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
import torch.distributions as dists # Added import

# --- START: Copied Helper functions from generation_utils.py ---
# These are needed because we are reimplementing the sampling loop locally.

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
    # Shift the indices to the right to keep the first token above the threshold
    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))  # Safety check
    # Remove all tokens with a probability less than the last token of the top-k
    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:
        logits = logits / temperature
    if top_p is not None and top_p < 1.0: # Apply top_p if valid
        logits = top_p_logits(logits, top_p)
    if top_k is not None and top_k > 0:    # Apply top_k if valid
        logits = top_k_logits(logits, top_k)

    # Ensure logits are not all -inf after filtering, if so, sample uniformly? Or handle error.
    # For simplicity, assume valid logits after filtering. If not, sampling might fail.
    # Add a small epsilon to prevent log(0) or issues with all -inf logits
    logits = torch.where(logits == torch.finfo(logits.dtype).min, torch.full_like(logits, -1e9), logits)


    probs = torch.softmax(logits, dim=-1)

    if temperature > 0:
        try:
            # Check for NaNs or Infs in probs before sampling
            if torch.isnan(probs).any() or torch.isinf(probs).any():
                 print("Warning: NaN or Inf detected in probabilities before sampling. Attempting to recover.")
                 # Simple recovery: Sample from uniform distribution or highest prob token
                 probs = torch.nan_to_num(probs, nan=0.0, posinf=0.0, neginf=0.0)
                 if probs.sum() == 0: # If all probabilities became zero
                     print("Warning: All probabilities became zero. Sampling uniformly.")
                     probs = torch.ones_like(probs) / probs.shape[-1]
                 else:
                     probs = probs / probs.sum(dim=-1, keepdim=True) # Re-normalize

            x0 = dists.Categorical(probs=probs).sample()
            confidence = torch.gather(probs, -1, x0.unsqueeze(-1)).squeeze(-1)
        except Exception as e: # Catch broader exceptions during sampling
            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)
        # Ensure there are at least 2 probabilities to compare
        top1_probs = sorted_probs[..., 0]
        top2_probs = sorted_probs[..., 1] if sorted_probs.shape[-1] > 1 else top1_probs # Handle case with only 1 possible token
        confidence = top1_probs - top2_probs

    if neg_entropy:
        epsilon = 1e-10
        log_probs = torch.log(probs + epsilon)
        confidence = torch.sum(probs * log_probs, dim=-1) # Should be negative entropy

    return confidence, x0

# --- END: Copied Helper functions ---


# Load model configuration to get special token IDs
config = AutoConfig.from_pretrained("Dream-org/Dream-v0-Instruct-7B", trust_remote_code=True)
# Use AutoModel for the base model loading, relying on trust_remote_code=True
# for the custom DreamModel class and generation mixin.
model_path = "Dream-org/Dream-v0-Instruct-7B"

# Determine device
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print(f"Using device: {device}")

# Load model and tokenizer
print("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
print("Loading model...")
# Ensure torch_dtype is set appropriately for your hardware if needed
model = AutoModel.from_pretrained(
    model_path,
    torch_dtype=torch.bfloat16 if device == 'cuda' else torch.float32, # Use bfloat16 only on CUDA
    trust_remote_code=True,
    # attn_implementation="flash_attention_2" # Optional: Speed up if FA2 is available
)
model = model.to(device).eval()
print("Model loaded.")

# Constants from Dream's config/tokenizer
MASK_TOKEN = tokenizer.mask_token
MASK_ID = tokenizer.mask_token_id # Use tokenizer's mask_token_id directly
PAD_ID = tokenizer.pad_token_id # Use tokenizer's pad_token_id
EOS_ID = tokenizer.eos_token_id # Use tokenizer's eos_token_id
# Use attributes from loaded config/tokenizer objects
# MASK_ID = config.mask_token_id # Can use this too, should be consistent
# PAD_ID = config.pad_token_id
# EOS_ID = config.eos_token_id

# Ensure mask_token_id is correctly identified
if MASK_ID is None:
    print("Warning: Mask token ID not found in config/tokenizer. Trying to fetch from tokenizer...")
    # Try getting from tokenizer directly if config doesn't have it or it's None
    mask_token_special = tokenizer.mask_token
    if mask_token_special:
        MASK_ID = tokenizer.convert_tokens_to_ids(mask_token_special)
        print(f"Found MASK_ID from tokenizer: {MASK_ID}")
    else:
        # Fallback or raise error if still not found
        raise ValueError("Cannot determine MASK_ID. Check model's tokenizer configuration.")

