def run_mcqa_comparison(
    question,
    opt_a,
    opt_b,
    opt_c,
    opt_d,
    opt_e,
    opt_f,
    opt_g,
    opt_h,
    generate_explanation,
):
    """Run the original MCQA comparison with DA-IT and DA-DPO."""
    global _model, _tokenizer, cancellation_requested

    cancellation_requested = False

    if _model is None or _tokenizer is None:
        gr.Info("Initializing models for the first time...")
        load_model_and_tokenizer()

    options = [opt_a, opt_b, opt_c, opt_d, opt_e, opt_f, opt_g, opt_h]
    active_options = [opt for opt in options if opt and opt.strip()]

    if not question or len(active_options) < 2:
        yield (
            "Error",
            "Please enter a question and at least two options.",
            "Error",
            "Please enter a question and at least two options.",
        )
        return

    option_labels = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
    option_text = "\n".join(
        f"{option_labels[i]}. {value}"
        for i, value in enumerate(active_options)
    )

    if generate_explanation:
        instruction = (
            "Provide the letter first, followed by a concise expert explanation "
            "in the form: 'The answer is [LETTER]. Because ...'"
        )
        max_tokens = 220
    else:
        instruction = "Return only the letter of the best answer."
        max_tokens = 30

    messages = [
        {
            "role": "system",
            "content": (
                "You are an expert in water and wastewater treatment, "
                "decarbonization, emissions, resource recovery, and "
                "environmental sustainability. Answer the multiple-choice "
                f"question accurately. {instruction}"
            ),
        },
        {
            "role": "user",
            "content": f"Question: {question}\nCandidate options:\n{option_text}",
        },
    ]

    chat_input = _tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True,
    )
    inputs = safe_tokenize(
        chat_input,
        _tokenizer,
        _model,
        max_new_tokens=max_tokens,
    )

    def generate_for(model_name):
        if cancellation_requested:
            raise gr.Error("Processing cancelled by user")

        generation_kwargs = dict(
            **inputs,
            max_new_tokens=max_tokens,
            eos_token_id=_tokenizer.eos_token_id,
            pad_token_id=_tokenizer.pad_token_id,
            do_sample=False,
            use_cache=True,
        )

        if model_name == "da_dpo":
            generation_kwargs.update(
                repetition_penalty=1.10,
                no_repeat_ngram_size=4,
            )

        with torch.inference_mode():
            _model.set_adapter(model_name)
            outputs = _model.generate(**generation_kwargs)

        generated_ids = outputs[0][inputs["input_ids"].shape[1]:]
        response = _tokenizer.decode(
            generated_ids,
            skip_special_tokens=True,
            clean_up_tokenization_spaces=False,
        ).strip()
        return clean_repetitions(response)

    try:
        with _generation_lock:
            yield "", "Running DA-IT...", "", ""

            da_it_raw = generate_for("da_it")
            da_it_letter = extract_letter(da_it_raw)

            yield da_it_letter, da_it_raw, "", "Running DA-DPO..."

            da_dpo_raw = generate_for("da_dpo")
            da_dpo_letter = extract_letter(da_dpo_raw)

        yield da_it_letter, da_it_raw, da_dpo_letter, da_dpo_raw

    except gr.Error as exc:
        if "cancelled" in str(exc).lower():
            gr.Info("Processing cancelled by user")
            return
        raise


# Function to handle cancellation
def cancel_processing():
    global cancellation_requested
    cancellation_requested = True
    return "Cancellation requested"


# Safe tokenization wrapper
def safe_tokenize(chat_input, _tokenizer, _model, max_new_tokens=1600):
    # 1. Validate input type
    if not isinstance(chat_input, str) or len(chat_input.strip()) == 0:
        raise ValueError("chat_input must be a non-empty string")

    # 2. Sanitize weird characters (e.g., emojis, zero-width spaces)
    clean_input = re.sub(r"[^\x00-\x7F]+", " ", chat_input)

