app.py

import os
import random
import uuid
import json
import time
import asyncio
from threading import Thread

import gradio as gr
import spaces
import torch
import numpy as np
from PIL import Image
import cv2

from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    TextIteratorStreamer,
    Qwen2VLForConditionalGeneration,
    AutoProcessor,
)
from transformers.image_utils import load_image
from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler

# Additional imports for new TTS
from snac import SNAC
from huggingface_hub import snapshot_download
from dotenv import load_dotenv
load_dotenv()

# Set up device
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
tts_device = "cuda" if torch.cuda.is_available() else "cpu"  # for SNAC and Orpheus TTS

# Load DeepHermes Llama (chat/LLM) model
hermes_model_id = "prithivMLmods/DeepHermes-3-Llama-3-3B-Preview-abliterated"
hermes_llm_tokenizer = AutoTokenizer.from_pretrained(hermes_model_id)
hermes_llm_model = AutoModelForCausalLM.from_pretrained(
    hermes_model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16,
)
hermes_llm_model.eval()

# Load Qwen2-VL processor and model for multimodal tasks
MODEL_ID_QWEN = "prithivMLmods/Qwen2-VL-OCR2-2B-Instruct" 
# (If needed, you can pass extra arguments such as a size dict here if required.)
processor = AutoProcessor.from_pretrained(MODEL_ID_QWEN, trust_remote_code=True)
model_m = Qwen2VLForConditionalGeneration.from_pretrained(
    MODEL_ID_QWEN,
    trust_remote_code=True,
    torch_dtype=torch.float16
).to("cuda").eval()

# Load Orpheus TTS model and SNAC for TTS synthesis
print("Loading SNAC model...")
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz")
snac_model = snac_model.to(tts_device)

tts_model_name = "canopylabs/orpheus-3b-0.1-ft"
# Download only model config and safetensors
snapshot_download(
    repo_id=tts_model_name,
    allow_patterns=[
        "config.json",
        "*.safetensors",
        "model.safetensors.index.json",
    ],
    ignore_patterns=[
        "optimizer.pt",
        "pytorch_model.bin",
        "training_args.bin",
        "scheduler.pt",
        "tokenizer.json",
        "tokenizer_config.json",
        "special_tokens_map.json",
        "vocab.json",
        "merges.txt",
        "tokenizer.*"
    ]
)
orpheus_tts_model = AutoModelForCausalLM.from_pretrained(tts_model_name, torch_dtype=torch.bfloat16)
orpheus_tts_model.to(tts_device)
orpheus_tts_tokenizer = AutoTokenizer.from_pretrained(tts_model_name)
print(f"Orpheus TTS model loaded to {tts_device}")

# Some global parameters for chat and image generation
MAX_MAX_NEW_TOKENS = 2048
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))

# Stable Diffusion XL setup
MODEL_ID_SD = os.getenv("MODEL_VAL_PATH") #SG161222/RealVisXL_V5.0_Lightning
MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "4096"))
USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1"
ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
BATCH_SIZE = int(os.getenv("BATCH_SIZE", "1"))  # For batched image generation

sd_pipe = StableDiffusionXLPipeline.from_pretrained(
    MODEL_ID_SD,
    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
    use_safetensors=True,
    add_watermarker=False,
).to(device)
sd_pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(sd_pipe.scheduler.config)

if torch.cuda.is_available():
    sd_pipe.text_encoder = sd_pipe.text_encoder.half()
if USE_TORCH_COMPILE:
    sd_pipe.compile()
if ENABLE_CPU_OFFLOAD:
    sd_pipe.enable_model_cpu_offload()

