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 ---
# [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)
    probs = probs / probs.sum(dim=-1, keepdim=True)
    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
    if neg_entropy:
        epsilon = 1e-10
        log_probs = torch.log(probs + epsilon)
        confidence = torch.sum(probs * log_probs, dim=-1)
    confidence = torch.nan_to_num(confidence, nan=0.0)
    return confidence, x0
# --- END: Copied Helper functions ---


# [Keep model loading, constants]
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"
)
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.")
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: IM_START_ID, IM_END_ID = None, 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

def format_chat_history(history: List[List[Optional[str]]]) -> List[Dict[str, str]]:
    """
    Formats chat history [[user, bot], [user, bot]] into [{'role': 'user', 'content': ...}, ...]
    for the tokenizer's chat template.
    """
    messages = []
    # Ensure history is not empty and is properly structured
    if not history:
        return messages
    for turn in history:
        if not isinstance(turn, (list, tuple)) or len(turn) != 2:
             print(f"Warning: Skipping malformed history turn: {turn}")
             continue
        user_msg, assistant_msg = turn
        if user_msg is not None: # Check if user message exists
             # Ensure content is a string
             user_content = str(user_msg) if user_msg is not None else ""
             messages.append({"role": "user", "content": user_content})
        # Add assistant message only if it exists and is not None
        if assistant_msg is not None:
            assistant_content = str(assistant_msg) if assistant_msg is not None else ""
            messages.append({"role": "assistant", "content": assistant_content})
    # print(f"Formatted messages for template: {messages}") # Debug
    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
) -> torch.Tensor:
    """ Applies constraints 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 OOB: {rel_pos}")
            except Exception as e: print(f"Warning (Step {current_step}): Constraint failed {rel_pos}: {e}")
    return modified_x


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

@spaces.GPU
@torch.no_grad()
def generate_dream_response(
    history: List[List[Optional[str]]], # IMPORTANT: This is the *full* history from the state
    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
    ): # No return type annotation for generators in older Python? Or use -> Iterator[Tuple[...]]
    """ Generates text step-by-step and yields visualization states live. """

    # Ensure history is valid before proceeding
    if not history or not history[-1] or history[-1][0] is None:
        # Yield the current (potentially empty) history back
        yield history, [("No valid input message found.", "red")], ""
        return

    # --- 1. Preparation ---
    # Use the *entire* history received from the state for context
    messages_for_template = format_chat_history(history)
    parsed_constraints = parse_constraints(constraints_text)

    try:
        inputs = tokenizer.apply_chat_template(
            messages_for_template,
            return_tensors="pt",
            return_dict=True,
            add_generation_prompt=True # This adds the assistant prompt 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]
        # print(f"Prompt length for model: {prompt_length}") # Debug
        # print(f"Input IDs to model (first 50): {input_ids[0, :50].tolist()}") # Debug

    except Exception as e:
        print(f"Error applying chat template: {e}")
        # Yield the current history back with an error message
        yield history, [("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)

    # --- Prepare Attention Mask ---
    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) # Shape [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 & State Setup ---
    previous_tokens_vis = None
    # Use the passed-in history directly. We will modify the *last* item's assistant response.
    # No need for history_copy if we are careful. Let's try modifying `history` directly.
    # IMPORTANT: Gradio state needs the component to receive the *entire object* back if it's mutated.
    # So yielding the modified `history` list itself should work.
    history_for_yield = history # Reference the original list

    # --- 4. Initial Yield (Masked State) ---
    initial_generated_tokens = x[0, prompt_length:].cpu()
    vis_data_initial = []
    for tok_id in initial_generated_tokens.tolist():
        vis_data_initial.append((MASK_TOKEN, "#444444"))
    previous_tokens_vis = initial_generated_tokens
    # Yield the *current* history (with None for last bot msg)
    yield history_for_yield, vis_data_initial, ""
    time.sleep(visualization_delay)

    # --- 5. Step-by-Step Diffusion Loop ---
    try:
        start_time = time.time()
        current_response_text = "" # Store intermediate text

        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)

            # [Sampling logic remains the same as previous working version]
            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)
                    x_new_masked_part[transfer_indices_relative] = 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 if alg != 'entropy' else -confidence
                            k_topk = min(num_samples, sort_metric.numel())
                            if k_topk > 0: _, transfer_indices_relative = torch.topk(sort_metric, k=k_topk)
                        else: # Sampled
                            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"W{i}: Multinomial failed ('{e}'). Fallback.") # Fallback handled below
                                if transfer_indices_relative.numel() == 0: # Fallback if sampling failed or wasn't possible
                                    sort_metric = confidence if alg != 'entropy' else -confidence
                                    k_fallback = min(num_samples, sort_metric.numel())
                                    if k_fallback > 0: _, transfer_indices_relative = torch.topk(sort_metric, k=k_fallback)
                        # Apply transfer
                        if transfer_indices_relative.numel() > 0:
                            valid_indices = transfer_indices_relative < x0_candidates.shape[0]
                            valid_transfer_indices = transfer_indices_relative[valid_indices]
                            if valid_transfer_indices.numel() > 0 and valid_transfer_indices.max() < x_new_masked_part.shape[0]:
                                x_new_masked_part[valid_transfer_indices] = x0_candidates[valid_transfer_indices].clone()


            x[mask_index] = x_new_masked_part
            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()
            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