    # 3. Tokenize with truncation to avoid position limit issues
    max_input_tokens = (
        _model.config.max_position_embeddings
        - max_new_tokens
        - 100
    )

    tokens = _tokenizer(
        clean_input,
        return_tensors="pt",
        truncation=True,
        max_length=max_input_tokens
    )

    # 4. Validate token IDs
    vocab_size = _model.get_input_embeddings().weight.shape[0]
    max_id = tokens["input_ids"].max().item()
    min_id = tokens["input_ids"].min().item()

    print(f"[DEBUG] chat_input: {repr(chat_input)}")
    print(f"[DEBUG] sanitized_input: {repr(clean_input)}")
    print(f"[DEBUG] token IDs min: {min_id}, max: {max_id}, vocab size: {vocab_size}")

    if max_id >= vocab_size or min_id < 0:
        raise ValueError(f"Token IDs out of range: min {min_id}, max {max_id}, vocab size {vocab_size}")

    # 5. Move tokens to model device
    tokens = {k: v.to(_model.device) for k, v in tokens.items() if isinstance(v, torch.Tensor)}
    return tokens

    
# General chat function
def chat_with_model(
    message,
    history,
    selected_model=DEFAULT_CHAT_MODEL,
    max_new_tokens=900,
):
    """General conversational QA while preserving multi-turn history."""
    global _model, _tokenizer

    if _model is None or _tokenizer is None:
        gr.Info("Initializing models for the first time...")
        load_model_and_tokenizer()

    if not isinstance(message, str) or not message.strip():
        return "Please provide a non-empty message."

    selected_model = selected_model or DEFAULT_CHAT_MODEL
    if selected_model not in CHAT_MODEL_ORDER:
        raise gr.Error(
            "Chat supports Base, DPO-only, DA-DPO, and TuluCore."
        )
    max_new_tokens = max(64, min(int(max_new_tokens), 1600))

    system_prompt = (
        """
        You are an expert AI assistant in water and wastewater engineering.
        When answering a question:
        - First, write down all relevant facts or values.
        - Next, identify which one is correct based on those facts for factual
          comparisons or multiple options.
        - Next, provide a clear description for conceptual definition questions.
        - Finally, clearly state your conclusion in this format:
          [Main answer]. [one or two sentences explaining the reasoning].
          [appropriate values, equations to support the reasoning].
        """
    )

    messages = [{"role": "system", "content": system_prompt}]
    messages.extend(normalize_chat_history(history))
    messages.append({"role": "user", "content": message.strip()})

    return generate_chat_response(
        messages=messages,
        selected_model=selected_model,
        max_new_tokens=max_new_tokens,
    )


AGENT_APPLICATIONS = {
    "general_qa": {
        "label": "General environmental QA",
        "instruction": (
            "Answer the technical question directly. Distinguish facts, "
            "assumptions, and recommendations."
        ),
    },
    "document_qa": {
        "label": "Document-grounded QA",
        "instruction": (
            "Use the supplied context as the only source for document-specific "
            "claims. State clearly when the context does not support an answer."
        ),
    },
    "paper_synthesis": {
        "label": "Multi-paper synthesis",
        "instruction": (
            "Compare the supplied papers with explicit source attribution. "
            "Preserve key methods and quantitative findings and do not mix "
            "evidence among papers."
        ),
    },
    "method_recommendation": {
        "label": "Method recommendation",
        "instruction": (
            "Recommend a method using explicit evidence, implementation "
            "constraints, limitations, and uncertainty. Do not claim superiority "
            "unless the supplied evidence directly supports it."
        ),
    },
    "research_gaps": {
        "label": "Research-gap identification",
        "instruction": (
            "Identify evidence-grounded research gaps. Link every gap to a "
            "specific limitation or unresolved issue in the supplied context."
        ),
    },
    "claim_check": {
        "label": "Unsupported-claim check",
        "instruction": (
            "Evaluate whether the requested claim is supported. Refuse to invent "
            "a paper, method, value, causal relationship, or percentage."
        ),
    },
}


