app.py

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

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

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

MAX_MAX_NEW_TOKENS = 2048
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

model_id = "prithivMLmods/FastThink-0.5B-Tiny"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16,
)
model.eval()

TTS_VOICES = [
    "en-US-JennyNeural",  # @tts1
    "en-US-GuyNeural",    # @tts2
]
MODEL_ID_VL = "prithivMLmods/Qwen2-VL-OCR-2B-Instruct" 
processor = AutoProcessor.from_pretrained(MODEL_ID_VL, trust_remote_code=True)
model_m = Qwen2VLForConditionalGeneration.from_pretrained(
    MODEL_ID_VL,
    trust_remote_code=True,
    torch_dtype=torch.float16
).to("cuda").eval()

async def text_to_speech(text: str, voice: str, output_file="output.mp3"):
    communicate = edge_tts.Communicate(text, voice)
    await communicate.save(output_file)
    return output_file

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

bad_words = json.loads(os.getenv('BAD_WORDS', "[]"))
bad_words_negative = json.loads(os.getenv('BAD_WORDS_NEGATIVE', "[]"))
default_negative = os.getenv("default_negative", "")

def check_text(prompt, negative=""):
    for i in bad_words:
        if i in prompt:
            return True
    for i in bad_words_negative:
        if i in negative:
            return True
    return False

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

MAX_SEED = np.iinfo(np.int32).max
CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "0") == "1"
MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "2048"))
USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1"
ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"

dtype = torch.float16 if device.type == "cuda" else torch.float32

if torch.cuda.is_available():
    # Lightning 5 model
    pipe = StableDiffusionXLPipeline.from_pretrained(
        "SG161222/RealVisXL_V5.0_Lightning",
        torch_dtype=dtype,
        use_safetensors=True,
        add_watermarker=False
    ).to(device)
    pipe.text_encoder = pipe.text_encoder.half()
    if ENABLE_CPU_OFFLOAD:
        pipe.enable_model_cpu_offload()
    else:
        pipe.to(device)
        print("Loaded RealVisXL_V5.0_Lightning on Device!")
    if USE_TORCH_COMPILE:
        pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
        print("Model RealVisXL_V5.0_Lightning Compiled!")
    
    # Lightning 4 model
    pipe2 = StableDiffusionXLPipeline.from_pretrained(
        "SG161222/RealVisXL_V4.0_Lightning",
        torch_dtype=dtype,
        use_safetensors=True,
        add_watermarker=False,
    ).to(device)
    pipe2.text_encoder = pipe2.text_encoder.half()
    if ENABLE_CPU_OFFLOAD:
        pipe2.enable_model_cpu_offload()
    else:
        pipe2.to(device)
        print("Loaded RealVisXL_V4.0 on Device!")
    if USE_TORCH_COMPILE:
        pipe2.unet = torch.compile(pipe2.unet, mode="reduce-overhead", fullgraph=True)
        print("Model RealVisXL_V4.0 Compiled!")
    
    # Turbo v3 model
    pipe3 = StableDiffusionXLPipeline.from_pretrained(
        "SG161222/RealVisXL_V3.0_Turbo",
        torch_dtype=dtype,
        use_safetensors=True,
        add_watermarker=False,
    ).to(device)
    pipe3.text_encoder = pipe3.text_encoder.half()
    if ENABLE_CPU_OFFLOAD:
        pipe3.enable_model_cpu_offload()
    else:
        pipe3.to(device)
        print("Loaded RealVisXL_V3.0_Turbo on Device!")
    if USE_TORCH_COMPILE:
        pipe3.unet = torch.compile(pipe3.unet, mode="reduce-overhead", fullgraph=True)
        print("Model RealVisXL_V3.0_Turbo Compiled!")
else:
    pipe = StableDiffusionXLPipeline.from_pretrained(
        "SG161222/RealVisXL_V5.0_Lightning",
        torch_dtype=dtype,
        use_safetensors=True,
        add_watermarker=False
    ).to(device)
    pipe2 = StableDiffusionXLPipeline.from_pretrained(
        "SG161222/RealVisXL_V4.0_Lightning",
        torch_dtype=dtype,
        use_safetensors=True,
        add_watermarker=False,
    ).to(device)
    pipe3 = StableDiffusionXLPipeline.from_pretrained(
        "SG161222/RealVisXL_V3.0_Turbo",
        torch_dtype=dtype,
        use_safetensors=True,
        add_watermarker=False,
    ).to(device)
    print("Running on CPU; models loaded in float32.")