# Make sure EOS_ID and PAD_ID are handled correctly; Dream uses the same ID for both
SPECIAL_TOKEN_IDS = {PAD_ID, EOS_ID, MASK_ID}
# Add other special tokens defined in tokenizer_config.json if needed for hiding
# Get IDs for im_start, im_end etc. if they should also be hidden/handled specially
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]]:
    """
    Parse constraints in format: 'position:word, position:word, ...'
    Returns a dictionary mapping the starting position (0-indexed from the start
    of the *generated* sequence) to a list of token IDs for the constraint word.
    """
    constraints = {}
    if not constraints_text:
        return constraints

    parts = constraints_text.split(',')
    for part in parts:
        part = part.strip() # Remove leading/trailing whitespace from the part itself
        if ':' not in part:
            continue
        pos_str, word = part.split(':', 1)
        try:
            # Position relative to the start of the *generation*
            pos = int(pos_str.strip())
            word = word.strip() # Strip whitespace from word
            # Tokenize the word - Dream tokenizer handles spaces well typically.
            # Let's check if the word starts with a space implicitly or needs one.
            # Standard tokenizers often need a space prefix if not at the start.
            # Test: tokenizer.encode(" world") vs tokenizer.encode("world")
            # Dream often encodes ' world' differently from 'world'.
            # Assume we want the word as it would appear mid-sentence unless pos is 0.
            token_ids = tokenizer.encode(word, add_special_tokens=False)
            # Add space prefix if needed based on position? This is tricky.
            # Let's assume the user provides the word how they want it tokenized,
            # potentially including a leading space if necessary.
            # Example: " 5: word" might be tokenized differently than "5:word".
            # Simplest approach: Tokenize exactly what the user provided.
            # Let's refine: add space prefix automatically if pos > 0, unless word already starts with space?
            # This seems more robust for typical usage.
            if pos > 0 and not word.startswith(" "):
                 token_ids_with_space = tokenizer.encode(" " + word, add_special_tokens=False)
                 # Check if adding space actually changes tokenization significantly
                 # Heuristic: if the first token ID changes, use the space-prefixed version.
                 first_token_no_space = tokenizer.encode(word, add_special_tokens=False)[0] if token_ids else None
                 first_token_with_space = tokenizer.encode(" " + word, add_special_tokens=False)[0] if token_ids_with_space else None

                 if first_token_no_space != first_token_with_space:
                     token_ids = token_ids_with_space
                 # If tokenization doesn't change much, maybe stick to original? Less surprising.
                 # Let's stick to adding the space if pos > 0 for consistency, like original code.
                 token_ids = tokenizer.encode(" " + word, add_special_tokens=False)

            elif pos == 0:
                 token_ids = tokenizer.encode(word, add_special_tokens=False)


            if token_ids and pos >= 0:
                constraints[pos] = token_ids
            elif not token_ids:
                print(f"Warning: Could not tokenize constraint word '{word}'")
        except ValueError:
            print(f"Warning: Invalid position '{pos_str}' in constraint part '{part}'")
            continue # Ignore malformed constraint parts
        except Exception as e:
            print(f"Warning: Error processing constraint '{part}': {e}")
            continue

    print(f"Parsed constraints: {constraints}") # Debugging
    return constraints


def format_chat_history(history: List[List[Optional[str]]]) -> List[Dict[str, str]]:
    """
    Format chat history for the Dream model's chat template.

    Args:
        history: List of [user_message, assistant_message] pairs.
                 The last assistant_message might be None.