def normalize_chat_history(history):
    """Convert Gradio messages or legacy tuples into chat-template messages."""
    normalized = []

    for item in history or []:
        if isinstance(item, dict):
            role = item.get("role")
            content = item.get("content")

            if isinstance(content, list):
                parts = []
                for part in content:
                    if isinstance(part, dict) and part.get("type") == "text":
                        if part.get("text"):
                            parts.append(str(part["text"]))
                    elif isinstance(part, str):
                        parts.append(part)
                content = "\n".join(parts)

            if role in {"user", "assistant"} and content:
                normalized.append({"role": role, "content": str(content)})

        elif isinstance(item, (list, tuple)) and len(item) >= 2:
            user_message, assistant_message = item[0], item[1]
            if user_message:
                normalized.append(
                    {"role": "user", "content": str(user_message)}
                )
            if assistant_message:
                normalized.append(
                    {"role": "assistant", "content": str(assistant_message)}
                )

    return normalized


def generate_chat_response(messages, selected_model, max_new_tokens):
    """Shared generation path for Chat and Agent."""
    chat_input = _tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True,
    )

    inputs = safe_tokenize(
        chat_input,
        _tokenizer,
        _model,
        max_new_tokens=max_new_tokens,
    )

    generation_kwargs = dict(
        **inputs,
        max_new_tokens=max_new_tokens,
        do_sample=False,
        eos_token_id=_tokenizer.eos_token_id,
        pad_token_id=_tokenizer.pad_token_id,
        use_cache=True,
    )

    # The benchmark showed that DA-DPO and TuluCore benefit from modest
    # repetition control for longer, open-ended generations.
    if selected_model in {"da_dpo", "tulucore"}:
        generation_kwargs.update(
            repetition_penalty=1.10,
            no_repeat_ngram_size=4,
        )

    with _generation_lock:
        try:
            if selected_model == "base":
                with _model.disable_adapter():
                    outputs = _model.generate(**generation_kwargs)
            else:
                _model.set_adapter(selected_model)
                outputs = _model.generate(**generation_kwargs)
        except Exception as exc:
            print(
                f"[ERROR] Generation failed for {selected_model}: {exc}",
                flush=True,
            )
            raise gr.Error(
                f"{MODEL_DISPLAY_NAMES.get(selected_model, selected_model)} "
                f"could not generate a response: {exc}"
            )

    input_length = inputs["input_ids"].shape[1]
    generated_ids = outputs[0][input_length:]
    return _tokenizer.decode(
        generated_ids,
        skip_special_tokens=True,
        clean_up_tokenization_spaces=False,
    ).strip()


def agent_run(
    selected_model,
    application,
    context,
    question,
    max_new_tokens=900,
):
    """Run a selectable WaterScope agent application."""
    global _model, _tokenizer

    if _model is None or _tokenizer is None:
        gr.Info("Initializing models for the first time...")
        load_model_and_tokenizer()

    if selected_model not in AGENT_MODEL_ORDER:
        raise gr.Error(
            "Agent applications support Base, DPO-only, DA-DPO, and TuluCore."
        )
    if application not in AGENT_APPLICATIONS:
        raise gr.Error("Select a valid agent application.")
    if not isinstance(question, str) or not question.strip():
        raise gr.Error("Enter a question or task.")

    max_new_tokens = max(64, min(int(max_new_tokens), 1600))
    application_config = AGENT_APPLICATIONS[application]

    system_prompt = f"""
You are WaterScope-AI, an expert scientific agent for water, wastewater,
environmental engineering, and sustainability.

