app.py

import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer

peft_model_id = f"jaydenccc/AI_Storyteller"
config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForCausalLM.from_pretrained(
    config.base_model_name_or_path,
    return_dict=True,
    load_in_8bit=True,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)

# Load the Lora model
model = PeftModel.from_pretrained(model, peft_model_id)

def make_inference(synopsis):
    batch = tokenizer(
        f"Below is a one-sentence synopsis, please write a captivating short story based on this synopsis.\n\n### Synopsis:\n{synopsis}\n\n### Short Story:\n", return_tensors='pt',
    )

    with torch.cuda.amp.autocast():
        output_tokens = model.generate(**batch, max_new_tokens=400, temperature = 0.9)

    full_output = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
    short_story = full_output.split("### Short Story:\n")[-1].strip()

    return short_story



if __name__ == "__main__":
    # make a gradio interface
    import gradio as gr

    gr.Interface(
        make_inference,
        [
            gr.inputs.Textbox(lines=1, label="One-Sentence Plot"),
        ],
        gr.outputs.Textbox(label="Short Story"),
        title="AI-Storyteller",
        description="AI-Storyteller is a bot that writes short stories given a one-sentence synopsis",
    ).launch()