app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
from threading import Thread

import torch, transformers
import sys, os
sys.path.append(
    os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)))
from transformers import AutoModelForCausalLM,AutoTokenizer,LlamaTokenizer

print("Creat tokenizer...")
tokenizer = LlamaTokenizer.from_pretrained('IEITYuan/Yuan2-2B-Janus-hf', add_eos_token=False, add_bos_token=False, eos_token='<eod>')
tokenizer.add_tokens(['<sep>', '<pad>', '<mask>', '<predict>', '<FIM_SUFFIX>', '<FIM_PREFIX>', '<FIM_MIDDLE>','<commit_before>','<commit_msg>','<commit_after>','<jupyter_start>','<jupyter_text>','<jupyter_code>','<jupyter_output>','<empty_output>'], special_tokens=True)

print("Creat model...")
model = AutoModelForCausalLM.from_pretrained('IEITYuan/Yuan2-2B-Janus-hf', device_map='auto', torch_dtype=torch.bfloat16, trust_remote_code=True)
# using CUDA for an optimal experience
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = model.to(device)

# Defining a custom stopping criteria class for the model's text generation.
class StopOnTokens(StoppingCriteria):
    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
        stop_ids = [2]  # IDs of tokens where the generation should stop.
        for stop_id in stop_ids:
            if input_ids[0][-1] == stop_id:  # Checking if the last generated token is a stop token.
                return True
        return False


# Function to generate model predictions.
def predict(message, history):
    # history_transformer_format = history + [[message, ""]]
    # stop = StopOnTokens()
    #
    # # Formatting the input for the model.
    # messages = "</s>".join(["</s>".join(["\n<|user|>:" + item[0], "\n<|assistant|>:" + item[1]])
    #                     for item in history_transformer_format])
    # 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=50,
    #     temperature=0.7,
    #     num_beams=1,
    #     stopping_criteria=StoppingCriteriaList([stop])
    # )
    # t = Thread(target=model.generate, kwargs=generate_kwargs)
    # t.start()  # Starting the generation in a separate thread.
    # partial_message = ""
    # for new_token in streamer:
    #     partial_message += new_token
    #     if '</s>' in partial_message:  # Breaking the loop if the stop token is generated.
    #         break
    #     yield partial_message
    inputs = tokenizer(message, return_tensors="pt")["input_ids"].to(device)
    outputs = model.generate(inputs, do_sample=False, max_length=500)
    print(tokenizer.decode(outputs[0]))
    return(tokenizer.decode(outputs[0]))


# Setting up the Gradio chat interface.
gr.ChatInterface(predict,
                 title="Yuan2_2b_chatBot",
                 description="请提问",
                 examples=['请问目前最先进的机器学习算法有哪些？','作一首关于新年快乐的诗','北京烤鸭怎么做？']
                 ).launch()  # Launching the web interface.