app.py

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

model = AutoModelForCausalLM.from_pretrained(
    "DuckyBlender/racist-phi3", 
  
    torch_dtype=torch.float16, 
    trust_remote_code=True,
  
)
tokenizer = AutoTokenizer.from_pretrained("DuckyBlender/racist-phi3")
device = torch.device("cpu")
model = model.to(device)

class StopOnTokens(StoppingCriteria):
    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
        stop_ids = [29, 0]
        for stop_id in stop_ids:
            if input_ids[0][-1] == stop_id:
                return True
        return False

def predict(message, history):
    history_transformer_format = history + [[message, ""]]
    stop = StopOnTokens()

    messages = "".join(["".join(["\n<human>:"+item[0], "\n<bot>:"+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=512,
        do_sample=True,
        top_p=0.90,
        top_k=1000,
        temperature=0.9,
        num_beams=1,
        stopping_criteria=StoppingCriteriaList([stop])
        )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

    partial_message = ""
    for new_token in streamer:
        if new_token != '<':
            partial_message += new_token
            yield partial_message

gr.ChatInterface(predict,theme='HaleyCH/HaleyCH_Theme').launch()