app.py

import gradio as gr
import torch
import numpy as np
import json
import time
from transformers import AutoTokenizer
import os
import importlib
from huggingface_hub import hf_hub_download
import spaces
from dotenv import load_dotenv
from infer import (
    load_trained_model,
    find_answer_start,
    get_noising_schedule,
    noisify_answer,
    generate_diffusion_text,
    filter_logits
)
from models import CustomTransformerModel
from model_config import CustomTransformerConfig

# Load .env only when running locally
if os.getenv("HF_TOKEN") is None:
    load_dotenv()

hf_token = os.getenv("HF_TOKEN")

if hf_token is None:
    raise ValueError("HF_TOKEN is not set")

rng = np.random.default_rng()

# Add new noising function
def confidence_guided_noising(input_ids, answer_start, confidences, noise_clipping, threshold=1.0, noise_start=1.0):
    noised = input_ids.copy()
    answer_len = len(input_ids) - answer_start
    num_to_noise = int(threshold * answer_len * noise_start)
    if num_to_noise == 0:
        return noised, []

    all_indices = np.arange(answer_start, len(input_ids))
    eos_indices = [i for i in all_indices if input_ids[i] == eos_token_id]
    non_eos_indices = [i for i in all_indices if input_ids[i] != eos_token_id]

    # Proportionally split how many to noise
    num_non_eos_to_noise = int(num_to_noise * len(non_eos_indices) / (len(non_eos_indices) + len(eos_indices) + 1e-5))
    num_eos_to_noise = num_to_noise - num_non_eos_to_noise

    noised_indices = []

    # --- Non-EOS ---
    if non_eos_indices:
        raw_weights = 1.0 - np.array([confidences[i - answer_start] for i in non_eos_indices])
        raw_weights = np.clip(raw_weights, a_min=noise_clipping, a_max=None)
        weights = raw_weights / raw_weights.sum()

        chosen = rng.choice(non_eos_indices, size=min(num_non_eos_to_noise, len(non_eos_indices)), replace=False, p=weights)
        noised_indices.extend(chosen.tolist())

    # --- EOS ---
    if eos_indices and num_eos_to_noise > 0:
        raw_weights = 1.0 - np.array([confidences[i - answer_start] for i in eos_indices])
        raw_weights = np.clip(raw_weights, a_min=noise_clipping, a_max=None)
        weights = raw_weights / raw_weights.sum()

        chosen = rng.choice(eos_indices, size=min(num_eos_to_noise, len(eos_indices)), replace=False, p=weights)
        noised_indices.extend(chosen.tolist())

    for idx in noised_indices:
        noised[idx] = mask_token_id

    noised_indices = sorted(noised_indices)
    return noised, noised_indices

@spaces.GPU
def generate_diffusion_text(input_ids, top_p, top_k):
    with torch.no_grad():
        input_tensor = torch.tensor([input_ids], dtype=torch.long).to(model.device)
        with torch.amp.autocast('cuda', dtype=torch.float16):
            logits = model(input_ids=input_tensor)["logits"]
        logits = filter_logits(logits, top_k=top_p, top_p=top_k) 
        logits = logits.clamp(min=-1e8, max=1e4)
        probs = torch.nn.functional.softmax(logits, dim=-1)[0]
        probs = torch.clamp(probs, min=1e-8, max=1.0)
        assert torch.all(torch.isfinite(probs)), "Non-finite values in probs!"
        assert (probs >= 0).all(), "Negative probs!"
        sampled = torch.multinomial(probs, num_samples=1).squeeze(-1).tolist()

        # Extract confidence of selected tokens
        conf = probs[range(len(sampled)), sampled].cpu().numpy()
    return sampled, conf 

def format_chat_prompt(question):
    return (
        "<|begin_of_text|>\n"
        "<|start_header_id|>system<|end_header_id|>\n"
        "You are a helpful assistant.\n"
        "<|start_header_id|>user<|end_header_id|>\n"
        f"{question}\n"
        "<|start_header_id|>assistant<|end_header_id|>\n"
    )

def render_html(label, text):
    return f"<b>{label}</b><br><div style='white-space: pre-wrap; line-height:1.8'>{text}</div>"

def highlight_tokens(tokens, color_indices=None, color="green"):
    highlighted = []
    for j, tok in enumerate(tokens):
        if tokenizer.convert_tokens_to_ids(tok) == eos_token_id:
            continue
        token_str = tokenizer.convert_tokens_to_string([tok])
        if color_indices and j in color_indices:
            highlighted.append(f'<span style="color:{color}">{token_str}</span>')
        else:
            highlighted.append(token_str)
    return "".join(highlighted)

# --- Inference Wrapper ---
def diffusion_chat(question, max_it, pause_length, sharpness, 
                   clustering, noise_start, use_confidence_noising, 
                   noise_clipping, top_p, top_k):

    if question.strip() == "":
        question = "What do you know about the city of Amsterdam?"