Application: {application_config["label"]}
Task behavior: {application_config["instruction"]}

Core requirements:
- Answer the user's actual task directly.
- Preserve important quantitative values and units.
- Attribute document-specific evidence clearly.
- Separate evidence from inference and recommendation.
- Never invent papers, citations, numerical values, standards, or findings.
- When evidence is insufficient, say exactly what cannot be concluded.
- Avoid repeated sentences or sections and stop when complete.
""".strip()

    context = (context or "").strip()
    user_content = question.strip()
    if context:
        user_content = (
            "SUPPLIED CONTEXT\n"
            "================\n"
            f"{context}\n\n"
            "USER TASK\n"
            "=========\n"
            f"{question.strip()}"
        )

    messages = [
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": user_content},
    ]

    started = time.time()
    response = generate_chat_response(
        messages=messages,
        selected_model=selected_model,
        max_new_tokens=max_new_tokens,
    )
    elapsed = time.time() - started

    diagnostics = (
        f"Model: {MODEL_DISPLAY_NAMES[selected_model]}\n"
        f"Application: {application_config['label']}\n"
        f"Context characters: {len(context):,}\n"
        f"Response characters: {len(response):,}\n"
        f"Generation time: {elapsed:.1f} s"
    )
    return response, diagnostics


# Backward-compatible API endpoint used by prior local scripts.
def agent_chat_with_model(
    message,
    chat_history=None,
    max_new_tokens=600,
    selected_model=DEFAULT_AGENT_MODEL,
):
    messages = [
        {
            "role": "system",
            "content": (
                "You are WaterScope-AI, an expert assistant in water and "
                "wastewater engineering. Answer accurately, remain grounded, "
                "and do not invent evidence."
            ),
        }
    ]
    messages.extend(normalize_chat_history(chat_history))
    messages.append({"role": "user", "content": str(message)})

    return generate_chat_response(
        messages=messages,
        selected_model=selected_model,
        max_new_tokens=max(64, min(int(max_new_tokens), 1200)),
    )


# Custom CSS for website-like appearance with lighter blue header
custom_css = """
.gradio-container {
    max-width: 1200px !important;
    margin: 0 auto !important;
    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif !important;
}
.header {
    text-align: center;
    padding: 20px;
    background: linear-gradient(135deg, #6eb1ff 0%, #88d3fe 100%);
    color: white;
    border-radius: 8px;
    margin-bottom: 20px;
}
.header h1 {
    margin: 0;
    font-size: 2.5em;
    font-weight: 600;
}
.header p {
    margin: 10px 0 0;
    font-size: 1.2em;
    opacity: 0.9;
}
.section {
    background: white;
    padding: 20px;
    border-radius: 8px;
    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
    margin-bottom: 20px;
}
.nav-bar {
    margin-bottom: 20px;
    display: flex;
    justify-content: center;
    gap: 10px;
}
.footer {
    text-align: center;
    padding: 15px;
    margin-top: 30px;
    color: #666;
    font-size: 0.9em;
    border-top: 1px solid #eee;
}
.dataframe-container {
    margin-top: 20px;
}
.model-output {
    background: #f8f9fa;
    padding: 15px;
    border-radius: 8px;
    border-left: 4px solid #6eb1ff;
}
.model-output h4 {
    margin-top: 0;
    color: #6eb1ff;
}
.option-controls {
    margin-top: 15px;
    display: flex;
    gap: 10px;
}
.cancel-btn {
    background: #f39c12 !important;
    color: white !important;
}
.cancel-btn:hover {
    background: #e67e22 !important;
}
.status-message {
    padding: 10px;
    border-radius: 4px;
    margin: 10px 0;
}
.status-info {
    background-color: #e3f2fd;
    border-left: 4px solid #2196f3;
}
.status-warning {
    background-color: #fff3e0;
    border-left: 4px solid #ff9800;
}
.status-error {
    background-color: #ffebee;
    border-left: 4px solid #f44336;
}
.status-success {
    background-color: #e8f5e9;
    border-left: 4px solid #4caf50;
}
/* Chat specific styles */
.chat-container {
    display: flex;
    flex-direction: column;
    height: 500px;
}
.chat-messages {
    flex: 1;
    overflow-y: auto;
    padding: 15px;
    background: var(--light);
    border-radius: 6px;
    margin-bottom: 15px;
    display: flex;
    flex-direction: column;
    gap: 15px;
}
.message {
    display: flex;
    max-width: 80%;
}
.user-message {
    align-self: flex-end;
}
.bot-message {
    align-self: flex-start;
}
.message-content {
    padding: 12px 16px;
    border-radius: 18px;
    line-height: 1.4;
}
.user-message .message-content {
    background: var(--accent);
    color: white;
    border-bottom-right-radius: 4px;
}
.bot-message .message-content {
    background: var(--light-gray);
    color: var(--dark);
    border-bottom-left-radius: 4px;
}
.chat-input-container {
    display: flex;
    gap: 10px;
}
.chat-input-container textarea {
    flex: 1;
    padding: 12px;
    border: 1px solid var(--border);
    border-radius: 6px;
    resize: vertical;
    font-family: inherit;
    font-size: 14px;
}
"""