            # --- Update intermediate response text ---
            intermediate_response_tokens = x[0, prompt_length:]
            current_response_text = tokenizer.decode(
                intermediate_response_tokens,
                skip_special_tokens=True,
                clean_up_tokenization_spaces=True
            ).strip()

            # --- Update history for yield ---
            # Update the placeholder in the *last turn* of the history list
            if history_for_yield and history_for_yield[-1]:
                 history_for_yield[-1][1] = current_response_text + "..." # Indicate streaming

            # --- Yield current state ---
            yield history_for_yield, vis_data, current_response_text
            time.sleep(visualization_delay)
            # --- End loop iteration ---

        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()

        # Update the history definitively with the final text
        if history_for_yield and history_for_yield[-1]:
            history_for_yield[-1][1] = final_response_text

        # Format final visualization
        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 the final state
        yield history_for_yield, 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()
        # Ensure the history state reflects the error somehow? Or just yield error vis.
        # Yield the history *as it was* when the error occurred.
        if history_for_yield and history_for_yield[-1]:
             history_for_yield[-1][1] = f"<Error: {e}>" # Put error in bot response
        yield history_for_yield, [("Error during generation.", "red")], ""
        return


# --- Gradio UI ---
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/)]"
        )

        # Use a single state variable for the history list
        chat_history_state = gr.State([])

        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=[] # Initial value set by state binding later
                )
                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, 10:time'",
                    placeholder="0:Hello, 10:world", value=""
                )
            with gr.Column(scale=2):
                output_vis = gr.HighlightedText(
                    label="Denoising Process Visualization", combine_adjacent=True,
                    show_legend=False, interactive=False
                )
                response_text_display = gr.Textbox(
                    label="Generated Response (Live)", interactive=False, lines=5
                )

        with gr.Accordion("Generation Settings", open=False):
             # [Settings sliders remain the same]
             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 (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 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='entropy', label="Remasking Strategy (Algorithm)")
             with gr.Row():
                visualization_delay = gr.Slider(minimum=0.0, maximum=0.5, value=0.03, step=0.01, label="Visualization Delay (seconds)")


        clear_btn = gr.Button("Clear Conversation")

        # --- Event Handler Functions ---

        def add_user_message(message: str, history: List[List[Optional[str]]]):
            """
            Adds the user message to the history state and prepares the UI
            for the bot's response (clearing previous outputs).
            """
            if not message.strip():
                gr.Warning("Please enter a message.")
                # Return unchanged history and empty outputs
                return history, history, "", [], ""
            # Append new turn with user message and None placeholder for bot response
            history.append([message, None])
            # Return updated history (for state), history (for immediate UI update),
            # empty input, empty vis, empty response text.
            return history, history, "", [], ""

        def clear_all():
            """Clears state and all relevant UI components."""
            return [], [], "", [], "" # state, chatbot, input, vis, response text

        # --- Connect UI elements ---

        # Define inputs/outputs for the generator
        generation_inputs = [
            chat_history_state, gen_length, steps, constraints_input,
            temperature, top_p, top_k, remasking_strategy, alg_temp,
            visualization_delay
        ]
        # Generator yields: history_list, vis_data, response_text
        generation_outputs = [chatbot_ui, output_vis, response_text_display]

        # Chain the actions: Submit/Click -> add_user_message -> generate_dream_response

        # 1. User submits message (Enter or Button)
        user_interaction = [user_input, chat_history_state]
        outputs_after_user_add = [
            chat_history_state, # Update the state
            chatbot_ui,         # Update chatbot UI immediately
            user_input,         # Clear user input box
            output_vis,         # Clear visualization
            response_text_display # Clear response text box
        ]

        submit_listener = user_input.submit(
            fn=add_user_message,
            inputs=user_interaction,
            outputs=outputs_after_user_add
        ).then( # 2. Trigger generation AFTER user message is added and UI cleared
            fn=generate_dream_response,
            inputs=generation_inputs, # Pass the updated state and parameters
            outputs=generation_outputs, # Stream updates to chatbot, vis, text
            show_progress="hidden"
        )

        click_listener = send_btn.click(
            fn=add_user_message,
            inputs=user_interaction,
            outputs=outputs_after_user_add
        ).then( # 2. Trigger generation AFTER user message is added and UI cleared
            fn=generate_dream_response,
            inputs=generation_inputs,
            outputs=generation_outputs,
            show_progress="hidden"
        )

        # 3. Clear Button
        clear_btn.click(
            clear_all,
            inputs=[],
            outputs=[
                chat_history_state, chatbot_ui, user_input,
                output_vis, response_text_display
            ]
        )

    return demo


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