    Returns:
        Formatted list of message dictionaries for tokenizer.apply_chat_template.
    """
    messages = []
    for user_msg, assistant_msg in history:
        if user_msg: # Defensive check
             messages.append({"role": "user", "content": user_msg})
        # Add assistant message only if it exists (it won't for the last turn before generation)
        if assistant_msg:
            messages.append({"role": "assistant", "content": assistant_msg})
    return messages

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 # For logging/debugging
) -> torch.Tensor:
    """Applies constraints directly to the state tensor `x`."""
    modified_x = x.clone() # Work on a copy to avoid modifying original if needed elsewhere
    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)

        # Ensure the constraint fits within the generation length
        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)
                # Force the constraint tokens onto the sequence
                modified_x[0, abs_start_pos:abs_end_pos] = constraint_tensor
                # print(f"Debug (Step {current_step}): Applied constraint {tokenizer.decode(word_token_ids)} at pos {rel_pos}") # Debug
            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 # Decorator for Hugging Face Spaces GPU usage
@torch.no_grad() # Ensure no gradients are computed during generation
def generate_dream_response(
    history: List[List[Optional[str]]],
    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 using the Dream model step-by-step and yields visualization states live.

    Args:
        history: Chat history.
        gen_length: Max new tokens to generate.
        steps: Number of diffusion steps.
        constraints_text: User-provided constraints string.
        temperature: Sampling temperature.
        top_p: Top-p sampling nucleus. Clamp to < 1.0 or None.
        top_k: Top-k sampling. Clamp to > 0 or None.
        alg: Remasking algorithm ('origin', 'maskgit_plus', 'topk_margin', 'entropy').
        alg_temp: Temperature for confidence-based algorithms.
        visualization_delay: Delay between visualization steps.

    Yields:
        Tuple[List[List[Optional[str]]], List[Tuple[str, Optional[str]]], str]:
            - Updated history (may be intermediate until final response)
            - Visualization data for HighlightedText for the current step
            - Intermediate or Final response text (yielded repeatedly)
    """

    if not history or not history[-1][0]:
        yield history, [("No input message found.", "red")], ""
        return

    # --- 1. Preparation ---
    last_user_message = history[-1][0]
    messages_for_template = format_chat_history(history) # Includes the latest user message

    # Parse constraints relative to the *generated* sequence
    parsed_constraints = parse_constraints(constraints_text) # Dict[rel_pos, List[token_id]]

    # Prepare inputs using the chat template
    try:
        inputs = tokenizer.apply_chat_template(
            messages_for_template,
            return_tensors="pt",
            return_dict=True,
            add_generation_prompt=True # Important for instruct models
        )
        input_ids = inputs.input_ids.to(device)
        prompt_attention_mask = inputs.attention_mask.to(device) # Mask for the prompt part
        prompt_length = input_ids.shape[1]
    except Exception as e:
        print(f"Error applying chat template: {e}")
        yield history, [("Error preparing input.", "red")], ""
        return

    # --- Config parameters for the loop ---
    eps = 1e-3 # Default from DreamGenerationConfig, make configurable if needed
    # Ensure top_p and top_k have valid values for filtering functions
    top_p_val = top_p if top_p is not None and 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'] else None

    # --- 2. Initialize Generation State ---
    total_length = prompt_length + gen_length

    # Initial state: prompt + MASK tokens
    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)

    # Prepare full attention mask (assuming full attention over generated part initially)
    generation_attention_mask = torch.ones((1, gen_length), dtype=torch.long, device=device)
    full_attention_mask = torch.cat((prompt_attention_mask, generation_attention_mask), dim=1)

