app.py

import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
import time
import numpy as np
from torch.nn import functional as F
import os
from threading import Thread

print(f"Starting to load the model to memory")
m = AutoModelForCausalLM.from_pretrained(
    "stabilityai/stablelm-2-zephyr-1_6b", torch_dtype=torch.float32, trust_remote_code=True)
tok = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-zephyr-1_6b", trust_remote_code=True)
generator = pipeline('text-generation', model=m, tokenizer=tok)
print(f"Sucessfully loaded the model to the memory")


start_message = ""

def user(message, history):
    # Append the user's message to the conversation history
    return "", history + [[message, ""]]


def chat(message, history):
    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 = tok.apply_chat_template(chat, tokenize=False)
    # Tokenize the messages string
    model_inputs = tok([messages], return_tensors="pt")
    streamer = TextIteratorStreamer(
        tok, 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=0.75,
        num_beams=1,
    )
    t = Thread(target=m.generate, kwargs=generate_kwargs)
    t.start()

    # print(history)
    # Initialize an empty string to store the generated text
    partial_text = ""
    for new_text in streamer:
        # print(new_text)
        partial_text += new_text
        history[-1][1] = partial_text
        # Yield an empty string to cleanup the message textbox and the updated conversation history
        yield history
    return partial_text


# with gr.Blocks() as demo:
#     # history = gr.State([])
#     gr.Markdown("## Stable LM 2 Zephyr 1.6b")
#     gr.HTML('''<center><a href="https://huggingface.co/spaces/stabilityai/stablelm-2-1_6b-zephyr?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate the Space to skip the queue and run in a private space</center>''')
#     chatbot = gr.Chatbot().style(height=500)
#     with gr.Row():
#         with gr.Column():
#             msg = gr.Textbox(label="Chat Message Box", placeholder="Chat Message Box",
#                              show_label=False).style(container=False)
#         with gr.Column():
#             with gr.Row():
#                 submit = gr.Button("Submit")
#                 stop = gr.Button("Stop")
#                 clear = gr.Button("Clear")

#     submit_event = msg.submit(fn=user, inputs=[msg, chatbot], outputs=[msg, chatbot], queue=False).then(
#         fn=chat, inputs=[chatbot], outputs=[chatbot], queue=True)
#     submit_click_event = submit.click(fn=user, inputs=[msg, chatbot], outputs=[msg, chatbot], queue=False).then(
#         fn=chat, inputs=[chatbot], outputs=[chatbot], queue=True)
#     stop.click(fn=None, inputs=None, outputs=None, cancels=[
#                submit_event, submit_click_event], queue=False)
#     clear.click(lambda: None, None, [chatbot], queue=False)
demo = gr.ChatInterface(fn=echo, examples=["hello", "hola", "merhaba"], title="Stable LM 2 Zephyr 1.6b")
demo.queue(max_size=32, concurrency_count=2)
demo.launch()