app.py

import os
from threading import Thread
from typing import Iterator
import json
import uuid

import gradio as gr
import spaces
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, BitsAndBytesConfig
from peft import PeftModel

MAX_MAX_NEW_TOKENS = 2048
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))

DESCRIPTION = """\
# Storytell AI 
Welcome to the Storytell AI space, crafted with care by Ranam & George. Dive into the world of educational storytelling with our [Storytell](https://huggingface.co/ranamhamoud/storytell) model. This iteration of the Llama 2 model with 7 billion parameters is fine-tuned to generate educational stories that engage and educate. Enjoy a journey of discovery and creativity—your storytelling lesson begins here!
"""


LICENSE = """
<p/>
---
As a derivate work of [Llama-2-7b-chat](https://huggingface.co/meta-llama/Llama-2-7b-chat) by Meta,
this demo is governed by the original [license](https://huggingface.co/spaces/huggingface-projects/llama-2-7b-chat/blob/main/LICENSE.txt) and [acceptable use policy](https://huggingface.co/spaces/huggingface-projects/llama-2-7b-chat/blob/main/USE_POLICY.md).
"""

if not torch.cuda.is_available():
    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"


if torch.cuda.is_available():
    bnb_config = BitsAndBytesConfig(
    load_in_8bit=True,
    bnb_4bit_compute_dtype=torch.float16,
    )
    model_id = "meta-llama/Llama-2-7b-chat-hf"
    base_model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto",quantization_config=bnb_config)
    model = PeftModel.from_pretrained(base_model,"ranamhamoud/storytell")
    tokenizer = AutoTokenizer.from_pretrained(model_id)
    tokenizer.pad_token = tokenizer.eos_token
    
def make_prompt(entry):
    return f"### Human: YOUR INSTRUCTION HERE,ONLY TELL A STORY: {entry} ### Assistant:"
    
@spaces.GPU
def generate(
    message: str,
    chat_history: list[tuple[str, str]],
    max_new_tokens: int = 1024,
    temperature: float = 0.1,  # Lower -> less random
    top_p: float = 0.1,  # Lower -> less random, considering only the top 10% of tokens at each step
    top_k: int = 1,  # Least random, only the most likely next token is considered
    repetition_penalty: float = 1.0,  # No repetition penalty
) -> Iterator[str]:
    conversation = []
    for user, assistant in chat_history:
        conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
    conversation.append({"role": "user", "content": make_prompt(message)})

    enc = tokenizer(make_prompt(message), return_tensors="pt", padding=True, truncation=True)


    input_ids = enc.input_ids
    if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
        input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
        gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
    input_ids = input_ids.to(model.device)

    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
    generate_kwargs = dict(
        {"input_ids": input_ids},
        streamer=streamer,
        max_new_tokens=max_new_tokens,
        do_sample=True,
        top_p=top_p,
        top_k=top_k,
        temperature=temperature,
        num_beams=1,
        repetition_penalty=repetition_penalty,
    )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

    outputs = []
    for text in streamer:
        outputs.append(text)
        yield "".join(outputs)

chat_interface = gr.ChatInterface(
    fn=generate,
    stop_btn=None,
    examples=[
        ["Can you explain briefly to me what is the Python programming language?"],
         ["I'm curious about Merge Sort."],
         ["Teach me about conditionals."]
    ],
)

with gr.Blocks(css="style.css") as demo:
    gr.Markdown(DESCRIPTION)
    chat_interface.render()
    gr.Markdown(LICENSE)

if __name__ == "__main__":
    demo.queue(max_size=20).launch()