# --- 5. Gradio UI ---
with gr.Blocks(
    title="WaterScope-AI",
    fill_width=True,
) as demo:
    
    # Custom Header with lighter blue
    with gr.Column(elem_classes="header"):
        gr.Markdown("WaterScope-AI")
        gr.Markdown("Domain-Specific Language Models and Agent Applications for Water Sustainability")
    
    # Navigation Bar
    with gr.Row(elem_classes="nav-bar"):
        gr.Button("Home", variant="secondary", size="sm")
        gr.Button("About", variant="secondary", size="sm")
        gr.Button("Documentation", variant="secondary", size="sm")
        gr.Button("Contact", variant="secondary", size="sm")
    
    # Create tabs for different functionalities
    with gr.Tabs():
        # MCQA Demo Tab
        with gr.TabItem("MCQA Demo"):
            # Status message area
            status_message = gr.HTML("", elem_classes="status-message")
            
            # Main content in a styled section
            with gr.Column(elem_classes="section"):
                # State for tracking number of visible options
                num_options_state = gr.State(4)
                
                # Top row with input and output panels
                with gr.Row():
                    # Left panel with inputs
                    with gr.Column(scale=1):
                        with gr.Group():
                            question_box = gr.Textbox(label="Question", lines=2, interactive=True)

                            gr.Markdown("#### Options")
                            
                            # Create option boxes using a list (like in the working version)
                            option_boxes = []
                            for i in range(MAX_OPTIONS):
                                option_boxes.append(gr.Textbox(
                                    label=f"Option {chr(ord('A') + i)}", 
                                    visible=(i < 4), 
                                    interactive=True
                                ))

                        with gr.Row():
                            add_option_btn = gr.Button("Add Option")
                            clear_btn = gr.Button("Clear")
                            explanation_checkbox = gr.Checkbox(label="Generate Explanation", value=False)

                        with gr.Row():
                            run_btn = gr.Button("Run Comparison", variant="primary")
                            cancel_btn = gr.Button("Cancel", variant="stop", visible=False, elem_classes="cancel-btn")

                    # Right panel with outputs
                    with gr.Column(scale=1):
                        gr.Markdown("### Model Outputs")
                        with gr.Row():
                            with gr.Column(elem_classes="model-output"):
                                gr.Markdown("#### DA-IT Model")
                                da_it_letter_box = gr.Textbox(
                                    label="Predicted Letter",
                                    interactive=False,
                                )
                                da_it_raw_box = gr.Textbox(
                                    label="Raw Answer",
                                    lines=3,
                                    interactive=False,
                                )
                            with gr.Column(elem_classes="model-output"):
                                gr.Markdown("#### DA-DPO Model")
                                da_dpo_letter_box = gr.Textbox(
                                    label="Predicted Letter",
                                    interactive=False,
                                )
                                da_dpo_raw_box = gr.Textbox(
                                    label="Raw Answer",
                                    lines=3,
                                    interactive=False,
                                )
            