MAX_SEED = np.iinfo(np.int32).max

# Utility functions
def save_image(img: Image.Image) -> str:
    unique_name = str(uuid.uuid4()) + ".png"
    img.save(unique_name)
    return unique_name

def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    return seed

def progress_bar_html(label: str) -> str:
    return f'''
<div style="display: flex; align-items: center;">
    <span style="margin-right: 10px; font-size: 14px;">{label}</span>
    <div style="width: 110px; height: 5px; background-color: #FFF0F5; border-radius: 2px; overflow: hidden;">
        <div style="width: 100%; height: 100%; background-color: #FF69B4; animation: loading 1.5s linear infinite;"></div>
    </div>
</div>
<style>
@keyframes loading {{
    0% {{ transform: translateX(-100%); }}
    100% {{ transform: translateX(100%); }}
}}
</style>
    '''

def downsample_video(video_path):
    vidcap = cv2.VideoCapture(video_path)
    total_frames = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))
    fps = vidcap.get(cv2.CAP_PROP_FPS)
    frames = []
    frame_indices = np.linspace(0, total_frames - 1, 10, dtype=int)
    for i in frame_indices:
        vidcap.set(cv2.CAP_PROP_POS_FRAMES, i)
        success, image = vidcap.read()
        if success:
            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
            pil_image = Image.fromarray(image)
            timestamp = round(i / fps, 2)
            frames.append((pil_image, timestamp))
    vidcap.release()
    return frames

def clean_chat_history(chat_history):
    cleaned = []
    for msg in chat_history:
        if isinstance(msg, dict) and isinstance(msg.get("content"), str):
            cleaned.append(msg)
    return cleaned

@spaces.GPU(duration=60, enable_queue=True)
def generate_image_fn(
    prompt: str,
    negative_prompt: str = "",
    use_negative_prompt: bool = False,
    seed: int = 1,
    width: int = 1024,
    height: int = 1024,
    guidance_scale: float = 3,
    num_inference_steps: int = 25,
    randomize_seed: bool = False,
    use_resolution_binning: bool = True,
    num_images: int = 1,
    progress=gr.Progress(track_tqdm=True),
):
    seed = int(randomize_seed_fn(seed, randomize_seed))
    generator = torch.Generator(device=device).manual_seed(seed)
    options = {
        "prompt": [prompt] * num_images,
        "negative_prompt": [negative_prompt] * num_images if use_negative_prompt else None,
        "width": width,
        "height": height,
        "guidance_scale": guidance_scale,
        "num_inference_steps": num_inference_steps,
        "generator": generator,
        "output_type": "pil",
    }
    if use_resolution_binning:
        options["use_resolution_binning"] = True
    images = []
    for i in range(0, num_images, BATCH_SIZE):
        batch_options = options.copy()
        batch_options["prompt"] = options["prompt"][i:i+BATCH_SIZE]
        if "negative_prompt" in batch_options and batch_options["negative_prompt"] is not None:
            batch_options["negative_prompt"] = options["negative_prompt"][i:i+BATCH_SIZE]
        if device.type == "cuda":
            with torch.autocast("cuda", dtype=torch.float16):
                outputs = sd_pipe(**batch_options)
        else:
            outputs = sd_pipe(**batch_options)
        images.extend(outputs.images)
    image_paths = [save_image(img) for img in images]
    return image_paths, seed

# New TTS functions (SNAC/Orpheus pipeline)
def process_prompt(prompt, voice, tokenizer, device):
    prompt = f"{voice}: {prompt}"
    input_ids = tokenizer(prompt, return_tensors="pt").input_ids
    start_token = torch.tensor([[128259]], dtype=torch.int64)  # Start of human
    end_tokens = torch.tensor([[128009, 128260]], dtype=torch.int64)  # End markers
    modified_input_ids = torch.cat([start_token, input_ids, end_tokens], dim=1)
    attention_mask = torch.ones_like(modified_input_ids)
    return modified_input_ids.to(device), attention_mask.to(device)

