Spaces:
Running
on
Zero
Running
on
Zero
File size: 3,029 Bytes
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from threading import Thread
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
import spaces
tokenizer = AutoTokenizer.from_pretrained("THUDM/glm-edge-1.5b-chat")
model = AutoModelForCausalLM.from_pretrained("THUDM/glm-edge-1.5b-chat", device_map='auto')
def preprocess_messages(history):
messages = []
for idx, (user_msg, model_msg) in enumerate(history):
if idx == len(history) - 1 and not messages:
messages.append({"role": "user", "content": user_msg})
break
if user_msg:
messages.append({"role": "user", "content": user_msg})
if model_msg:
messages.append({"role": "assistant", "content": messages})
return messages
@spaces.GPU()
def predict(history, max_length, top_p, temperature):
messages = preprocess_messages(history)
model_inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, tokenize=True, return_tensors="pt", return_dict=True
).to(model.device)
streamer = TextIteratorStreamer(tokenizer, timeout=60, skip_prompt=True, skip_special_tokens=True)
generate_kwargs = {
"input_ids": model_inputs["input_ids"],
"attention_mask": model_inputs["attention_mask"],
"streamer": streamer,
"max_new_tokens": max_length,
"do_sample": True,
"top_p": top_p,
"temperature": temperature,
"repetition_penalty": 1.2,
}
generate_kwargs['eos_token_id'] = tokenizer.encode("<|user|>")
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start()
for new_token in streamer:
if new_token:
history[-1][1] += new_token
yield history
def main():
with gr.Blocks() as demo:
gr.HTML("""<h1 align="center">GLM-Edge-Chat Gradio Demo</h1>""")
with gr.Row():
with gr.Column(scale=3):
chatbot = gr.Chatbot()
with gr.Row():
with gr.Column(scale=2):
user_input = gr.Textbox(show_label=True, placeholder="Input...", label="User Input")
submitBtn = gr.Button("Submit")
emptyBtn = gr.Button("Clear History")
with gr.Column(scale=1):
max_length = gr.Slider(0, 8192, value=4096, step=1.0, label="Maximum length", interactive=True)
top_p = gr.Slider(0, 1, value=0.8, step=0.01, label="Top P", interactive=True)
temperature = gr.Slider(0.01, 1, value=0.6, step=0.01, label="Temperature", interactive=True)
# Define functions for button actions
def user(query, history):
return "", history + [[query, ""]]
submitBtn.click(user, [user_input, chatbot], [user_input, chatbot], queue=False).then(
predict, [chatbot, max_length, top_p, temperature], chatbot
)
emptyBtn.click(lambda: (None, None), None, [chatbot], queue=False)
demo.queue()
demo.launch()
if __name__ == "__main__":
main()
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