            # Table section with custom styling
            with gr.Column(elem_classes="section dataframe-container"):
                gr.Markdown("### Browse 777 MCQAs (click a row to autofill)")
                mcqa_table = gr.Dataframe(
                    value=MCQA_DF.values.tolist(),
                    headers=["Question", "A", "B", "C", "D"],
                    datatype=["str"]*5,
                    interactive=False,
                    wrap=True,
                    max_height=400
                )
        
        # Chat Tab
        with gr.TabItem("Chat"):
            with gr.Column(elem_classes="section"):
                gr.Markdown(
                    "### General Chat\n"
                    "Use any retained model for conversational water and "
                    "environmental engineering questions."
                )

                with gr.Row():
                    chat_model = gr.Dropdown(
                        choices=[
                            (MODEL_DISPLAY_NAMES[name], name)
                            for name in CHAT_MODEL_ORDER
                        ],
                        value=DEFAULT_CHAT_MODEL,
                        label="Chat model",
                    )
                    chat_max_tokens = gr.Slider(
                        minimum=128,
                        maximum=1600,
                        value=900,
                        step=64,
                        label="Maximum new tokens",
                    )

                with gr.Row():
                    system_status = gr.Textbox(
                        value="Ready",
                        label="System status",
                        interactive=False,
                    )
                    api_status = gr.Textbox(
                        value="Ready",
                        label="Generation status",
                        interactive=False,
                    )

                chatbot = gr.Chatbot(
                    label="Conversation",
                    elem_classes="chat-messages",
                    height=430,
                )

                with gr.Row():
                    msg = gr.Textbox(
                        label="Your message",
                        placeholder="Ask a water or environmental engineering question...",
                        lines=3,
                        scale=5,
                    )
                    send_btn = gr.Button(
                        "Send",
                        variant="primary",
                        scale=1,
                    )

                clear_chat = gr.Button("Clear conversation")

        # Agent Applications Tab
        with gr.TabItem("Agent Applications"):
            with gr.Column(elem_classes="section"):
                gr.Markdown(
                    "### Test WaterScope-AI across agent applications\n"
                    "Select a model and application, provide optional source "
                    "material, and submit a task."
                )

                with gr.Row():
                    agent_model = gr.Dropdown(
                        choices=[
                            (MODEL_DISPLAY_NAMES[name], name)
                            for name in AGENT_MODEL_ORDER
                        ],
                        value=DEFAULT_AGENT_MODEL,
                        label="Agent model",
                    )
                    agent_application = gr.Dropdown(
                        choices=[
                            (config["label"], key)
                            for key, config in AGENT_APPLICATIONS.items()
                        ],
                        value="document_qa",
                        label="Application",
                    )
                    agent_max_tokens = gr.Slider(
                        minimum=128,
                        maximum=1600,
                        value=900,
                        step=64,
                        label="Maximum new tokens",
                    )

                agent_context = gr.Textbox(
                    label="Optional context or documents",
                    placeholder=(
                        "Paste one or more paper summaries, regulatory passages, "
                        "process data, or other source material here."
                    ),
                    lines=14,
                )
                agent_question = gr.Textbox(
                    label="Question or task",
                    placeholder=(
                        "Example: Compare the treatment methods and recommend "
                        "the most defensible option based only on the context."
                    ),
                    lines=4,
                )

                with gr.Row():
                    agent_run_button = gr.Button(
                        "Run Agent",
                        variant="primary",
                    )
                    agent_clear_button = gr.Button("Clear")

                agent_output = gr.Textbox(
                    label="Agent response",
                    lines=18,
                    interactive=False,
                )
                agent_diagnostics = gr.Textbox(
                    label="Run diagnostics",
                    lines=5,
                    interactive=False,
                )

