app.py

import os
from threading import Thread
from typing import Iterator

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,INCLUDE AT LEAST AN MCQ, FILL IN THE BLANK AND TRUE OR FALSE: {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()