app.py

# dream_app.py
import torch
import numpy as np
import gradio as gr
import spaces # Ensure spaces is installed if needed for GPU decorator
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel, AutoConfig
import time
import re
from typing import List, Dict, Tuple, Optional, Any # Added Any
import torch.distributions as dists # Added import
import traceback # For better error printing
from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS


def _compute_default_rope_parameters(config=None, device=None, seq_len=None, **rope_kwargs):
    """Compatibility shim for Transformers versions that removed the default RoPE entry."""
    if config is not None and rope_kwargs:
        raise ValueError("`config` and `rope_kwargs` are mutually exclusive for default RoPE parameters.")

    if rope_kwargs:
        base = rope_kwargs["base"]
        dim = rope_kwargs["dim"]
    else:
        base = config.rope_theta
        partial_rotary_factor = getattr(config, "partial_rotary_factor", 1.0)
        head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
        dim = int(head_dim * partial_rotary_factor)

    inv_freq = 1.0 / (
        base ** (torch.arange(0, dim, 2, dtype=torch.int64).float().to(device) / dim)
    )
    return inv_freq, 1.0


ROPE_INIT_FUNCTIONS.setdefault("default", _compute_default_rope_parameters)

# --- START: Copied Helper functions from generation_utils.py ---
# [Keep the copied functions: top_p_logits, top_k_logits, sample_tokens]
def top_p_logits(logits, top_p=None):
    """ Applies top-p filtering to logits. """
    if top_p is None or top_p >= 1.0:
        return logits
    sorted_logits, sorted_indices = torch.sort(logits, descending=True)
    cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
    sorted_indices_to_remove = cumulative_probs > top_p
    sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
    sorted_indices_to_remove[..., 0] = 0
    mask = torch.zeros_like(logits, dtype=torch.bool, device=logits.device)
    mask = mask.scatter_(-1, sorted_indices, sorted_indices_to_remove)
    logits = logits.masked_fill(mask, torch.finfo(logits.dtype).min)
    return logits

def top_k_logits(logits, top_k=None):
    """ Applies top-k filtering to logits. """
    if top_k is None or top_k <= 0:
        return logits
    top_k = min(top_k, logits.size(-1))
    indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
    logits = logits.masked_fill(indices_to_remove, torch.finfo(logits.dtype).min)
    return logits

def sample_tokens(logits, temperature=0.0, top_p=None, top_k=None, margin_confidence=False, neg_entropy=False):
    """ Samples tokens based on logits and calculates confidence. """
    if temperature > 0:
        safe_temp = max(temperature, 1e-6)
        logits = logits / safe_temp
    if top_p is not None and 0.0 < top_p < 1.0:
        logits = top_p_logits(logits, top_p)
    if top_k is not None and top_k > 0:
        logits = top_k_logits(logits, top_k)

    is_all_neg_inf = torch.all(logits <= torch.finfo(logits.dtype).min, dim=-1, keepdim=True)
    if torch.any(is_all_neg_inf):
        uniform_logits = torch.zeros_like(logits)
        logits = torch.where(is_all_neg_inf, uniform_logits, logits)

    probs = torch.softmax(logits, dim=-1)
    probs = torch.clamp(probs, min=0.0)
    prob_sum = probs.sum(dim=-1, keepdim=True)
    safe_prob_sum = torch.max(prob_sum, torch.tensor(1e-12, device=probs.device, dtype=probs.dtype))
    probs = probs / safe_prob_sum
    probs = torch.nan_to_num(probs, nan=0.0)

    if temperature > 0:
        try:
            x0 = dists.Categorical(probs=probs).sample()
            confidence = torch.gather(probs, -1, x0.unsqueeze(-1)).squeeze(-1)
        except Exception as e:
            print(f"Warning: Error during Categorical sampling: {e}. Falling back to argmax.")
            confidence, x0 = probs.max(dim=-1)
    else:
        confidence, x0 = probs.max(dim=-1)

    if margin_confidence:
        sorted_probs, _ = torch.sort(probs, dim=-1, descending=True)
        top1_probs = sorted_probs[..., 0]
        top2_probs = sorted_probs[..., 1] if sorted_probs.shape[-1] > 1 else top1_probs
        confidence = top1_probs - top2_probs
    elif neg_entropy: # Use elif to avoid calculating entropy if margin_confidence was True
        epsilon = 1e-10
        log_probs = torch.log(probs + epsilon)
        confidence = torch.sum(probs * log_probs, dim=-1) # Negative entropy
    # Else: confidence is just the probability of the sampled token if temperature > 0, or max prob otherwise

    confidence = torch.nan_to_num(confidence, nan=0.0)
    return confidence, x0
# --- END: Copied Helper functions ---


