Spaces:
Running
on
Zero
Running
on
Zero
File size: 3,315 Bytes
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import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
from threading import Thread
import spaces
class ChatInterface:
def __init__(self, model_name="deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"):
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
device_map="auto"
)
def format_chat_prompt(self, message, history, system_message):
messages = [{"role": "system", "content": system_message}]
for user_msg, assistant_msg in history:
if user_msg:
messages.append({"role": "user", "content": user_msg})
if assistant_msg:
messages.append({"role": "assistant", "content": assistant_msg})
messages.append({"role": "user", "content": message})
# Format messages according to model's expected chat template
prompt = self.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
return prompt
@spaces.GPU
def generate_response(
self,
message,
history,
system_message,
max_tokens,
temperature,
top_p,
):
prompt = self.format_chat_prompt(message, history, system_message)
inputs = self.tokenizer(prompt, return_tensors="pt").to(self.model.device)
# Setup streamer
streamer = TextIteratorStreamer(
self.tokenizer,
timeout=10.0,
skip_prompt=True,
skip_special_tokens=True
)
# Generate in a separate thread to enable streaming
generation_kwargs = dict(
inputs=inputs,
streamer=streamer,
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
do_sample=True,
)
thread = Thread(target=self.model.generate, kwargs=generation_kwargs)
thread.start()
# Stream the response
response = ""
for new_text in streamer:
response += new_text
yield response
def create_demo():
chat_interface = ChatInterface()
demo = gr.ChatInterface(
chat_interface.generate_response,
additional_inputs=[
gr.Textbox(
value="You are a friendly Chatbot.",
label="System message"
),
gr.Slider(
minimum=1,
maximum=2048,
value=512,
step=1,
label="Max new tokens"
),
gr.Slider(
minimum=0.1,
maximum=4.0,
value=0.7,
step=0.1,
label="Temperature"
),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)"
),
],
)
return demo
if __name__ == "__main__":
demo = create_demo()
demo.launch() |