app.py

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

from huggingface_hub import InferenceClient

"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
# client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
from unsloth import FastLanguageModel
import torch

max_seq_length = 2048
dtype = torch.float16
load_in_4bit = True

model_id = "giustinod/TestLogica-AZService"

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = model_id,
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit
)

FastLanguageModel.for_inference(model) # Enable native 2x faster inference

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>:"+str(item[0]), "\n<bot>:"+str(item[1])])
                for item in history_transformer_format])

    model_inputs = tokenizer([messages], return_tensors="pt").to("cuda")
    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 = 1.0,
        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


if __name__ == "__main__":
    gr.ChatInterface(predict).launch()