DEFAULT_MODEL = "Lightning 5"
MODEL_CHOICES = [DEFAULT_MODEL, "Lightning 4", "Turbo v3"]
models = {
    "Lightning 5": pipe,
    "Lightning 4": pipe2,
    "Turbo v3": pipe3
}

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

@spaces.GPU
def generate(
    input_dict: dict,
    chat_history: list[dict],
    max_new_tokens: int = DEFAULT_MAX_NEW_TOKENS,
    temperature: float = 0.6,
    top_p: float = 0.9,
    top_k: int = 50,
    repetition_penalty: float = 1.2,
):
    text = input_dict["text"]
    files = input_dict.get("files", [])

    lower_text = text.lower().strip()
    # Check if the prompt is an image generation command using model flags.
    if (lower_text.startswith("@lightningv5") or 
        lower_text.startswith("@lightningv4") or 
        lower_text.startswith("@turbov3")):
        
        # Determine model choice based on flag.
        model_choice = None
        if "@lightningv5" in lower_text:
            model_choice = "Lightning 5"
        elif "@lightningv4" in lower_text:
            model_choice = "Lightning 4"
        elif "@turbov3" in lower_text:
            model_choice = "Turbo v3"
        
        # Remove the model flag from the prompt.
        prompt_clean = re.sub(r"@lightningv5", "", text, flags=re.IGNORECASE)
        prompt_clean = re.sub(r"@lightningv4", "", prompt_clean, flags=re.IGNORECASE)
        prompt_clean = re.sub(r"@turbov3", "", prompt_clean, flags=re.IGNORECASE)
        prompt_clean = prompt_clean.strip().strip('"')
        
        # Default parameters for single image generation.
        width = 1024
        height = 1024
        guidance_scale = 6.0
        seed_val = 0
        randomize_seed_flag = True
        
        seed_val = int(randomize_seed_fn(seed_val, randomize_seed_flag))
        generator = torch.Generator(device=device).manual_seed(seed_val)
        
        options = {
            "prompt": prompt_clean,
            "negative_prompt": default_negative,
            "width": width,
            "height": height,
            "guidance_scale": guidance_scale,
            "num_inference_steps": 30,
            "generator": generator,
            "num_images_per_prompt": 1,
            "use_resolution_binning": True,
            "output_type": "pil",
        }
        if device.type == "cuda":
            torch.cuda.empty_cache()
        
        selected_pipe = models.get(model_choice, pipe)
        yield "⚡ Generating image ███████▒▒▒ 69%"
        images = selected_pipe(**options).images
        image_path = save_image(images[0])
        yield gr.Image(image_path)
        return

    # Otherwise, handle text/chat (and TTS) generation.
    tts_prefix = "@tts"
    is_tts = any(text.strip().lower().startswith(f"{tts_prefix}{i}") for i in range(1, 3))
    voice_index = next((i for i in range(1, 3) if text.strip().lower().startswith(f"{tts_prefix}{i}")), None)
    
    if is_tts and voice_index:
        voice = TTS_VOICES[voice_index - 1]
        text = text.replace(f"{tts_prefix}{voice_index}", "").strip()
        conversation = [{"role": "user", "content": text}]
    else:
        voice = None
        text = text.replace(tts_prefix, "").strip()
        conversation = clean_chat_history(chat_history)
        conversation.append({"role": "user", "content": text})
    
    if files:
        images = [load_image(image) for image in files] if len(files) > 1 else [load_image(files[0])]
        messages = [{
            "role": "user",
            "content": [
                *[{"type": "image", "image": image} for image in images],
                {"type": "text", "text": text},
            ]
        }]
        prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
        inputs = processor(text=[prompt], 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 "💭 Thinking..."
        for new_text in streamer:
            buffer += new_text
            buffer = buffer.replace("<|im_end|>", "")
            time.sleep(0.01)
            yield buffer
    else:
        input_ids = 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(model.device)
        streamer = TextIteratorStreamer(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=model.generate, kwargs=generation_kwargs)
        t.start()

        outputs = []
        for new_text in streamer:
            outputs.append(new_text)
            yield "".join(outputs)

        final_response = "".join(outputs)
        yield final_response

        if is_tts and voice:
            output_file = asyncio.run(text_to_speech(final_response, voice))
            yield gr.Audio(output_file, autoplay=True)

DESCRIPTION = """
# Imagineo Chat ⚡
"""

css = '''
h1 {
  text-align: center;
  display: block;
}

#duplicate-button {
  margin: auto;
  color: #fff;
  background: #1565c0;
  border-radius: 100vh;
}
'''

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=[
        ["Python Program for Array Rotation"],
        ["@tts1 Who is Nikola Tesla, and why did he die?"],
        ['@lightningv5 Chocolate dripping from a donut against a yellow background, in the style of brocore, hyper-realistic'],
        ['@lightningv4 A serene landscape with mountains'],
        ['@turbov3 Abstract art, colorful and vibrant'],
        ["@tts2 What causes rainbows to form?"],
    ],
    cache_examples=False,
    type="messages",
    description=DESCRIPTION,
    css=css,
    fill_height=True,
    textbox=gr.MultimodalTextbox(label="Query Input", file_types=["image"], file_count="multiple", placeholder="use the tags @lightningv5 @lightningv4 @turbov3 for image gen !"),
    stop_btn="Stop Generation",
    multimodal=True,
    
)

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