app.py

# llada_app.py -> dream_app.py

import torch
import numpy as np
import gradio as gr
import spaces
# import torch.nn.functional as F # Not needed for DREAM's basic visualization
from transformers import AutoTokenizer, AutoModel
import time
import re # Keep for parsing constraints

# Use try-except for space deployment vs local
try:
    # Used for spaces deployment with GPU
    gpu_check = spaces.GPU
    print("Running in Gradio Spaces with GPU environment.")
except AttributeError:
    # Fallback for local execution or environments without spaces.GPU
    print("Running in local environment or without spaces.GPU.")
    # Define a dummy decorator if spaces.GPU is not available
    def gpu_check(func):
        return func

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

# --- Load DREAM Model and Tokenizer ---
model_path = "Dream-org/Dream-v0-Instruct-7B"
print(f"Loading model: {model_path}")
model = AutoModel.from_pretrained(model_path, torch_dtype=torch.bfloat16, trust_remote_code=True).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
print("Model and tokenizer loaded.")

# --- Constants for DREAM ---
# Find the mask token and ID from the DREAM tokenizer
if tokenizer.mask_token is None:
    # Handle cases where a mask token might not be explicitly set
    # You might need to choose a suitable placeholder or investigate further
    # For now, let's try adding one if it's missing and check its id
    # This is speculative and might depend on the specific tokenizer setup
    print("Warning: Mask token not found in tokenizer. Attempting to add.")
    tokenizer.add_special_tokens({'mask_token': '[MASK]'})
    model.resize_token_embeddings(len(tokenizer)) # Important if vocab size changed
    if tokenizer.mask_token is None:
         raise ValueError("Could not set a mask token for the tokenizer.")

MASK_TOKEN = tokenizer.mask_token
MASK_ID = tokenizer.mask_token_id
print(f"Using MASK_TOKEN='{MASK_TOKEN}' with ID={MASK_ID}")
# --- Helper Functions (Constraint Parsing, History Formatting) ---

def parse_constraints(constraints_text):
    """Parse constraints in format: 'position:word, position:word, ...'"""
    constraints = {}
    if not constraints_text:
        return constraints

    parts = constraints_text.split(',')
    for part in parts:
        part = part.strip() # Trim whitespace
        if ':' not in part:
            continue
        try:
            pos_str, word = part.split(':', 1)
            pos = int(pos_str.strip())
            word = word.strip()
            # Allow empty words if needed, but usually we want a word
            if word and pos >= 0:
                constraints[pos] = word
        except ValueError:
            print(f"Warning: Could not parse constraint part: '{part}'")
            continue

    return constraints

def format_chat_history(history):
    """
    Format chat history for the DREAM model (standard messages format)

    Args:
        history: List of [user_message, assistant_message] pairs

    Returns:
        Formatted conversation for the model (list of dictionaries)
    """
    messages = []
    # Add system prompt if desired (check DREAM examples/recommendations)
    # messages.append({"role": "system", "content": "You are a helpful assistant."}) # Optional
    for user_msg, assistant_msg in history:
        if user_msg: # Handle potential None message if clearing failed
             messages.append({"role": "user", "content": user_msg})
        if assistant_msg:  # Skip if None (for the latest user message awaiting response)
            messages.append({"role": "assistant", "content": assistant_msg})

    return messages

# --- Core Generation Logic for DREAM with Visualization ---

@gpu_check # Use the potentially dummy decorator
def dream_generate_response_with_visualization(
    messages,
    gen_length=64,
    steps=64, # Default based on DREAM examples
    constraints=None,
    temperature=0.6, # Default based on DREAM examples
    top_p=0.95, # Default based on DREAM examples
    alg="entropy", # Default based on DREAM examples
    alg_temp=0.0, # Default based on DREAM examples
):
    """
    Generate text with DREAM model with visualization using the generation hook.