# --- Model Loading and Constants ---
# Load model configuration to get special token IDs
config = AutoConfig.from_pretrained("Dream-org/Dream-v0-Instruct-7B", trust_remote_code=True)
model_path = "Dream-org/Dream-v0-Instruct-7B"
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print(f"Using device: {device}")

print("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
print("Loading model...")
model = AutoModel.from_pretrained(
    model_path,
    torch_dtype=torch.bfloat16 if device == 'cuda' else torch.float32,
    trust_remote_code=True,
    attn_implementation="sdpa" # Explicitly request SDPA
)
model = model.to(device).eval()
print("Model loaded.")

MASK_TOKEN = tokenizer.mask_token
MASK_ID = tokenizer.mask_token_id
PAD_ID = tokenizer.pad_token_id
EOS_ID = tokenizer.eos_token_id

if MASK_ID is None:
    raise ValueError("Cannot determine MASK_ID. Check model's tokenizer configuration.")

SPECIAL_TOKEN_IDS = {PAD_ID, EOS_ID, MASK_ID}
try:
    IM_START_ID = tokenizer.convert_tokens_to_ids("<|im_start|>")
    IM_END_ID = tokenizer.convert_tokens_to_ids("<|im_end|>")
    SPECIAL_TOKEN_IDS.add(IM_START_ID)
    SPECIAL_TOKEN_IDS.add(IM_END_ID)
except KeyError:
    print("Warning: <|im_start|> or <|im_end|> not found in tokenizer vocab.")
    IM_START_ID = None
    IM_END_ID = None


# --- Helper Functions ---
def parse_constraints(constraints_text: str) -> Dict[int, List[int]]:
    """ Parses word constraints. """
    constraints = {}
    if not constraints_text: return constraints
    parts = constraints_text.split(',')
    for part in parts:
        part = part.strip()
        if ':' not in part: continue
        pos_str, word = part.split(':', 1)
        try:
            pos = int(pos_str.strip())
            word = word.strip()
            token_ids = []
            if word:
                text_to_encode = (" " + word) if (pos > 0 and not word.startswith(" ")) else word
                token_ids = tokenizer.encode(text_to_encode, add_special_tokens=False)
            if token_ids and pos >= 0: constraints[pos] = token_ids
            elif not token_ids and word: print(f"Warning: Could not tokenize constraint word '{word}'")
        except ValueError: print(f"Warning: Invalid position '{pos_str}' in constraint part '{part}'")
        except Exception as e: print(f"Warning: Error processing constraint '{part}': {e}")
    return constraints

# Removed format_chat_history as the state will now be in the correct format

def apply_constraints_to_state(
    x: torch.Tensor,
    prompt_length: int,
    total_length: int,
    parsed_constraints: Dict[int, List[int]],
    current_step: Optional[int] = None
) -> torch.Tensor:
    """ Applies constraints directly to the state tensor `x`. """
    modified_x = x.clone()
    for rel_pos, word_token_ids in parsed_constraints.items():
        abs_start_pos = prompt_length + rel_pos
        abs_end_pos = abs_start_pos + len(word_token_ids)
        if abs_start_pos < total_length and abs_end_pos <= total_length:
            try:
                constraint_tensor = torch.tensor(word_token_ids, dtype=torch.long, device=modified_x.device)
                modified_x[0, abs_start_pos:abs_end_pos] = constraint_tensor
            except IndexError: print(f"Warning (Step {current_step}): Constraint at {rel_pos} ('{tokenizer.decode(word_token_ids)}') goes out of bounds.")
            except Exception as e: print(f"Warning (Step {current_step}): Failed to apply constraint at {rel_pos}: {e}")
    return modified_x


# --- Core Generation Logic with Live Visualization ---

@spaces.GPU
@torch.no_grad()
def generate_dream_response(
    history_dict_list: List[Dict[str, str]], # Now expects list of dicts
    gen_length: int,
    steps: int,
    constraints_text: str,
    temperature: float,
    top_p: Optional[float],
    top_k: Optional[int],
    alg: str,
    alg_temp: Optional[float],
    visualization_delay: float
    ) -> List[Tuple[str, str]]:
    """ Generates text step-by-step and yields visualization states live. """

    if not history_dict_list or history_dict_list[-1]['role'] != 'user':
        # Handle cases where history is empty or doesn't end with user message
        # This check might be redundant if add_user_message handles it, but good for safety.
        yield history_dict_list, [("No user message found.", "red")], ""
        return

