Spaces:
Runtime error
Runtime error
File size: 5,051 Bytes
bb45d22 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 |
from threading import Thread
import torch
from PIL import Image
import gradio as gr
import spaces
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig, TextIteratorStreamer
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 = [
".gitattributes",
"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"
)
# def no_logger():
# logging.config.dictConfig({
# 'version': 1,
# 'disable_existing_loggers': True,
# })
# List of domains
MODEL_PATH = "./model/"
model = AutoModelForCausalLM.from_pretrained(
MODEL_PATH,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
).to(0)
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
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()
output = ""
for new_token in streamer:
output += new_token
yield output
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) |