    Args:
        messages: List of message dictionaries with 'role' and 'content'
        gen_length: Length of text to generate (max_new_tokens)
        steps: Number of diffusion steps
        constraints: Dictionary mapping positions (relative to response start) to words
        temperature: Sampling temperature
        top_p: Nucleus sampling p
        alg: Remasking algorithm ('origin', 'maskgit_plus', 'topk_margin', 'entropy')
        alg_temp: Temperature for confidence-based algorithms

    Returns:
        Tuple: (List of visualization states, final generated text string)
    """
    print("--- Starting DREAM Generation ---")
    print(f"Parameters: gen_length={gen_length}, steps={steps}, temperature={temperature}, top_p={top_p}, alg='{alg}', alg_temp={alg_temp}")
    print(f"Constraints: {constraints}")

    # --- Input Preparation ---
    if constraints is None:
        constraints = {}

    # Convert word constraints to token IDs (handle multi-token words)
    processed_constraints = {}
    print("Processing constraints:")
    for pos, word in constraints.items():
        # Prepend space for consistent tokenization, similar to LLaDA example
        tokens = tokenizer.encode(" " + word, add_special_tokens=False)
        if not tokens:
            print(f"  Warning: Could not tokenize constraint word '{word}' at position {pos}. Skipping.")
            continue
        print(f"  Pos {pos}, Word '{word}' -> Tokens {tokens}")
        for i, token_id in enumerate(tokens):
            # Ensure we don't overwrite parts of multi-token constraints accidentally
            if pos + i not in processed_constraints:
                 processed_constraints[pos + i] = token_id
            else:
                 print(f"  Warning: Overlapping constraint at position {pos+i}. Keeping first.")

    # Prepare the prompt using chat template
    # Note: DREAM examples use add_generation_prompt=True
    try:
        inputs = tokenizer.apply_chat_template(
            messages,
            return_tensors="pt",
            return_dict=True,
            add_generation_prompt=True # Crucial for instruction-tuned models like Dream-Instruct
        )
        input_ids = inputs.input_ids.to(device=device)
        attention_mask = inputs.attention_mask.to(device=device) # Get attention mask
        prompt_length = input_ids.shape[1]
        print(f"Input prompt length: {prompt_length}")
        print(f"Input IDs: {input_ids}")
    except Exception as e:
        print(f"Error applying chat template: {e}")
        # Provide a fallback or raise the error
        # Fallback: Simple concatenation (less ideal for instruction models)
        # chat_input = "".join([f"{msg['role']}: {msg['content']}\n" for msg in messages]) + "assistant:"
        # input_ids = tokenizer(chat_input, return_tensors="pt").input_ids.to(device)
        # attention_mask = torch.ones_like(input_ids)
        # prompt_length = input_ids.shape[1]
        # print(f"Warning: Using basic concatenation due to template error. Prompt length: {prompt_length}")
        return [([("Error applying chat template.", "red")],)], f"Error: {e}"


    if prompt_length + gen_length > 2048: # Check context length (DREAM uses 2048)
         print(f"Warning: Requested length ({prompt_length + gen_length}) exceeds model max length (2048). Truncating gen_length.")
         gen_length = 2048 - prompt_length
         if gen_length <= 0:
             print("Error: Prompt is already too long.")
             return [([("Prompt too long.", "red")],)], "Error: Prompt too long."


    # --- State for Visualization Hook ---
    visualization_states = []
    last_x = None # Store the sequence from the previous step

    # Initial state: Prompt + all masks
    initial_x_part = torch.full((1, gen_length), MASK_ID, dtype=torch.long, device=device)
    # Apply initial constraints to the masked part *before* showing the first state
    for pos, token_id in processed_constraints.items():
        absolute_pos = pos # Position relative to start of generation
        if 0 <= absolute_pos < gen_length:
            initial_x_part[0, absolute_pos] = token_id

    initial_state_vis = []
    for i in range(gen_length):
        token_id = initial_x_part[0, i].item()
        if token_id == MASK_ID:
            initial_state_vis.append((MASK_TOKEN, "#444444")) # Mask color
        else:
            # This must be a constraint applied initially
            token_str = tokenizer.decode([token_id], skip_special_tokens=True)
            initial_state_vis.append((token_str if token_str else "?", "#800080")) # Constraint color (purple)
    visualization_states.append(initial_state_vis)

    # --- Define the Hook Function ---
    def generation_tokens_hook_func(step, x, logits):
        nonlocal last_x, visualization_states # Allow modification of outer scope variables
        print(f"Hook called for step {step}")

        current_x = x.clone() # Work on a copy for comparison

        # 1. Apply Constraints *before* generating visualization
        # Constraints are relative to the start of the *generated* part
        constrained_x = current_x.clone()
        prompt_len = current_x.shape[1] - gen_length # Recalculate just in case
        if prompt_len < 0:
            print("Warning: prompt_len negative in hook, skipping constraints/vis.")
            return current_x # Return unmodified if something is wrong

        constraints_applied_this_step = False
        for pos, token_id in processed_constraints.items():
            absolute_pos = prompt_len + pos
            if prompt_len <= absolute_pos < current_x.shape[1]:
                if constrained_x[0, absolute_pos] != token_id:
                    constrained_x[0, absolute_pos] = token_id
                    constraints_applied_this_step = True
                    # print(f"  Constraint applied at pos {pos} ({absolute_pos}) -> token {token_id}")