    # --- 1. Preparation ---
    parsed_constraints = parse_constraints(constraints_text)

    # Prepare history for the model template (don't include the empty assistant msg yet)
    history_for_template = history_dict_list # Already in list-of-dicts format

    try:
        inputs = tokenizer.apply_chat_template(
            history_for_template, # Pass the list of dicts directly
            return_tensors="pt",
            return_dict=True,
            add_generation_prompt=True # Crucial: Adds the '<|im_start|>assistant\n' turn
        )
        input_ids = inputs.input_ids.to(device)
        prompt_attention_mask = inputs.attention_mask.to(device) if 'attention_mask' in inputs else torch.ones_like(input_ids)
        prompt_length = input_ids.shape[1]
    except Exception as e:
        print(f"Error applying chat template: {e}")
        traceback.print_exc()
        yield history_dict_list, [("Error preparing input.", "red")], ""
        return

    eps = 1e-3
    top_p_val = top_p if top_p is not None and 0.0 < top_p < 1.0 else None
    top_k_val = top_k if top_k is not None and top_k > 0 else None
    alg_temp_val = alg_temp if alg in ['maskgit_plus', 'topk_margin', 'entropy'] and alg_temp is not None and alg_temp > 0 else None

    # --- 2. Initialize Generation State ---
    total_length = prompt_length + gen_length
    initial_generation_part = torch.full((1, gen_length), MASK_ID, dtype=torch.long, device=device)
    x = torch.cat((input_ids, initial_generation_part), dim=1)

    generation_attention_mask = torch.ones((1, gen_length), dtype=torch.long, device=device)
    full_attention_mask_long = torch.cat((prompt_attention_mask, generation_attention_mask), dim=1)

    attention_mask_for_model = full_attention_mask_long.to(model.dtype)
    large_neg_val = torch.finfo(model.dtype).min
    attention_mask_for_model = (1.0 - attention_mask_for_model) * large_neg_val
    attention_mask_for_model = attention_mask_for_model.unsqueeze(1).unsqueeze(2) # [B, 1, 1, N]

    timesteps = torch.linspace(1, eps, steps + 1, device=device)
    x = apply_constraints_to_state(x, prompt_length, total_length, parsed_constraints, current_step=-1)

    # --- 3. Visualization & History Setup ---
    previous_tokens_vis = None
    final_response_text = ""
    # The history_dict_list is the state we update and yield for the chatbot UI
    # Add the empty assistant message placeholder *to the history state* now
    history_dict_list.append({"role": "assistant", "content": ""})

    # --- 4. Initial Yield (Masked State) ---
    initial_generated_tokens = x[0, prompt_length:].cpu()
    vis_data_initial = []
    for tok_id in initial_generated_tokens.tolist():
        display_token = MASK_TOKEN
        color = "#444444"
        vis_data_initial.append((display_token, color))

    previous_tokens_vis = initial_generated_tokens
    # Yield the history (which now includes the empty assistant turn)
    yield history_dict_list, vis_data_initial, ""
    time.sleep(visualization_delay)

    # --- 5. Step-by-Step Diffusion Loop ---
    try:
        start_time = time.time()
        for i in range(steps):
            mask_index = (x == MASK_ID)
            if not mask_index.any():
                 print(f"No mask tokens left at step {i}. Stopping early.")
                 break

            outputs = model(
                input_ids=x,
                attention_mask=attention_mask_for_model,
                position_ids=None, use_cache=False, return_dict=True
            )
            logits = outputs.logits
            logits = torch.cat([logits[:,:1], logits[:, :-1]], dim=1)

            mask_logits = logits[mask_index]
            if mask_logits.numel() == 0:
                 print(f"No masked tokens found for logit selection at step {i}. Stopping.")
                 break

            t = timesteps[i]
            s = timesteps[i + 1]
            x_new_masked_part = torch.full_like(x[mask_index], MASK_ID, device=device, dtype=torch.long)

