app.py

from threading import Thread
import torch
from PIL import Image
import gradio as gr
import spaces
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from huggingface_hub.inference._generated.types import TextGenerationStreamOutput, TextGenerationStreamOutputToken
import os
from huggingface_hub import hf_hub_download



os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"

HF_TOKEN = os.environ.get("HF_TOKEN", None)
MODEL_ID = os.environ.get("MODEL_ID")
MODEL_NAME = MODEL_ID.split("/")[-1]

TITLE = "<h1><center>VL-Chatbox</center></h1>"

DESCRIPTION = "<h3><center>MODEL LOADED: " + MODEL_NAME + "</center></h3>"

DEFAULT_SYSTEM = "You named Chatbox. You are a good assitant."

CSS = """
.duplicate-button {
  margin: auto !important;
  color: white !important;
  background: black !important;
  border-radius: 100vh !important;
}
"""

filenames = [
    "config.json",
    "generation_config.json",
    "model-00001-of-00004.safetensors",
    "model-00002-of-00004.safetensors",
    "model-00003-of-00004.safetensors",
    "model-00004-of-00004.safetensors",
    "model.safetensors.index.json",
    "special_tokens_map.json",
    "tokenizer.json",
    "tokenizer_config.json"
]

for filename in filenames:
    downloaded_model_path = hf_hub_download(
        repo_id=MODEL_ID,
        filename=filename,
        local_dir="./model/"
    )

for items in os.listdir("./model"):
    print(items)

# def no_logger():
#     logging.config.dictConfig({
#         'version': 1,
#         'disable_existing_loggers': True,
#     })


model = AutoModelForCausalLM.from_pretrained(
    "./model/",
    torch_dtype=torch.float16,
    low_cpu_mem_usage=True,
    trust_remote_code=True
).to(0)
tokenizer = AutoTokenizer.from_pretrained("./model/",trust_remote_code=True)
vision_tower = model.get_vision_tower()
vision_tower.load_model()
vision_tower.to(device="cuda", dtype=torch.float16)
image_processor = vision_tower.image_processor
tokenizer.pad_token = tokenizer.eos_token

# Define terminators (if applicable, adjust as needed)
terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]




@spaces.GPU
def stream_chat(message, history: list, system: str, temperature: float, max_new_tokens: int):
    print(message)
    conversation = [{"role": "system", "content": system or DEFAULT_SYSTEM}]
    for prompt, answer in history:
        conversation.extend([{"role": "user", "content": prompt}, {"role": "assistant", "content": answer}])

    conversation.append({"role": "user", "content": message['text']})
    if message["files"]:
        image = Image.open(message["files"][0]).convert('RGB')
        # Process the conversation text
        inputs = model.build_conversation_input_ids(
            tokenizer, 
            query=message['text'], 
            image=image, 
            image_processor=image_processor,
        )
        input_ids = inputs["input_ids"].to(device='cuda', non_blocking=True)
        images = inputs["image"].to(dtype=torch.float16, device='cuda', non_blocking=True)
    else:
        input_ids = tokenizer.apply_chat_template(
            conversation, 
            add_generation_prompt=True, 
            return_tensors="pt"
        ).to(model.device)
        images = None
    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)

    generate_kwargs = dict(
        input_ids=input_ids,
        streamer=streamer,
        max_new_tokens=max_new_tokens,
        temperature=temperature,
        do_sample=True,
        eos_token_id=terminators,
        images=images
    )
    if temperature == 0:
        generate_kwargs["do_sample"] = False

    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()
    input_token_len = input_ids.shape[1]
    
    output = ""
    for next_text in streamer:
        yield TextGenerationStreamOutput(
            index=0,
            token=TextGenerationStreamOutputToken(
                id=0,
                logprob=0,
                text=next_text,
                special=False,
            )
        )


chatbot = gr.Chatbot(height=450)
chat_input = gr.MultimodalTextbox(interactive=True, file_types=["image"], placeholder="Enter message or upload file...", show_label=False)

with gr.Blocks(css=CSS) as demo:
    gr.HTML(TITLE)
    gr.HTML(DESCRIPTION)
    gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button")
    gr.ChatInterface(
        fn=stream_chat,
        multimodal=True,
        chatbot=chatbot,
        textbox=chat_input,
        fill_height=True,
        additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
        additional_inputs=[
            gr.Text(
                value="",
                label="System",
                render=False,
            ),
            gr.Slider(
                minimum=0,
                maximum=1,
                step=0.1,
                value=0.8,
                label="Temperature",
                render=False,
            ),
            gr.Slider(
                minimum=128,
                maximum=4096,
                step=1,
                value=1024,
                label="Max new tokens",
                render=False,
            ),
        ],
    )


if __name__ == "__main__":
    demo.queue(api_open=False).launch(show_api=False, share=False)