    # Backward-compatible hidden endpoint: /agent_chat
    agent_api_message = gr.Textbox(visible=False)
    agent_api_history = gr.JSON(value=[], visible=False)
    agent_api_max_tokens = gr.Number(value=600, precision=0, visible=False)
    agent_api_model = gr.Dropdown(
        choices=AGENT_MODEL_ORDER,
        value=DEFAULT_AGENT_MODEL,
        visible=False,
    )
    agent_api_output = gr.Textbox(visible=False)
    agent_api_trigger = gr.Button("Agent Chat API", visible=False)

    agent_api_trigger.click(
        fn=agent_chat_with_model,
        inputs=[
            agent_api_message,
            agent_api_history,
            agent_api_max_tokens,
            agent_api_model,
        ],
        outputs=agent_api_output,
        api_name="agent_chat",
    )

    # Structured agent endpoint: /agent_run
    agent_run_button.click(
        fn=agent_run,
        inputs=[
            agent_model,
            agent_application,
            agent_context,
            agent_question,
            agent_max_tokens,
        ],
        outputs=[agent_output, agent_diagnostics],
        api_name="agent_run",
    )

    agent_clear_button.click(
        fn=lambda: ("", "", "", ""),
        inputs=None,
        outputs=[
            agent_context,
            agent_question,
            agent_output,
            agent_diagnostics,
        ],
        queue=False,
    )

    # Footer
    with gr.Column(elem_classes="footer"):
        gr.Markdown("© 2025 WaterScope-AI | Built with Gradio")
    
    # Function to add more options
    def add_option(current_count):
        if current_count < MAX_OPTIONS:
            current_count += 1
        updates = [gr.update(visible=i < current_count) for i in range(MAX_OPTIONS)]
        return current_count, *updates

    # Function to clear all inputs and outputs (from working version)
    def clear_all():
        """Clear all MCQA inputs, outputs, and option visibility states."""
        option_visibility_updates = [
            gr.update(visible=(i < 4), value="")
            for i in range(MAX_OPTIONS)
        ]

        return (
            4,                         # Reset the visible-option count
            "",                        # Clear the question
            *[""] * MAX_OPTIONS,       # Clear option values
            False,                     # Uncheck explanation
            "", "", "", "",            # Clear DA-IT and DA-DPO outputs
            *option_visibility_updates,
        )
    
    # Fixed function to load row data
    def load_row(evt: gr.SelectData):
        """Load a selected row from the dataframe into the input fields"""
        if evt.index[0] >= len(MCQA_DF):
            return ["", ""] + [""] * MAX_OPTIONS
        
        row = MCQA_DF.iloc[evt.index[0]]
        # Return question and first 4 options (A-D), and empty for the rest
        return_values = [
            row["Question"] if pd.notna(row["Question"]) else "",
            row["A"] if pd.notna(row["A"]) else "",
            row["B"] if pd.notna(row["B"]) else "",
            row["C"] if pd.notna(row["C"]) else "",
            row["D"] if pd.notna(row["D"]) else ""
        ]
        # Add empty values for any additional options
        return_values += [""] * (MAX_OPTIONS - 4)
        return return_values

    # Function to toggle cancel button visibility
    def toggle_cancel_button():
        return gr.update(visible=True)

    # Function to hide cancel button
    def hide_cancel_button():
        return gr.update(visible=False)

    # Function to update status message
    def update_status(message, type="info"):
        if type == "info":
            cls = "status-info"
        elif type == "warning":
            cls = "status-warning"
        elif type == "error":
            cls = "status-error"
        elif type == "success":
            cls = "status-success"
        else:
            cls = "status-info"
            
        return f'<div class="status-message {cls}">{message}</div>'

    # Connect the table selection event
    mcqa_table.select(
        fn=load_row,
        inputs=None,
        outputs=[question_box, *option_boxes]
    )

    # Connect the add option button
    add_option_btn.click(
        fn=add_option,
        inputs=[num_options_state],
        outputs=[num_options_state, *option_boxes]
    )
    
