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import torch | |
from peft import PeftModel, PeftConfig | |
from transformers import AutoModelForCausalLM, AutoTokenizer | |
from IPython.display import display, Markdown | |
peft_model_id = f"adamtappis/marketing_emails_model" | |
config = PeftConfig.from_pretrained(peft_model_id) | |
model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=False) | |
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(product, description): | |
batch = tokenizer(f"### INSTRUCTION\nBelow is a product and description, please write a marketing email for this product.\n\n### Product:\n{product}\n### Description:\n{description}\n\n### Marketing Email:\n", return_tensors='pt') | |
with torch.cuda.amp.autocast(): | |
output_tokens = model.generate(**batch, max_new_tokens=200) | |
display(Markdown((tokenizer.decode(output_tokens[0], skip_special_tokens=True)))) | |
if __name__ == "__main__": | |
# make a gradio interface | |
import gradio as gr | |
gr.Interface( | |
make_inference, | |
[ | |
gr.inputs.Textbox(lines=1, label="Product Name"), | |
gr.inputs.Textbox(lines=1, label="Product Description"), | |
], | |
gr.outputs.Textbox(label="Email"), | |
title="🗣️Marketing Email Generator📄", | |
description="🗣️Marketing Email Generator📄 is a tool that allows you to generate marketing emails for different products", | |
).launch() |