        # 2. Generate Visualization State for *this* step
        current_state_vis = []
        # Compare current_x (before explicit constraint application in *this* hook call)
        # with last_x (state from *previous* hook call / initial state)
        # Generate based on the state *before* reapplying constraints here,
        # but *after* the model's diffusion step determined current_x.
        gen_part_current = current_x[0, prompt_len:]
        gen_part_last = last_x[0, prompt_len:] if last_x is not None else None

        for i in range(gen_length):
            current_token_id = gen_part_current[i].item()
            token_str = tokenizer.decode([current_token_id], skip_special_tokens=True).strip()
            # Use a placeholder if decoding results in empty string
            display_token = token_str if token_str else MASK_TOKEN if current_token_id == MASK_ID else "?"

            # Check if this position is constrained
            is_constrained = i in processed_constraints

            if current_token_id == MASK_ID:
                color = "#444444" # Dark Gray for masks
            elif is_constrained and processed_constraints[i] == current_token_id:
                 color = "#800080" # Purple for correctly constrained tokens
            elif gen_part_last is None or gen_part_last[i].item() == MASK_ID:
                # Newly revealed (was mask in previous step or initial state)
                color = "#66CC66" # Light Green
            else:
                # Previously revealed and not masked
                color = "#6699CC" # Light Blue

            current_state_vis.append((display_token, color))

        visualization_states.append(current_state_vis)

        # 3. Update last_x for the *next* step's comparison
        # Store the state *after* applying constraints for accurate comparison next time
        last_x = constrained_x.clone()

        # 4. Return the sequence with constraints applied for the model's next step
        # print(f"Hook returning constrained_x: {constrained_x[:, prompt_len:]}")
        return constrained_x # Return the sequence with constraints enforced


    # --- Run DREAM Generation ---
    try:
        print("Calling model.diffusion_generate...")
        # Make sure last_x is initialized correctly before the first hook call
        # It should represent the state *before* the first diffusion step.
        initial_full_x = torch.cat([input_ids, initial_x_part], dim=1)
        last_x = initial_full_x.clone()

        output = model.diffusion_generate(
            input_ids,
            attention_mask=attention_mask,
            max_new_tokens=gen_length,
            output_history=False, # We build history in the hook
            return_dict_in_generate=True,
            steps=steps,
            temperature=temperature,
            top_p=top_p,
            alg=alg,
            alg_temp=alg_temp if alg != "origin" else 0.0, # alg_temp only for confidence algs
            generation_tokens_hook_func=generation_tokens_hook_func
        )
        print("model.diffusion_generate finished.")

        # Extract final generated sequence (response part only)
        # The hook ensures the returned sequence has constraints applied
        final_sequence = output.sequences[0]
        response_token_ids = final_sequence[prompt_length:]

        # Decode the final response
        final_text = tokenizer.decode(
            response_token_ids,
            skip_special_tokens=True,
            clean_up_tokenization_spaces=True # Recommended for cleaner output
        ).strip()
        print(f"Final generated text: {final_text}")

        # Add the very final state to visualization if the hook didn't capture it
        # (Should be captured, but as a safeguard)
        if len(visualization_states) <= steps: # Hook might run 'steps' times
             final_state_vis = []
             final_gen_part = final_sequence[prompt_length:]
             for i in range(gen_length):
                 token_id = final_gen_part[i].item()
                 token_str = tokenizer.decode([token_id], skip_special_tokens=True).strip()
                 display_token = token_str if token_str else MASK_TOKEN if token_id == MASK_ID else "?"
                 is_constrained = i in processed_constraints

                 if token_id == MASK_ID: color = "#444444"
                 elif is_constrained and processed_constraints[i] == token_id: color = "#800080"
                 else: color = "#6699CC" # Default to blue for final state tokens
                 final_state_vis.append((display_token, color))
             visualization_states.append(final_state_vis)


    except Exception as e:
        print(f"Error during generation: {e}")
        import traceback
        traceback.print_exc()
        # Add error message to visualization
        error_msg = f"Error during generation: {str(e)}"
        visualization_states.append([("Error", "red")])
        final_text = f"Generation failed: {e}"

    print("--- DREAM Generation Finished ---")
    return visualization_states, final_text