    # Check if model needs specific attention mask format (e.g., causal for prompt?)
    # The original `diffusion_generate` handles this internally. Replicating requires care.
    # Based on `_sample`, it prepares a broadcastable mask if padding exists, else uses "full".
    # Let's assume "full" attention is okay for Dream's purpose here, as mask tokens don't depend on future masks.
    # If the base model *requires* causal masking internally even with diffusion, this might need adjustment.
    # For simplicity, using a full mask (ones) over the whole sequence.
    # The model's internal attention should handle causality if needed.
    # Let's stick to the simpler full mask preparation from the original code when no padding.
    if torch.any(full_attention_mask == 0): # Handle padding if present (shouldn't be with template?)
         tok_idx = full_attention_mask.long().cumsum(-1) - 1
         tok_idx.masked_fill_(full_attention_mask == 0, 0) # Use 0 for padding index? Or 1? Check original. Original used 1.
         tok_idx.masked_fill_(full_attention_mask == 0, 1)
         attention_mask_for_model = torch.logical_and(
             full_attention_mask.unsqueeze(1).unsqueeze(-2),
             full_attention_mask.unsqueeze(1).unsqueeze(-1),
         ) # Shape [B, 1, N, N]
    else:
         tok_idx = None
         attention_mask_for_model = None # Let the model handle full attention if mask is None

    # Timesteps for diffusion
    timesteps = torch.linspace(1, eps, steps + 1, device=device)

    # Apply initial constraints (before first step)
    x = apply_constraints_to_state(x, prompt_length, total_length, parsed_constraints, current_step=-1) # Step -1 for initial

    # --- 3. Visualization Setup ---
    previous_tokens_vis = None # Keep track of the previous step's state for coloring
    final_response_text = "" # Store the final decoded text
    history_copy = [list(item) for item in history] # Make a mutable copy

    # --- 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" # Dark Gray for masks
        vis_data_initial.append((display_token, color))

    previous_tokens_vis = initial_generated_tokens
    yield history_copy, vis_data_initial, "" # Yield initial state
    time.sleep(visualization_delay)

    # --- 5. Step-by-Step Diffusion Loop ---
    try:
        start_time = time.time()
        for i in range(steps):
            # --- Model Forward Pass ---
            mask_index = (x == MASK_ID) # Find masks in the *current* state x
            if not mask_index.any(): # Stop if no masks left
                 print(f"No mask tokens left at step {i}. Stopping early.")
                 break

            # print(f"Step {i}: Input shape {x.shape}, Mask sum {mask_index.sum()}") # Debug
            # print(f"Step {i}: Input tokens (first/last 10): {x[0, :10].tolist()} ... {x[0, -10:].tolist()}") # Debug

            # Call the model - ensure attention mask format is correct
            # The model forward expects `attention_mask` usually of shape [B, N] or broadcastable.
            # If we use `attention_mask_for_model = None`, it implies full attention.
            # If we computed `attention_mask_for_model` as [B, 1, N, N], pass that.
            # Let's try passing the [B, N] mask and let the model handle broadcasting/causality internally.
            outputs = model(
                input_ids=x,
                attention_mask=full_attention_mask, # Pass the [B, N] mask
                position_ids=None, # Let model compute default positions
                use_cache=False, # No cache needed for diffusion steps
                return_dict=True
            )
            logits = outputs.logits

            # Shift logits like in original code? Check `generation_utils.py`.
            # Yes, `logits = torch.cat([logits[:,:1], logits[:, :-1]], dim=1)`
            # This seems to align logits with the *previous* token's prediction. Is this correct for diffusion?
            # Let's assume the original code did this for a reason, perhaps related to how the model was trained or expects inputs.
            # Update: Looking at standard LM forward pass, logits[t] predicts token[t+1].
            # The shift aligns logits[t] with token[t]. Let's keep it.
            logits = torch.cat([logits[:,:1], logits[:, :-1]], dim=1)


            # Select logits for masked positions
            # Ensure mask_index has the same batch dimension size as logits
            # mask_index shape is [B, N], logits shape is [B, N, V]
            # We need to select elements from the last dim of logits where mask is True
            mask_logits = logits[mask_index] # This correctly selects [num_masked_tokens, V]

            if mask_logits.numel() == 0: # If no masks, logits selection is empty
                 print(f"No masked tokens found for logit selection at step {i}. Stopping.")
                 break

