app.py

import os
from threading import Event, Thread
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    StoppingCriteria,
    StoppingCriteriaList,
    TextIteratorStreamer,
)
from huggingface_hub import login
import gradio as gr
import torch

login(os.getenv("HF_TOKEN", None))

model_name = "richardr1126/spider-natsql-wizard-coder-8bit"
tok = AutoTokenizer.from_pretrained(model_name)

max_new_tokens = 1536

print(f"Starting to load the model {model_name}")

m = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map=0,
    load_in_8bit=True,
)

m.config.pad_token_id = m.config.eos_token_id
m.generation_config.pad_token_id = m.config.eos_token_id

stop_tokens = [";", "###", "Result"]
stop_token_ids = tok.convert_tokens_to_ids(stop_tokens)

print(f"Successfully loaded the model {model_name} into memory")

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

def bot(input_message: str, temperature=0.1, top_p=0.9, top_k=0, repetition_penalty=1.08):
    stop = StopOnTokens()

    messages = input_message

    input_ids = tok(messages, return_tensors="pt").input_ids
    input_ids = input_ids.to(m.device)
    streamer = TextIteratorStreamer(tok, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
    generate_kwargs = dict(
        input_ids=input_ids,
        max_new_tokens=max_new_tokens,
        temperature=temperature,
        do_sample=temperature > 0.0,
        top_p=top_p,
        top_k=top_k,
        repetition_penalty=repetition_penalty,
        streamer=streamer,
        stopping_criteria=StoppingCriteriaList([stop]),
    )

    stream_complete = Event()

    def generate_and_signal_complete():
        m.generate(**generate_kwargs)
        stream_complete.set()

    t1 = Thread(target=generate_and_signal_complete)
    t1.start()

    partial_text = ""
    for new_text in streamer:
        partial_text += new_text

    return partial_text

gradio_interface = gr.Interface(
  fn=bot,
  inputs=[
      "text",
      gr.Slider(label="Temperature", minimum=0.0, maximum=1.0, value=0.1, step=0.1),
      gr.Slider(label="Top-p (nucleus sampling)", minimum=0.0, maximum=1.0, value=0.9, step=0.01),
      gr.Slider(label="Top-k", minimum=0, maximum=200, value=0, step=1),
      gr.Slider(label="Repetition Penalty", minimum=1.0, maximum=2.0, value=1.08, step=0.1)
  ],
  outputs="text",
  title="REST API with Gradio and Huggingface Spaces",
  description="This is a demo of how to build an AI powered REST API with Gradio and Huggingface Spaces – for free! See the **Use via API** link at the bottom of this page.",
)
gradio_interface.launch()