            # [Keep sampling logic the same - 'origin' and confidence-based]
            if alg == 'origin':
                p_transfer = (1.0 - s / t) if i < steps - 1 else 1.0
                num_masked = mask_logits.shape[0]
                transfer_indices_relative = torch.rand(num_masked, device=device) < p_transfer
                logits_to_sample = mask_logits[transfer_indices_relative]
                if logits_to_sample.numel() > 0:
                    _, sampled_tokens = sample_tokens(logits_to_sample, temperature=temperature, top_p=top_p_val, top_k=top_k_val)
                    if transfer_indices_relative.sum() == sampled_tokens.numel(): # Basic check
                        x_new_masked_part[transfer_indices_relative] = sampled_tokens
                    else: print(f"Warning step {i} (origin): Mismatch transfer indices and sampled tokens.")


            else: # Confidence-based
                use_margin = (alg == 'topk_margin')
                use_entropy = (alg == 'entropy')
                confidence, x0_candidates = sample_tokens(mask_logits, temperature=temperature, top_p=top_p_val, top_k=top_k_val, margin_confidence=use_margin, neg_entropy=use_entropy)

                num_mask_token = mask_logits.shape[0]
                target_num_revealed_float = num_mask_token * (1.0 - s / t)
                number_transfer_tokens = int(target_num_revealed_float) if i < steps - 1 else num_mask_token

                if number_transfer_tokens > 0:
                    num_samples = min(number_transfer_tokens, num_mask_token)
                    if num_samples > 0:
                        transfer_indices_relative = torch.tensor([], dtype=torch.long, device=device) # Init empty
                        if alg_temp_val is None or alg_temp_val <= 0: # Top-k
                            sort_metric = confidence
                            k_topk = min(num_samples, sort_metric.numel())
                            if k_topk > 0: _, transfer_indices_relative = torch.topk(sort_metric, k=k_topk)
                        else: # Sample based on temp
                            if confidence.numel() > 0:
                                conf_probs = confidence / alg_temp_val
                                conf_probs = torch.nan_to_num(conf_probs, nan=0.0, posinf=1e9, neginf=-1e9)
                                conf_probs = torch.clamp(conf_probs - conf_probs.max(), min=-30)
                                conf_probs = F.softmax(conf_probs, dim=-1)
                                conf_probs = torch.clamp(conf_probs, min=0.0)
                                conf_probs = torch.nan_to_num(conf_probs, nan=0.0)
                                prob_sum = conf_probs.sum()
                                target_sum_tensor = torch.tensor(1.0, device=device, dtype=prob_sum.dtype)
                                if not torch.isclose(prob_sum, target_sum_tensor, atol=1e-4) and prob_sum > 0:
                                    safe_prob_sum = torch.max(prob_sum, torch.tensor(1e-12, device=device, dtype=prob_sum.dtype))
                                    conf_probs = conf_probs / safe_prob_sum
                                final_prob_sum_check = conf_probs.sum()
                                if conf_probs.numel() > 0 and num_samples > 0 and torch.all(conf_probs >= 0) and torch.isclose(final_prob_sum_check, target_sum_tensor, atol=1e-4):
                                    try: transfer_indices_relative = torch.multinomial(conf_probs, num_samples=num_samples, replacement=False)
                                    except RuntimeError as e:
                                        print(f"Warning step {i}: Multinomial sampling failed ('{e}'). Falling back to top-k.")
                                        sort_metric = confidence
                                        k_multinomial_fallback = min(num_samples, sort_metric.numel())
                                        if k_multinomial_fallback > 0: _, transfer_indices_relative = torch.topk(sort_metric, k=k_multinomial_fallback)
                                else: # Fallback if probs invalid for multinomial
                                    # print(f"Warning step {i}: Invalid probabilities for multinomial sampling (sum={final_prob_sum_check:.4f}). Falling back to top-k.")
                                    sort_metric = confidence
                                    k_multinomial_fallback = min(num_samples, sort_metric.numel())
                                    if k_multinomial_fallback > 0: _, transfer_indices_relative = torch.topk(sort_metric, k=k_multinomial_fallback)

                        # Apply transfer
                        if transfer_indices_relative.numel() > 0:
                             if x0_candidates.numel() > 0 and transfer_indices_relative.max() < x0_candidates.shape[0]:
                                 if transfer_indices_relative.max() < x_new_masked_part.shape[0]:
                                      x_new_masked_part[transfer_indices_relative] = x0_candidates[transfer_indices_relative].clone()
                                 else: print(f"Warning step {i}: transfer_indices out of bounds for x_new_masked_part.")
                             else: print(f"Warning step {i}: transfer_indices out of bounds for x0_candidates or x0_candidates empty.")


            x[mask_index] = x_new_masked_part
            x = apply_constraints_to_state(x, prompt_length, total_length, parsed_constraints, current_step=i)