            # print(f"Step {i}: mask_logits shape: {mask_logits.shape}") # Debug

            # --- Sampling / Remasking Logic ---
            t = timesteps[i]
            s = timesteps[i + 1]

            x_new_masked_part = torch.full_like(x[mask_index], MASK_ID, device=device, dtype=torch.long)

            if alg == 'origin':
                # Original diffusion logic
                p_transfer = (1.0 - s / t) if i < steps - 1 else 1.0 # Ensure float division
                # Sample only for the tokens to be revealed in this step
                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:
                    # print(f"Step {i} (origin): Sampling {logits_to_sample.shape[0]} tokens.") # Debug
                    _, sampled_tokens = sample_tokens(logits_to_sample, temperature=temperature, top_p=top_p_val, top_k=top_k_val)
                    # Place sampled tokens into the correct positions within the masked part
                    x_new_masked_part[transfer_indices_relative] = sampled_tokens
                # else:
                    # print(f"Step {i} (origin): No tokens to sample (p_transfer={p_transfer}).") # Debug

            else:
                # Confidence-based algorithms (maskgit_plus, topk_margin, entropy)
                use_margin = (alg == 'topk_margin')
                use_entropy = (alg == 'entropy')
                # print(f"Step {i} ({alg}): Sampling all {mask_logits.shape[0]} masked tokens for confidence.") # Debug
                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
                )
                # print(f"Step {i} ({alg}): Confidence range: [{confidence.min():.2f}, {confidence.max():.2f}]") # Debug


                num_mask_token = mask_logits.shape[0]
                # Calculate number to reveal based on time steps, ensure it's an int
                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:
                    # print(f"Step {i} ({alg}): Need to reveal {number_transfer_tokens} tokens.") # Debug
                    if alg_temp_val is None or alg_temp_val <= 0: # Use top-k confidence
                         # Sort by confidence (use negative entropy directly if alg='entropy')
                         # For entropy, lower (more negative) is higher confidence (less uncertainty)
                        sort_metric = confidence if alg != 'entropy' else -confidence
                        _, transfer_indices_relative = torch.topk(sort_metric, k=min(number_transfer_tokens, num_mask_token)) # Ensure k is not > num_mask_token
                    else: # Use sampling based on confidence temperature
                        conf_probs = confidence / alg_temp_val
                        # Check for inf/-inf before softmax
                        conf_probs = torch.nan_to_num(conf_probs, nan=0.0, posinf=1e9, neginf=-1e9)
                        conf_probs = F.softmax(conf_probs, dim=-1)
                        # Check probs sum to 1
                        if not torch.allclose(conf_probs.sum(), torch.tensor(1.0, device=device), atol=1e-4):
                             print(f"Warning step {i}: Confidence probabilities do not sum to 1 after softmax ({conf_probs.sum()}). Re-normalizing.")
                             conf_probs = conf_probs / conf_probs.sum(dim=-1, keepdim=True) # Normalize

                        # Ensure num_samples is valid
                        num_samples = min(number_transfer_tokens, num_mask_token)
                        if conf_probs.numel() > 0 and num_samples > 0:
                            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.")
                                # Fallback to top-k if multinomial fails (e.g., due to prob issues)
                                sort_metric = confidence if alg != 'entropy' else -confidence
                                _, transfer_indices_relative = torch.topk(sort_metric, k=num_samples)
                        else:
                            transfer_indices_relative = torch.tensor([], dtype=torch.long, device=device) # No indices if no probs or num_samples=0

                    # Place the selected candidate tokens into the masked part update
                    if transfer_indices_relative.numel() > 0:
                        x_new_masked_part[transfer_indices_relative] = x0_candidates[transfer_indices_relative].clone()
                    # else:
                        # print(f"Step {i} ({alg}): No tokens revealed via confidence ({number_transfer_tokens} target).") # Debug

            # Update the global state `x` only at the masked positions
            x[mask_index] = x_new_masked_part

            # --- Apply Constraints ---
            # Constraints should be applied *after* sampling/revealing tokens for the step
            x = apply_constraints_to_state(x, prompt_length, total_length, parsed_constraints, current_step=i)

