Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,246 Bytes
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from threading import Thread
import gradio as gr
import spaces
import torch
from PIL import Image
from transformers import AutoModelForVision2Seq, AutoProcessor, AutoTokenizer, TextIteratorStreamer
TITLE = "<h1><center>Chat with PaliGemma-3B-Chat-v0.1</center></h1>"
DESCRIPTION = "<h3><center>Visit <a href='https://huggingface.co/hiyouga/PaliGemma-3B-Chat-v0.1' target='_blank'>our model page</a> for details.</center></h3>"
CSS = """
.duplicate-button {
margin: auto !important;
color: white !important;
background: black !important;
border-radius: 100vh !important;
}
"""
model_id = "hiyouga/PaliGemma-3B-Chat-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForVision2Seq.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
@spaces.GPU
def stream_chat(message: Dict[str, str], history: list):
print(message)
conversation = []
for prompt, answer in history:
conversation.extend([{"role": "user", "content": prompt}, {"role": "assistant", "content": answer}])
conversation.append({"role": "user", "content": message})
input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt").to(
model.device
)
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,
)
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)
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,
fill_height=True,
cache_examples=False,
)
if __name__ == "__main__":
demo.launch()
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