            # --- Yield Visualization & Update History ---
            current_generated_tokens = x[0, prompt_length:].cpu()
            vis_data = []
            # [Visualization formatting logic remains the same]
            for j in range(gen_length):
                current_tok_id = current_generated_tokens[j].item()
                previous_tok_id = previous_tokens_vis[j].item() if previous_tokens_vis is not None and j < len(previous_tokens_vis) else MASK_ID
                try:
                    decoded_token = tokenizer.decode([current_tok_id], skip_special_tokens=False, clean_up_tokenization_spaces=False)
                    display_token = MASK_TOKEN if current_tok_id == MASK_ID else decoded_token
                except Exception: display_token = f"[ID:{current_tok_id}]"
                color = None; token_to_display = display_token
                if current_tok_id == MASK_ID: color = "#444444"
                elif previous_tok_id == MASK_ID: color = "#66CC66"
                else: color = "#6699CC"
                should_hide = (PAD_ID is not None and current_tok_id == PAD_ID) or (EOS_ID is not None and current_tok_id == EOS_ID)
                if should_hide and previous_tok_id == current_tok_id: token_to_display = ""; color = None
                if token_to_display: vis_data.append((token_to_display, color))

            previous_tokens_vis = current_generated_tokens

            intermediate_response_tokens = x[0, prompt_length:]
            intermediate_response_text = tokenizer.decode(
                intermediate_response_tokens, skip_special_tokens=True, clean_up_tokenization_spaces=True
            ).strip()

            # --- Update the *last* message in history_dict_list ---
            history_dict_list[-1]['content'] = intermediate_response_text

            # Yield the updated history list (for chatbot UI), vis data, and response text
            yield history_dict_list, vis_data, intermediate_response_text
            time.sleep(visualization_delay)

        end_time = time.time()
        print(f"Dream generation finished in {end_time - start_time:.2f} seconds.")

        # --- 6. Final Processing & Yield ---
        final_sequence = x[0]
        response_tokens = final_sequence[prompt_length:]
        final_response_text = tokenizer.decode(
            response_tokens, skip_special_tokens=True, clean_up_tokenization_spaces=True
        ).strip()

        # Ensure the final text is in the history object before the last yield
        history_dict_list[-1]['content'] = final_response_text

        final_generated_tokens = x[0, prompt_length:].cpu()
        vis_data_final = []
        # [Final visualization formatting logic remains the same]
        for j in range(gen_length):
            current_tok_id = final_generated_tokens[j].item()
            previous_tok_id = previous_tokens_vis[j].item() if previous_tokens_vis is not None and j < len(previous_tokens_vis) else MASK_ID
            try:
                decoded_token = tokenizer.decode([current_tok_id], skip_special_tokens=False, clean_up_tokenization_spaces=False)
                display_token = MASK_TOKEN if current_tok_id == MASK_ID else decoded_token
            except Exception: display_token = f"[ID:{current_tok_id}]"
            color = None; token_to_display = display_token
            if current_tok_id == MASK_ID: color = "#444444"
            elif previous_tok_id == MASK_ID: color = "#66CC66"
            else: color = "#6699CC"
            should_hide = (PAD_ID is not None and current_tok_id == PAD_ID) or (EOS_ID is not None and current_tok_id == EOS_ID)
            if should_hide and previous_tok_id == current_tok_id: token_to_display = ""; color = None
            if token_to_display: vis_data_final.append((token_to_display, color))

        yield history_dict_list, vis_data_final, final_response_text
        print("Visualization streaming complete.")

    except Exception as e:
        print(f"Error during generation or processing: {e}")
        traceback.print_exc()
        # Attempt to add error message to history if possible
        if history_dict_list and history_dict_list[-1]['role'] == 'assistant':
             history_dict_list[-1]['content'] = f"Error: {e}"
        yield history_dict_list, [("Error during generation.", "red")], f"Error: {e}" # Also show error in text box
        return


