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 edge_tts

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

# ============================================
#            CHAT & TTS SETUP
# ============================================

DESCRIPTION = """
# QwQ Edge 💬
"""

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

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

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")

# Load text-only model and tokenizer
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
TTS_VOICES = [
    "en-US-JennyNeural",  # @tts1
    "en-US-GuyNeural",    # @tts2
]

# Load multimodal (OCR) model and processor
MODEL_ID = "prithivMLmods/Qwen2-VL-OCR-2B-Instruct" 
processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)
model_m = Qwen2VLForConditionalGeneration.from_pretrained(
    MODEL_ID,
    trust_remote_code=True,
    torch_dtype=torch.float16
).to("cuda").eval()

async def text_to_speech(text: str, voice: str, output_file="output.mp3"):
    """Convert text to speech using Edge TTS and save as MP3"""
    communicate = edge_tts.Communicate(text, voice)
    await communicate.save(output_file)
    return output_file

def clean_chat_history(chat_history):
    """
    Filter out any chat entries whose "content" is not a string.
    This helps prevent errors when concatenating previous messages.
    """
    cleaned = []
    for msg in chat_history:
        if isinstance(msg, dict) and isinstance(msg.get("content"), str):
            cleaned.append(msg)
    return cleaned

# ============================================
#            IMAGE GENERATION SETUP
# ============================================

# Environment variables and parameters for Stable Diffusion XL
MODEL_ID_SD = os.getenv("MODEL_VAL_PATH")  # SDXL Model repository path via env variable
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

# Load the SDXL pipeline
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)

# **Fix for dtype mismatch in the text encoder:**
if torch.cuda.is_available():
    sd_pipe.text_encoder = sd_pipe.text_encoder.half()

# Optional: compile the model for speedup if enabled
if USE_TORCH_COMPILE:
    sd_pipe.compile()

# Optional: offload parts of the model to CPU if needed
if ENABLE_CPU_OFFLOAD:
    sd_pipe.enable_model_cpu_offload()

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

def save_image(img: Image.Image) -> str:
    """Save a PIL image with a unique filename and return the path."""
    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

@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),
):
    """Generate images using the SDXL pipeline."""
    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]
        images.extend(sd_pipe(**batch_options).images)
    image_paths = [save_image(img) for img in images]
    return image_paths, seed

# ============================================
#       MAIN GENERATION FUNCTION (CHAT)
# ============================================

@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, TTS, and image generation.
    Special commands:
      - "@tts1" or "@tts2": triggers text-to-speech.
      - "@image": triggers image generation using the SDXL pipeline.
    """
    text = input_dict["text"]
    files = input_dict.get("files", [])

    # ----------------------------
    #  IMAGE GENERATION BRANCH
    # ----------------------------
    if text.strip().lower().startswith("@image"):
        # Remove the "@image" tag and use the rest as prompt
        prompt = text[len("@image"):].strip()
        yield "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 the generated image so that the chat interface displays it.
        yield gr.Image(image_paths[0])
        return  # Exit early

    # ----------------------------
    #  TTS Branch (if query starts with @tts)
    # ----------------------------
    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()
        # Clear previous chat history for a fresh TTS request.
        conversation = [{"role": "user", "content": text}]
    else:
        voice = None
        # Remove any stray @tts tags and build the conversation history.
        text = text.replace(tts_prefix, "").strip()
        conversation = clean_chat_history(chat_history)
        conversation.append({"role": "user", "content": text})

    # ----------------------------
    #  Multimodal (image + text) branch
    # ----------------------------
    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 = 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:
        # ----------------------------
        #  Text-only branch
        # ----------------------------
        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 TTS was requested, convert the final response to speech.
        if is_tts and voice:
            output_file = asyncio.run(text_to_speech(final_response, voice))
            yield gr.Audio(output_file, autoplay=True)

# ============================================
#             GRADIO DEMO SETUP
# ============================================

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=[
        ["@tts1 Who is Nikola Tesla, and why did he die?"],
        [{"text": "Extract JSON from the image", "files": ["examples/document.jpg"]}],
        [{"text": "summarize the letter", "files": ["examples/1.png"]}],
        ["A train travels 60 kilometers per hour. If it travels for 5 hours, how far will it travel in total?"],
        ["Write a Python function to check if a number is prime."],
        ["@tts2 What causes rainbows to form?"],
        ["@image A futuristic city skyline at dusk"],
    ],
    cache_examples=False,
    type="messages",
    description=DESCRIPTION,
    css=css,
    fill_height=True,
    textbox=gr.MultimodalTextbox(label="Query Input", file_types=["image"], file_count="multiple"),
    stop_btn="Stop Generation",
    multimodal=True,
)

if __name__ == "__main__":
    # To create a public link, set share=True in launch().
    demo.queue(max_size=20).launch(share=True)