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import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
import os
from threading import Thread
import spaces
token = os.environ["HF_TOKEN"]
model = AutoModelForCausalLM.from_pretrained("google/gemma-7b-it",
# torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
torch_dtype=torch.float16,
token=token)
tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b-it",token=token)
# using CUDA for an optimal experience
# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
device = torch.device('cuda')
model = model.to(device)
@spaces.GPU
def chat(message, history):
chat = []
for item in history:
chat.append({"role": "user", "content": item[0]})
if item[1] is not None:
chat.append({"role": "assistant", "content": item[1]})
chat.append({"role": "user", "content": message})
messages = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
# Tokenize the messages string
model_inputs = tokenizer([messages], return_tensors="pt").to(device)
streamer = TextIteratorStreamer(
tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
generate_kwargs = dict(
model_inputs,
streamer=streamer,
max_new_tokens=1024,
do_sample=True,
top_p=0.95,
top_k=1000,
temperature=0.75,
num_beams=1,
)
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start()
# Initialize an empty string to store the generated text
partial_text = ""
for new_text in streamer:
# print(new_text)
partial_text += new_text
# Yield an empty string to cleanup the message textbox and the updated conversation history
yield partial_text
demo = gr.ChatInterface(fn=chat,
chatbot=gr.Chatbot(show_label=True, show_share_button=True, show_copy_button=True, likeable=True, layout="bubble", bubble_full_width=False),
theme="soft",
examples=[["Write me a poem about Machine Learning."]],
title="Text Streaming")
demo.launch() |