            # --- Yield Visualization ---
            current_generated_tokens = x[0, prompt_length:].cpu() # Get generated part, move to CPU
            vis_data = []
            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 else MASK_ID

                try:
                    decoded_token = tokenizer.decode([current_tok_id], skip_special_tokens=False)
                    display_token = MASK_TOKEN if current_tok_id == MASK_ID else decoded_token
                except Exception:
                    display_token = f"[ID:{current_tok_id}]" # Fallback

                color = None
                token_to_display = display_token

                if current_tok_id == MASK_ID:
                    color = "#444444" # Dark Gray for masks
                elif previous_tok_id == MASK_ID: # Token was just revealed
                    color = "#66CC66" # Light Green
                else: # Token was already revealed
                    color = "#6699CC" # Light Blue

                # Hide special tokens (PAD/EOS) if they were already revealed (LLaDA effect)
                # Ensure PAD_ID and EOS_ID are not None before checking
                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 = "" # Hide by making empty
                    color = None # No color for hidden


                if token_to_display:
                    vis_data.append((token_to_display, color))
                elif len(vis_data) > 0 and isinstance(vis_data[-1], tuple) and vis_data[-1][0] == " ":
                    # Avoid adding multiple spaces if tokens are hidden consecutively
                    pass
                elif len(vis_data) > 0 and not isinstance(vis_data[-1], tuple) and vis_data[-1] == " ":
                     pass # Already added a space
                elif len(vis_data) > 0 :
                     # Add a single space if hiding follows a visible token, improves readability slightly
                     # Let's simplify: just omit hidden tokens. Adding spaces might be complex.
                      pass

            # Update previous state for the next iteration
            previous_tokens_vis = current_generated_tokens

            # Decode intermediate response (might be partial) - skip specials for readability
            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()

            # Yield current state
            # We yield the *current* history, the vis data for this step, and intermediate text
            # The final text will overwrite the intermediate text in the UI eventually
            yield history_copy, 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:]

        # Decode the final response text
        final_response_text = tokenizer.decode(
            response_tokens,
            skip_special_tokens=True, # Skip EOS, PAD, MASK etc. in the final text
            clean_up_tokenization_spaces=True
        ).strip()

        # Update history with the final response *before* the last yield
        history_copy[-1][1] = final_response_text

        # Yield the final state (which might be the same as the last yielded state if loop finished)
        # Need to format vis_data one last time based on the final `x`
        final_generated_tokens = x[0, prompt_length:].cpu()
        vis_data_final = []
        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 else MASK_ID

            try:
                decoded_token = tokenizer.decode([current_tok_id], skip_special_tokens=False)
                display_token = MASK_TOKEN if current_tok_id == MASK_ID else decoded_token
            except Exception:
                display_token = f"[ID:{current_tok_id}]" # Fallback

            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 the final history, final visualization, and final text
        yield history_copy, vis_data_final, final_response_text
        print("Visualization streaming complete.")


    except Exception as e:
        print(f"Error during generation or processing: {e}")
        import traceback
        traceback.print_exc()
        # Update history with error message? Or leave as None? Let's leave as None.
        yield history_copy, [("Error during generation.", "red")], ""
        return


# --- Gradio UI (Remains largely the same, ensures outputs match yield structure) ---
css = '''
.category-legend{display:none}
button{min-height: 60px}
'''
def create_chatbot_demo():
    with gr.Blocks(css=css, theme=gr.themes.Soft()) 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/)]" # Note: Link might be hypothetical
        )

        # STATE MANAGEMENT
        # chat_history = gr.State([]) # Use gr.Chatbot's internal state implicitly if possible, or manage manually
        # Let's manage manually with a list for clarity with yielding updates
        _chat_history_store = gr.State([]) # Hidden state to store actual history list

        # UI COMPONENTS
        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 chatbot UI empty
                )

                # Message input
                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 # Remove container for tighter packing
                        )
                        send_btn = gr.Button("Send", scale=1, variant="primary")


                constraints_input = gr.Textbox(
                    label="Word Constraints (Optional)",
                    info="Place words at specific positions (0-indexed from start of generation). Format: 'pos:word, pos:word,...'. Example: '0:Once, 5:upon, 10:time'",
                    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, # Legend isn't very informative here
                    interactive=False # Not interactive
                )
                # Add a text box to display the final/intermediate response clearly
                response_text_display = gr.Textbox(
                    label="Generated Response",
                    interactive=False,
                    lines=5 # Show a few lines
                )