# --- Gradio UI Setup ---

css = '''
.category-legend{display:none}
/* button{height: 60px} */ /* Optional: Adjust button height */
.small_btn {
    max-width: 100px; /* Adjust as needed */
    height: 40px;    /* Adjust as needed */
    flex-grow: 0;  /* Prevent button from growing */
    margin-left: 5px; /* Add some space */
}
.chat-input-row {
    display: flex;
    align-items: center; /* Vertically align items */
}
.chat-input-row > * {
    margin-right: 5px; /* Space between textbox and button */
}
.chat-input-row > *:last-child {
    margin-right: 0;
}
'''
def create_chatbot_demo():
    with gr.Blocks(css=css) as demo:
        gr.Markdown("# Dream 7B - Diffusion Language Model Demo")
        gr.Markdown("A demonstration of the Dream 7B diffusion-based language model. Watch the text generate step-by-step.")
        gr.Markdown("[Model Card](https://huggingface.co/Dream-org/Dream-v0-Instruct-7B) - [Blog Post](https://hkunlp.github.io/blog/2025/dream/)")

        # STATE MANAGEMENT
        chat_history = gr.State([])

        # UI COMPONENTS
        with gr.Row():
            with gr.Column(scale=3):
                chatbot_ui = gr.Chatbot(
                    label="Conversation",
                    height=500,
                    bubble_full_width=False # Improves layout for shorter messages
                 )

                # Message input Row
                with gr.Row(elem_classes="chat-input-row"):
                        user_input = gr.Textbox(
                            label="Your Message",
                            placeholder="Type your message here and press Enter...",
                            scale=4, # Give textbox more space
                            container=False, # Remove container background/padding
                            show_label=False
                        )
                        send_btn = gr.Button("Send", scale=1, elem_classes="small_btn")

                constraints_input = gr.Textbox(
                    label="Word Constraints (Optional)",
                    info="Force specific words at positions (0-indexed from response start). Format: 'pos:word, pos:word'. Example: '0:Once, 5:upon, 10:time'",
                    placeholder="e.g., 0:Hello, 6:world",
                    value="" # Default empty
                )
            with gr.Column(scale=2):
                output_vis = gr.HighlightedText(
                    label="Denoising Process Visualization",
                    combine_adjacent=False,
                    show_legend=False, # Keep legend off as requested
                    # Color map for legend (though hidden)
                    # color_map={
                    #     "Mask": "#444444",
                    #     "New": "#66CC66",
                    #     "Old": "#6699CC",
                    #     "Constraint": "#800080",
                    #     "Error": "red"
                    # }
                )
                gr.Markdown(
                    "**Color Legend:** <span style='color:#444444'>■ Mask</span> | <span style='color:#66CC66'>■ Newly Generated</span> | <span style='color:#6699CC'>■ Previously Generated</span> | <span style='color:#800080'>■ Constraint</span>"
                )


        # 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=1.5, value=0.6, step=0.05, # Wider range for temp
                    label="Temperature"
                )
                top_p = gr.Slider(
                    minimum=0.0, maximum=1.0, value=0.95, step=0.05,
                    label="Top-P (Nucleus Sampling)"
                )
            with gr.Row():
                 # Map UI choices to DREAM's alg parameters
                remasking_strategy = gr.Radio(
                    choices=[
                        ("Random", "origin"), # User friendly name -> actual param
                        ("Entropy", "entropy"),
                        ("MaskGit+", "maskgit_plus"),
                        ("TopK Margin", "topk_margin"),
                    ],
                    value="entropy", # Default
                    label="Generation Order Strategy (alg)"
                )
                alg_temp = gr.Slider(
                    minimum=0.0, maximum=1.0, value=0.1, step=0.05,
                    label="Order Randomness (alg_temp)" ,
                    info="Adds randomness to non-Random strategies. Ignored for Random."
                )

            with gr.Row():
                visualization_delay = gr.Slider(
                    minimum=0.0, maximum=0.5, value=0.05, step=0.01,
                    label="Visualization Delay (seconds)"
                )

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

        # Hidden textbox to potentially store intermediate response (might not be needed)
        # current_response = gr.Textbox(visible=False)

        # --- Event Handlers ---

        # Helper to add message to history state
        def add_message_to_history(history, message, response):
            history = history.copy() # Modify copy
            history.append([message, response])
            return history

