app.py

import os
import re
import torch
from threading import Thread
from typing import Iterator
from mongoengine import connect, Document, StringField, SequenceField
import gradio as gr
import spaces
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextIteratorStreamer
from peft import PeftModel

# Constants
MAX_MAX_NEW_TOKENS = 2048
DEFAULT_MAX_NEW_TOKENS = 930
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))

# Description and License Texts
DESCRIPTION = """
# ✨Storytell AI🧑🏽‍💻
Welcome to the **Storytell AI** space, crafted with care by Ranam & George. Dive into the world of educational storytelling with our 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! You can prompt this model to explain any computer science concept. **Please check the examples below**. 
"""
LICENSE = """
---
As a derivative work of [Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) 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).
"""

# GPU Check and add CPU warning
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():
    model_id = "meta-llama/Llama-2-7b-hf"
    bnb_config = BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_use_double_quant=False,
        bnb_4bit_quant_type="nf4",
        bnb_4bit_compute_dtype=torch.bfloat16
    )
    base_model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", quantization_config=bnb_config)
    storytell_model = PeftModel.from_pretrained(base_model, "ranamhamoud/storytell")
    storytell_tokenizer = AutoTokenizer.from_pretrained(model_id)
    storytell_tokenizer.pad_token = storytell_tokenizer.eos_token
    
    
    editing_model_id = "meta-llama/Llama-2-7b-chat-hf"
    editing_model = AutoModelForCausalLM.from_pretrained(editing_model_id, torch_dtype=torch.float16, device_map="auto")
    editing_tokenizer = AutoTokenizer.from_pretrained(model_id)
    editing_tokenizer.use_default_system_prompt = False

    
# MongoDB Connection
PASSWORD = os.environ.get("MONGO_PASS")
connect(host=f"mongodb+srv://ranamhammoud11:{PASSWORD}@stories.zf5v52a.mongodb.net/")

# MongoDB Document
class Story(Document):
    message = StringField()
    content = StringField()
    story_id = SequenceField(primary_key=True)

# Utility function for prompts
def make_prompt(entry):
    return  f"### Human: Don't repeat the assesments, limit to 500 words {entry} ### Assistant:"
    # f"TELL A STORY, RELATE TO COMPUTER SCIENCE, INCLUDE ASSESMENTS. MAKE IT REALISTIC AND AROUND 800 WORDS, END THE STORY WITH "THE END.": {entry}"

def process_text(text):
    text = re.sub(r'\[.*?\]', '', text, flags=re.DOTALL)

    return text


# Gradio Function
@spaces.GPU
def generate(
    model_choice: str,
    message: str,
    chat_history: list[tuple[str, str]],
    max_new_tokens: int = DEFAULT_MAX_NEW_TOKENS,
    temperature: float = 0.6,
    top_p: float = 0.7,
    top_k: int = 20,
    repetition_penalty: float = 1.0,
) -> Iterator[str]:
    conversation = []
    if model_choice == "Storytell":
        model = storytell_model
        tokenizer = storytell_tokenizer
    else:
        model = editing_model
        tokenizer = editing_tokenizer
    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.to(model.device)
    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.")
    
    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=False)
    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:
        processed_text = process_text(text)
        outputs.append(processed_text)
        output = "".join(outputs)
        yield output

    final_story = "".join(outputs)
    try:
        saved_story = Story(message=message, content=final_story).save()
        yield f"{final_story}\n\n Story saved with ID: {saved_story.story_id}"
    except Exception as e:
        yield f"Failed to save story: {str(e)}"

# Gradio Interface Setup
chat_interface = gr.ChatInterface(
    fn=generate,
    stop_btn=None,
    additional_inputs=[gr.Dropdown(["Storytell", "HF Meta Llama 7b Chat"], label="Choose Model")],
    examples=[
        ["Can you explain briefly to me what is the Python programming language?"],
        ["Could you please provide an explanation about the concept of recursion?"],
        ["Could you explain what a URL is?"]
    ],
    theme='shivi/calm_seafoam'
)

# Gradio Web Interface
with gr.Blocks(css="style.css",theme='shivi/calm_seafoam') as demo:
    gr.Markdown(DESCRIPTION)
    chat_interface.render()
    gr.Markdown(LICENSE)


# Main Execution
if __name__ == "__main__":
    demo.queue(max_size=20)
    demo.launch(share=True)