app_pkb.py

import gradio as gr
import torch
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    TextIteratorStreamer
)
import os
from threading import Thread
import spaces
import time

hf_token = os.environ["HF_TOKEN"]
model_name = os.environ["MODEL_NAME"]

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    token=hf_token
)

tokenizer = AutoTokenizer.from_pretrained(model_name, token=hf_token)

terminators = [
    tokenizer.eos_token_id,
]

if torch.cude.is_available():
    device = torch.device("cuda")
    print(f"Using GPU: {torch.cuda.get_device_name(device)}")
else:
    device =  torch.device("cpu")
    print("Using CPU")

model = model.to(device)

@spaces.GPU(duration=60)
def chat(message, history, temperature, do_sample, max_tokens):
    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_gereration_prompt=True)
    model_inputs = tokenizer([messages], return_tensors="pt").to(device)
    streamer = TextIteratorStreamer(
        tokenizer,
        timeout=20,
        skip_prompt=True,
        skip_special_tokens=True
    )
    generate_kwargs = dict(
        model_inputs,
        streamer=streamer,
        max_new_tokens=max_tokens,
        temperature=temperature,
        eos_token_id=terminators
    )

    if temperature == 0:
        generate_kwargs["do_sample"] = False

    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

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

    yield partial_text


demo = gr.ChatInterface(
    fn=chat,
    examples=[["write me a poem about machine Learning"]],
    additional_inputs_accordion=gr.Accordion(
        label="⚙️ Parameters", open=False, render=False
    ),
    additional_inputs=[
        gr.Slider(
            minimum=0, maximum=1, step=0.1, value=0.9, label="Temperature", render=False
        ),
        gr.Checkbox(label="Sampling", value=True),
        gr.Slider(
            minimum=128,
            maximum=4096,
            step=1,
            value=512,
            label="Max new tokens",
            render=False
        ),
        
    ],
    stop_btn="Stop Generation",
    title="Chat with Phi3.5 ERPNext",
    description="Noew Running antony - Phi3.5 ERPNext"
)

demo.launch()