def parse_output(generated_ids):
    token_to_find = 128257
    token_to_remove = 128258
    token_indices = (generated_ids == token_to_find).nonzero(as_tuple=True)
    if len(token_indices[1]) > 0:
        last_occurrence_idx = token_indices[1][-1].item()
        cropped_tensor = generated_ids[:, last_occurrence_idx+1:]
    else:
        cropped_tensor = generated_ids
    processed_rows = []
    for row in cropped_tensor:
        masked_row = row[row != token_to_remove]
        processed_rows.append(masked_row)
    code_lists = []
    for row in processed_rows:
        row_length = row.size(0)
        new_length = (row_length // 7) * 7
        trimmed_row = row[:new_length]
        trimmed_row = [t - 128266 for t in trimmed_row]
        code_lists.append(trimmed_row)
    return code_lists[0]

def redistribute_codes(code_list, snac_model):
    device = next(snac_model.parameters()).device
    layer_1 = []
    layer_2 = []
    layer_3 = []
    for i in range((len(code_list)+1)//7):
        layer_1.append(code_list[7*i])
        layer_2.append(code_list[7*i+1]-4096)
        layer_3.append(code_list[7*i+2]-(2*4096))
        layer_3.append(code_list[7*i+3]-(3*4096))
        layer_2.append(code_list[7*i+4]-(4*4096))
        layer_3.append(code_list[7*i+5]-(5*4096))
        layer_3.append(code_list[7*i+6]-(6*4096))
    codes = [
        torch.tensor(layer_1, device=device).unsqueeze(0),
        torch.tensor(layer_2, device=device).unsqueeze(0),
        torch.tensor(layer_3, device=device).unsqueeze(0)
    ]
    audio_hat = snac_model.decode(codes)
    return audio_hat.detach().squeeze().cpu().numpy()

@spaces.GPU()
def generate_speech(text, voice, temperature, top_p, repetition_penalty, max_new_tokens, progress=gr.Progress()):
    if not text.strip():
        return None
    try:
        progress(0.1, "Processing text...")
        input_ids, attention_mask = process_prompt(text, voice, orpheus_tts_tokenizer, tts_device)
        progress(0.3, "Generating speech tokens...")
        with torch.no_grad():
            generated_ids = orpheus_tts_model.generate(
                input_ids=input_ids,
                attention_mask=attention_mask,
                max_new_tokens=max_new_tokens,
                do_sample=True,
                temperature=temperature,
                top_p=top_p,
                repetition_penalty=repetition_penalty,
                num_return_sequences=1,
                eos_token_id=128258,
            )
        progress(0.6, "Processing speech tokens...")
        code_list = parse_output(generated_ids)
        progress(0.8, "Converting to audio...")
        audio_samples = redistribute_codes(code_list, snac_model)
        return (24000, audio_samples)
    except Exception as e:
        print(f"Error generating speech: {e}")
        return None

# Main generate function for the chat interface
@spaces.GPU
def generate(
    input_dict: dict,
    chat_history: list[dict],
    max_new_tokens: int = 1024,
    temperature: float = 0.6,
    top_p: float = 0.9,
    top_k: int = 50,
    repetition_penalty: float = 1.2,
):
    """
    Generates chatbot responses with support for multimodal input, image generation,
    TTS, and LLM-augmented TTS.
    
    Trigger commands:
      - "@image": generate an image.
      - "@video-infer": process video.
      - "@<voice>-tts": directly convert text to speech.
      - "@<voice>-llm": infer with the DeepHermes Llama model then convert to speech.
    """
    text = input_dict["text"]
    files = input_dict.get("files", [])
    lower_text = text.strip().lower()

    # Branch for image generation.
    if lower_text.startswith("@image"):
        prompt = text[len("@image"):].strip()
        yield progress_bar_html("Generating Image")
        image_paths, used_seed = generate_image_fn(
            prompt=prompt,
            negative_prompt="",
            use_negative_prompt=False,
            seed=1,
            width=1024,
            height=1024,
            guidance_scale=3,
            num_inference_steps=25,
            randomize_seed=True,
            use_resolution_binning=True,
            num_images=1,
        )
        yield gr.Image(image_paths[0])
        return