    # Define the MCQA components cleared by the Clear button.
    outputs_to_clear = [
        num_options_state,
        question_box,
        *option_boxes,
        explanation_checkbox,
        da_it_letter_box,
        da_it_raw_box,
        da_dpo_letter_box,
        da_dpo_raw_box,
        *option_boxes,
    ]
    
    # Connect the clear button (from working version)
    clear_btn.click(
        fn=clear_all, 
        inputs=None, 
        outputs=outputs_to_clear, 
        queue=False
    ).then(
        fn=lambda: update_status("Form cleared", "info"),
        inputs=None,
        outputs=[status_message],
        queue=False
    )

    # Connect the run button
    run_event = run_btn.click(
        fn=lambda: update_status("Initializing processing...", "info"),
        inputs=None,
        outputs=[status_message],
        queue=False
    ).then(
        fn=toggle_cancel_button,
        inputs=None,
        outputs=[cancel_btn],
        queue=False
    ).then(
        fn=run_mcqa_comparison,
        inputs=[question_box, *option_boxes, explanation_checkbox],
        outputs=[
            da_it_letter_box,
            da_it_raw_box,
            da_dpo_letter_box,
            da_dpo_raw_box,
        ]
    ).then(
        fn=lambda: update_status("Processing completed successfully", "success"),
        inputs=None,
        outputs=[status_message],
        queue=False
    ).then(
        fn=hide_cancel_button,
        inputs=None,
        outputs=[cancel_btn],
        queue=False
    )

    # Connect the cancel button
    cancel_btn.click(
        fn=cancel_processing,
        inputs=None,
        outputs=None,
        queue=False
    ).then(
        fn=lambda: update_status("Processing cancelled by user", "warning"),
        inputs=None,
        outputs=[status_message],
        queue=False
    ).then(
        fn=hide_cancel_button,
        inputs=None,
        outputs=[cancel_btn],
        queue=False
    )

    # Chat functionality
    def respond(
        message,
        chat_history,
        selected_model,
        max_new_tokens,
    ):
        chat_history = list(chat_history or [])

        if not isinstance(message, str) or not message.strip():
            return "", chat_history, "Ready", "No message submitted"

        message = message.strip()

        try:
            bot_message = chat_with_model(
                message=message,
                history=chat_history,
                selected_model=selected_model,
                max_new_tokens=max_new_tokens,
            )
            chat_history.extend(
                [
                    {"role": "user", "content": message},
                    {"role": "assistant", "content": bot_message},
                ]
            )
            return "", chat_history, "Ready", (
                f"Response generated with "
                f"{MODEL_DISPLAY_NAMES[selected_model]}"
            )
        except Exception as exc:
            error_message = f"Sorry, I encountered an error: {exc}"
            chat_history.extend(
                [
                    {"role": "user", "content": message},
                    {"role": "assistant", "content": error_message},
                ]
            )
            return "", chat_history, "Error", str(exc)


    # Connect the chat send button
    chat_inputs = [msg, chatbot, chat_model, chat_max_tokens]
    chat_outputs = [msg, chatbot, system_status, api_status]

    msg.submit(
        respond,
        chat_inputs,
        chat_outputs,
        api_name="respond",
    )
    send_btn.click(
        respond,
        chat_inputs,
        chat_outputs,
        api_name="respond_button",
    )
    
    # Connect the clear chat button
    def clear_chat_func():
        system_status.value = "Ready"
        api_status.value = "Ready"
        return []
    
    clear_chat.click(clear_chat_func, None, chatbot, queue=False)

# Pre-load the model when the app starts
print("Pre-loading models...")
load_model_and_tokenizer()
print("Models loaded successfully!")

demo.queue(default_concurrency_limit=1).launch(
    debug=True,
    show_error=True,
    theme=gr.themes.Glass(primary_hue="blue"),
    css=custom_css,
)