        # Function when user submits message (Enter or Send button)
        def user_message_submitted(message, history):
            print(f"User submitted: '{message}'")
            if not message or not message.strip():
                 print("Empty message submitted, doing nothing.")
                 # Return unchanged state if message is empty
                 # Need to return values for all outputs of the .submit/.click
                 return history, history, "", [] # history, chatbot_ui, user_input, output_vis

            # Add user message to history (with None for bot response initially)
            history = add_message_to_history(history, message, None)

            # Prepare updated history for display in Chatbot UI
            history_for_display = history.copy()

            # Clear the input textbox
            message_out = ""
             # Clear the visualization
            vis_clear = []

            # Return updated history state, chatbot display, cleared input, cleared visualization
            return history, history_for_display, message_out, vis_clear

        # Function to generate bot response (triggered after user message is processed)
        def bot_response_generator(
            history, gen_length, steps, constraints_text, delay,
            temperature, top_p, alg, alg_temp
            ):
            print("--- Generating Bot Response ---")
            if not history or history[-1][1] is not None:
                print("History empty or last message already has response. Skipping generation.")
                # Yield current state if called unnecessarily
                yield history, [], "No response generated."
                return

            # Get the conversation history in the format the model expects
            messages = format_chat_history(history) # Includes the latest user query

            # Parse constraints from the textbox
            parsed_constraints = parse_constraints(constraints_text)

            try:
                # Generate response with visualization
                vis_states, response_text = dream_generate_response_with_visualization(
                    messages,
                    gen_length=gen_length,
                    steps=steps,
                    constraints=parsed_constraints,
                    temperature=temperature,
                    top_p=top_p,
                    alg=alg,
                    alg_temp=alg_temp
                )

                # Update the history state with the final bot response
                history[-1][1] = response_text.strip()

                # Yield the initial visualization state immediately
                if vis_states:
                    yield history, vis_states[0] # Update chatbot, update visualization
                else:
                    # Handle case where generation failed before first state
                    yield history, [("Generation failed.", "red")]

                # Then animate through the rest of the visualization states
                for state in vis_states[1:]:
                    time.sleep(delay)
                    yield history, state # Update chatbot (implicitly via history), update visualization

            except Exception as e:
                print(f"Error in bot_response_generator: {e}")
                import traceback
                traceback.print_exc()
                error_msg = f"Error: {str(e)}"
                 # Show error in visualization
                error_vis = [(error_msg, "red")]
                 # Update history with error message? Optional.
                # history[-1][1] = error_msg
                yield history, error_vis

        # Function to clear everything
        def clear_conversation():
            print("Clearing conversation.")
            return [], [], "", [] # chat_history, chatbot_ui, user_input, output_vis

        # --- Wire UI elements to functions ---

        # Typing in Textbox and pressing Enter
        user_input.submit(
            fn=user_message_submitted,
            inputs=[user_input, chat_history],
            outputs=[chat_history, chatbot_ui, user_input, output_vis], # Update history state, chatbot display, clear input, clear vis
            queue=False # Process immediately
        ).then(
            fn=bot_response_generator,
            inputs=[
                chat_history, gen_length, steps, constraints_input, visualization_delay,
                temperature, top_p, remasking_strategy, alg_temp
            ],
            outputs=[chatbot_ui, output_vis] # Update chatbot display (with new response), update visualization
            # Note: history state is updated implicitly by bot_response_generator modifying its input
        )

        # Clicking the Send button
        send_btn.click(
            fn=user_message_submitted,
            inputs=[user_input, chat_history],
            outputs=[chat_history, chatbot_ui, user_input, output_vis],
            queue=False
        ).then(
            fn=bot_response_generator,
            inputs=[
                chat_history, gen_length, steps, constraints_input, visualization_delay,
                temperature, top_p, remasking_strategy, alg_temp
            ],
            outputs=[chatbot_ui, output_vis]
        )

        # Clicking the Clear button
        clear_btn.click(
            fn=clear_conversation,
            inputs=[],
            outputs=[chat_history, chatbot_ui, user_input, output_vis],
            queue=False
        )

    return demo

# --- Launch the Gradio App ---
if __name__ == "__main__":
    print("Creating Gradio demo...")
    demo = create_chatbot_demo()
    print("Launching Gradio demo...")
    # Use queue for potentially long generation times
    # share=True generates a public link (useful for Colab/Spaces)
    demo.queue().launch(share=True, debug=True) # Add debug=True for more logs