    # Branch for video processing.
    if lower_text.startswith("@video-infer"):
        prompt = text[len("@video-infer"):].strip()
        if files:
            video_path = files[0]
            frames = downsample_video(video_path)
            messages = [
                {"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}]},
                {"role": "user", "content": [{"type": "text", "text": prompt}]}
            ]
            for frame in frames:
                image, timestamp = frame
                image_path = f"video_frame_{uuid.uuid4().hex}.png"
                image.save(image_path)
                messages[1]["content"].append({"type": "text", "text": f"Frame {timestamp}:"})
                messages[1]["content"].append({"type": "image", "url": image_path})
        else:
            messages = [
                {"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}]},
                {"role": "user", "content": [{"type": "text", "text": prompt}]}
            ]
        inputs = processor.apply_chat_template(
            messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt"
        ).to("cuda")
        streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
        generation_kwargs = {
            **inputs,
            "streamer": streamer,
            "max_new_tokens": max_new_tokens,
            "do_sample": True,
            "temperature": temperature,
            "top_p": top_p,
            "top_k": top_k,
            "repetition_penalty": repetition_penalty,
        }
        thread = Thread(target=model_m.generate, kwargs=generation_kwargs)
        thread.start()
        buffer = ""
        yield progress_bar_html("Processing video with Qwen2VL")
        for new_text in streamer:
            buffer += new_text.replace("<|im_end|>", "")
            time.sleep(0.01)
            yield buffer
        return

    # Define TTS and LLM tag mappings.
    tts_tags = {"@tara-tts": "tara", "@dan-tts": "dan", "@josh-tts": "josh", "@emma-tts": "emma"}
    llm_tags = {"@tara-llm": "tara", "@dan-llm": "dan", "@josh-llm": "josh", "@emma-llm": "emma"}

    # Branch for direct TTS (no LLM inference).
    for tag, voice in tts_tags.items():
        if lower_text.startswith(tag):
            text = text[len(tag):].strip()
            # Directly generate speech from the provided text.
            audio_output = generate_speech(text, voice, temperature, top_p, repetition_penalty, max_new_tokens)
            yield gr.Audio(audio_output, autoplay=True)
            return

    # Branch for LLM-augmented TTS.
    for tag, voice in llm_tags.items():
        if lower_text.startswith(tag):
            text = text[len(tag):].strip()
            conversation = [{"role": "user", "content": text}]
            input_ids = hermes_llm_tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt")
            if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
                input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
            input_ids = input_ids.to(hermes_llm_model.device)
            streamer = TextIteratorStreamer(hermes_llm_tokenizer, timeout=20.0, skip_prompt=True, skip_special_tokens=True)
            generation_kwargs = {
                "input_ids": input_ids,
                "streamer": streamer,
                "max_new_tokens": max_new_tokens,
                "do_sample": True,
                "top_p": top_p,
                "top_k": 50,
                "temperature": temperature,
                "num_beams": 1,
                "repetition_penalty": repetition_penalty,
            }
            t = Thread(target=hermes_llm_model.generate, kwargs=generation_kwargs)
            t.start()
            outputs = []
            for new_text in streamer:
                outputs.append(new_text)
            final_response = "".join(outputs)
            # Convert LLM response to speech.
            audio_output = generate_speech(final_response, voice, temperature, top_p, repetition_penalty, max_new_tokens)
            yield gr.Audio(audio_output, autoplay=True)
            return