# --- Gradio UI ---
css = '''
.category-legend{display:none}
'''
def create_chatbot_demo():
    with gr.Blocks(css=css) as demo:
        gr.Markdown("# Dream 7B - Diffusion Language Model Demo")
        gr.Markdown(
            "[[Model Card](https://huggingface.co/Dream-org/Dream-v0-Instruct-7B)] "
            "[[Blog](https://hkunlp.github.io/blog/2025/dream/)]"
        )

        with gr.Row():
            with gr.Column(scale=3):
                chatbot_ui = gr.Chatbot(
                    label="Conversation",
                    height=500,
                    show_copy_button=True,
                    bubble_full_width=False,
                    value=[], # Initialize empty
                    type="messages" # Crucial: Use the messages format
                )
                with gr.Group():
                    with gr.Row():
                        user_input = gr.Textbox(
                            label="Your Message", placeholder="Type your message here...",
                            scale=7, autofocus=True, show_label=False, container=False
                        )
                        send_btn = gr.Button("Send", scale=1, variant="primary")
                constraints_input = gr.Textbox(
                    label="Word Constraints (Optional)",
                    info="Format: 'pos:word, pos:word,...'. Example: '0:Once, 5:upon'",
                    placeholder="0:Hello, 10:world", value=""
                )
            with gr.Column(scale=2):
                output_vis = gr.HighlightedText(
                    label="Denoising Process Visualization", combine_adjacent=False,
                    show_legend=True, interactive=False
                )
                response_text_display = gr.Textbox(
                    label="Current/Final Response", interactive=False, lines=5, visible=False
                )

        # [Keep Accordion with Generation Settings the same]
        with gr.Accordion("Generation Settings", open=False):
             with gr.Row():
                gen_length = gr.Slider(minimum=16, maximum=512, value=128, step=8, label="Max New Tokens")
                steps = gr.Slider(minimum=8, maximum=512, value=128, step=8, label="Diffusion Steps")
             with gr.Row():
                temperature = gr.Slider(minimum=0.0, maximum=1.0, value=0.4, step=0.05, label="Temperature (0 = greedy)")
                alg_temp = gr.Slider(minimum=0.0, maximum=1.0, value=0.1, step=0.05, label="Remasking Temp (Conf Algs)")
             with gr.Row():
                top_p = gr.Slider(minimum=0.0, maximum=1.0, value=0.95, step=0.05, label="Top-P (0 disables)")
                top_k = gr.Slider(minimum=0, maximum=200, value=0, step=5, label="Top-K (0 disables)")
             with gr.Row():
                 remasking_strategy = gr.Radio(choices=['origin', 'maskgit_plus', 'topk_margin', 'entropy'], value='origin', label="Remasking Strategy")
             with gr.Row():
                visualization_delay = gr.Slider(minimum=0.0, maximum=0.5, value=0.0, step=0.01, label="Visualization Delay (s)")

        clear_btn = gr.Button("Clear Conversation")

        # --- Event Handlers ---

        # User function: Appends user message to the history (list of dicts)
        def add_user_message(message: str, history: List[Dict[str, str]]):
            if not message.strip():
                gr.Warning("Please enter a message.")
                return history, "" # Return unchanged history, empty input
            history.append({"role": "user", "content": message})
            # Return updated history for chatbot UI, and clear input box
            return history, ""

        # Bot function (now the generator)
        # Inputs: Chatbot history (list of dicts), generation params
        # Outputs: Chatbot history (updated list of dicts), visualization, response text
        generation_inputs = [
            chatbot_ui, # Pass chatbot state directly (list of dicts)
            gen_length, steps, constraints_input,
            temperature, top_p, top_k, remasking_strategy, alg_temp,
            visualization_delay
        ]
        generation_outputs = [chatbot_ui, output_vis, response_text_display]

        # --- Connect UI elements ---

        # Textbox Submission (Enter key)
        submit_listener = user_input.submit(
            fn=add_user_message,
            inputs=[user_input, chatbot_ui],
            outputs=[chatbot_ui, user_input] # Update chatbot UI and clear input
        ).then(
            fn=generate_dream_response,
            inputs=generation_inputs,
            outputs=generation_outputs,
            show_progress="hidden" # Hide default progress bar
        )

        # Send Button Click
        click_listener = send_btn.click(
            fn=add_user_message,
            inputs=[user_input, chatbot_ui],
            outputs=[chatbot_ui, user_input] # Update chatbot UI and clear input
        ).then(
            fn=generate_dream_response,
            inputs=generation_inputs,
            outputs=generation_outputs,
            show_progress="hidden"
        )

        # Clear Button Action
        clear_btn.click(
            lambda: ([], [], ""), # Function to return empty values
            inputs=[],
            outputs=[chatbot_ui, output_vis, response_text_display], # Clear chatbot, vis, text
            queue=False # No need to queue clearing usually
        )

    return demo

# --- Launch ---
if __name__ == "__main__":
    demo = create_chatbot_demo()
    demo.queue().launch(debug=True, share=False)