        # Advanced generation settings
        with gr.Accordion("Generation Settings", open=False):
            with gr.Row():
                gen_length = gr.Slider(
                    minimum=16, maximum=512, value=128, step=8, # Increased max length
                    label="Max New Tokens"
                )
                steps = gr.Slider(
                    minimum=8, maximum=512, value=128, step=8, # Increased max steps
                    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 (Confidence Algs)"
                )

            with gr.Row():
                top_p = gr.Slider(
                    minimum=0.0, maximum=1.0, value=0.95, step=0.05,
                    label="Top-P (<=0 or >=1 disables)" # Clarify disabling condition
                )
                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='entropy', # Default to entropy as in example
                    label="Remasking Strategy (Algorithm)"
                )

            with gr.Row():
                visualization_delay = gr.Slider(
                    minimum=0.0, maximum=0.5, value=0.03, step=0.01, # Slightly faster default
                    label="Visualization Delay (seconds)"
                )

        # Clear button
        clear_btn = gr.Button("Clear Conversation")

        # --- Event Handlers ---

        def add_user_message_to_history(message: str, history_store: List[List[Optional[str]]]):
            """Adds user message, clears input, prepares for bot response."""
            if not message.strip():
                gr.Warning("Please enter a message.")
                # Return unchanged history, empty vis, empty response text
                return history_store, history_store, "", [], ""

            # Add user message with placeholder for bot response
            history_store.append([message, None])
            # Return updated history store, history for chatbot UI, empty input, empty vis, empty response
            return history_store, history_store, "", [], ""

        def clear_conversation():
            """Clears the chat history, visualization, and response text."""
            return [], [], "", [], "" # History store, chatbot UI, input, vis, response text

        # --- Connect UI elements ---

        # Define the inputs for the generation function once
        generation_inputs = [
            _chat_history_store, gen_length, steps, constraints_input,
            temperature, top_p, top_k, remasking_strategy, alg_temp,
            visualization_delay
        ]
        # Define the outputs for the generation function
        # Now yields: history_copy, vis_data, intermediate_response_text
        # Map these to: chatbot_ui, output_vis, response_text_display
        generation_outputs = [chatbot_ui, output_vis, response_text_display]

        # Handle Textbox Submission (Enter key)
        submit_listener = user_input.submit(
            fn=add_user_message_to_history,
            inputs=[user_input, _chat_history_store],
            outputs=[_chat_history_store, chatbot_ui, user_input, output_vis, response_text_display] # Step 1: Add user msg & clear outputs
        )
        # Chain the bot response generation after the user message is added
        submit_listener.then(
            fn=generate_dream_response,
            inputs=generation_inputs,
            outputs=generation_outputs, # Step 2: Generate response and stream vis/text
            show_progress="hidden" # Hide default progress bar as we have live vis
        )

        # Handle Send Button Click
        click_listener = send_btn.click(
            fn=add_user_message_to_history,
            inputs=[user_input, _chat_history_store],
            outputs=[_chat_history_store, chatbot_ui, user_input, output_vis, response_text_display] # Step 1: Add user msg & clear outputs
        )
        # Chain the bot response generation after the user message is added
        click_listener.then(
            fn=generate_dream_response,
            inputs=generation_inputs,
            outputs=generation_outputs, # Step 2: Generate response and stream vis/text
            show_progress="hidden"
        )

        # Clear Button Action
        clear_btn.click(
            clear_conversation,
            inputs=[],
            outputs=[_chat_history_store, chatbot_ui, user_input, output_vis, response_text_display]
        )

    return demo

# --- Launch ---
if __name__ == "__main__":
    demo = create_chatbot_demo()
    # Use queue for handling multiple users and streaming
    demo.queue().launch(debug=True, share=False) # Set share=True for public link