    # Default branch for regular chat (text and multimodal without TTS).
    conversation = clean_chat_history(chat_history)
    conversation.append({"role": "user", "content": text})
    if files:
        if len(files) > 1:
            images = [load_image(image) for image in files]
        elif len(files) == 1:
            images = [load_image(files[0])]
        else:
            images = []
        messages = [{
            "role": "user",
            "content": [
                *[{"type": "image", "image": image} for image in images],
                {"type": "text", "text": text},
            ]
        }]
        prompt_full = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
        inputs = processor(text=[prompt_full], images=images, return_tensors="pt", padding=True).to("cuda")
        streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
        generation_kwargs = {**inputs, "streamer": streamer, "max_new_tokens": max_new_tokens}
        thread = Thread(target=model_m.generate, kwargs=generation_kwargs)
        thread.start()
        buffer = ""
        yield progress_bar_html("Processing Qwen2VL")
        for new_text in streamer:
            buffer += new_text.replace("<|im_end|>", "")
            time.sleep(0.01)
            yield buffer
    else:
        input_ids = hermes_llm_tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt")
        if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
            input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
            gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
        input_ids = input_ids.to(hermes_llm_model.device)
        streamer = TextIteratorStreamer(hermes_llm_tokenizer, timeout=20.0, skip_prompt=True, skip_special_tokens=True)
        generation_kwargs = {
            "input_ids": input_ids,
            "streamer": streamer,
            "max_new_tokens": max_new_tokens,
            "do_sample": True,
            "top_p": top_p,
            "top_k": top_k,
            "temperature": temperature,
            "num_beams": 1,
            "repetition_penalty": repetition_penalty,
        }
        t = Thread(target=hermes_llm_model.generate, kwargs=generation_kwargs)
        t.start()
        outputs = []
        yield progress_bar_html("Processing with DeepHermes LLM")
        for new_text in streamer:
            outputs.append(new_text)
            yield "".join(outputs)
        final_response = "".join(outputs)
        yield final_response

# Gradio Interface
demo = gr.ChatInterface(
    fn=generate,
    additional_inputs=[
        gr.Slider(label="Max new tokens", minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS),
        gr.Slider(label="Temperature", minimum=0.1, maximum=4.0, step=0.1, value=0.6),
        gr.Slider(label="Top-p (nucleus sampling)", minimum=0.05, maximum=1.0, step=0.05, value=0.9),
        gr.Slider(label="Top-k", minimum=1, maximum=1000, step=1, value=50),
        gr.Slider(label="Repetition penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.2),
    ],
    examples=[

        ["@josh-tts Hey! I’m Josh, [gasp] and wow, did I just surprise you with my realistic voice?"],
        ["@dan-llm Explain the General Relativity theorem in short"],
        ["@emma-tts Hey, I’m Emma, [sigh] and yes, I can talk just like a person… even when I’m tired."],
        ["@josh-llm What causes rainbows to form?"],
        ["@tara-tts Hey there, my name is Tara, [laugh] and I’m a speech generation model that can sound just like you!"],
        ["@dan-tts Yo, I’m Dan, [groan] and yes, I can even sound annoyed if I have to."],
        [{"text": "summarize the letter", "files": ["examples/1.png"]}],
        ["Write python program for array rotation"],
        ["@tara-llm Who is Nikola Tesla, and why did he die?"],
        ["@emma-llm Explain the causes of rainbows"],
        ["@image Chocolate dripping from a donut"],
        [{"text": "@video-infer Summarize the event in video", "files": ["examples/sky.mp4"]}],
        [{"text": "@video-infer Describe the video", "files": ["examples/Missing.mp4"]}],

    ],
    cache_examples=False,
    type="messages",
    description="# **Orpheus Edge🧤** `voice: tara, dan, emma, josh` \n `emotion: <laugh>, <chuckle>, <sigh>, <cough>, <sniffle>, <groan>, <yawn>, <gasp>. Use @video-infer, @image, orpheus: @<voice>-tts, or @<voice>-llm triggers llm response`",
    fill_height=True,
    textbox=gr.MultimodalTextbox(label="Query Input", file_types=["image", "video"], file_count="multiple", placeholder="‎ Use @tara-tts/@dan-tts for direct TTS or @tara-llm/@dan-llm for LLM+TTS, etc."),
    stop_btn="Stop Generation",
    multimodal=True,
)

if __name__ == "__main__":
    demo.queue(max_size=20).launch(share=True)