    prompt = format_chat_prompt(question)
    input_ids = tokenizer.encode(prompt, add_special_tokens=False)
    answer_start = find_answer_start(input_ids, assistant_marker_ids)
    if answer_start is None:
        yield render_html("Error", "Could not find Assistant marker in input.")
        return

    input_ids = (input_ids + [mask_token_id] * (256 - len(input_ids)))[:256]
    ori_input_tokens = input_ids

    current_tokens, just_noised_indices = noisify_answer(
        input_ids, answer_start, tokenizer, threshold=1.0, clustering=clustering, noise_start=1.0
    )
    yield render_html("Iteration 0 (initial noise)",
                      tokenizer.decode(current_tokens[answer_start:], skip_special_tokens=True))
    time.sleep(pause_length)

    last_tokens = []
    prev_tokens = []

    for i in range(max_it):
        generated_tokens, confidences = generate_diffusion_text(current_tokens, top_p, top_k)
        current_tokens = ori_input_tokens[:answer_start] + generated_tokens[answer_start:]

        decoded = tokenizer.convert_ids_to_tokens(current_tokens[answer_start:])
        diff_indices = [j for j in range(len(decoded)) if j >= len(prev_tokens) or decoded[j] != prev_tokens[j]]
        prev_tokens = decoded

        yield render_html(f"Iteration {i+1}/{max_it} (after generation)",
                          highlight_tokens(decoded, diff_indices, color="green"))
        time.sleep(pause_length)

        # Early stopping
        last_tokens.append(current_tokens)
        if len(last_tokens) > 3:
            last_tokens.pop(0)
        if len(last_tokens) == 3 and len(set(map(tuple, last_tokens))) == 1:
            yield render_html("Stopped early", f"After {i+1} iterations.")
            break

        # Noising step
        threshold = get_noising_schedule(i, max_it, sharpness=sharpness)
        if use_confidence_noising:
            noised_answer, just_noised_indices = confidence_guided_noising(
                current_tokens, answer_start, confidences, noise_clipping,
                threshold=threshold, noise_start=noise_start
            )
        else:
            noised_answer, just_noised_indices = noisify_answer(
                current_tokens, answer_start, tokenizer,
                threshold=threshold, clustering=clustering, noise_start=noise_start
            )

        decoded = tokenizer.convert_ids_to_tokens(current_tokens[answer_start:])
        red_indices = [j for j in range(len(decoded)) if (answer_start + j) in just_noised_indices]
        yield render_html(f"Iteration {i+1}/{max_it} (before noising)",
                          highlight_tokens(decoded, red_indices, color="red"))

        current_tokens = ori_input_tokens[:answer_start] + noised_answer[answer_start:]

    # Final output
    answer_ids = current_tokens[answer_start:]
    try:
        final_ids = answer_ids[:answer_ids.index(eos_token_id)]
    except ValueError:
        final_ids = answer_ids

    final_output = tokenizer.decode(final_ids, skip_special_tokens=True)
    yield render_html(f"Final Output ({len(final_ids)} tokens after {i+1} iterations)", final_output)


# --- Gradio Interface ---
print("Loading model...")
ckpt_path = hf_hub_download(
    repo_id="ruurd/tini_model",
    filename="diffusion-model-8B.pth",
    token=os.getenv("HF_TOKEN")
)
model, tokenizer = load_trained_model(checkpoint_path=ckpt_path)
print("✅ Model loaded.")

vocab_size = len(tokenizer)
eos_token_id = tokenizer.eos_token_id
mask_token_id = tokenizer.encode('MASK', add_special_tokens=False)[0]
assistant_marker_ids = tokenizer.encode("<|start_header_id|>assistant<|end_header_id|>", add_special_tokens=False)

demo = gr.Interface(
    fn=diffusion_chat,
    inputs=[
        gr.Textbox(label="User Question", lines=2, placeholder="What do you know about the city of Amsterdam?"),
        gr.Slider(1, 512, value=64, step=1, label="Number of iterarions: ↑ = more iterations"),
        gr.Slider(0.01, 5, value=0.01, step=0.01, label="Pause between iteration ↑ = longer pause"),
        gr.Slider(1.0, 20.0, value=1.0, step=0.5, label="Noise decay sharpness: ↓ = more noise in later iterations"),
        gr.Slider(0.0, 1.0, value=0.0, step=0.05, label="Clustering: ↑ = more clustered noising"),
        gr.Slider(0.0, 1.0, value=0.2, step=0.05, label="Noise start fraction: ↑ = more noise"),
        gr.Checkbox(value=False, label="Use confidence-guided noising"),
        gr.Slider(0.01, 1.0, value=0.01, step=0.01, label="Noise clipping: ↓ = more confidence guidance"),
        gr.Slider(1, 1000, value = 100, step = 1, label = "Top-p: ↑ = more random answers"),
        gr.Slider(0.0, 1.0, value = 0.9, step = 0.01, label = "Top-k: ↑ = more random answers")
    ],
    outputs=[gr.HTML(label="Diffusion Output")],
    title="Diffusion Language Model Chat",
    theme="default",
    description="This interface runs a diffusion-based language model to generate answers progressively."
)

demo.launch(share=True, allowed_paths=["."], ssr_mode=False)