MarketMailAI / app.py
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import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
peft_model_id = "Bsbell21/MarketMailAI"
config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForCausalLM.from_pretrained(
config.base_model_name_or_path,
return_dict=True,
device_map="auto"
)
#tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
mixtral_tokenizer = AutoTokenizer.from_pretrained(peft_model_id)
# Load the Lora model
model = PeftModel.from_pretrained(model, peft_model_id)
def input_from_text(product, description):
return f"<s>[INST]Below 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:[/INST]"
def make_inference(product, description):
inputs = mixtral_tokenizer(input_from_text(product, description), return_tensors="pt")
outputs = merged_model.generate(
**inputs,
max_new_tokens=150,
generation_kwargs={"repetition_penalty" : 1.7}
)
# print(mixtral_tokenizer.decode(outputs[0], skip_special_tokens=True))
result = mixtral_tokenizer.decode(outputs[0], skip_special_tokens=True).split("[/INST]")[1]
return result
'''
def make_inference(product_name, product_description):
batch = tokenizer(
f"### Product and Description:\n{product_name}: {product_description}\n\n### Ad:",
return_tensors="pt",
)
batch = {key: value.to('cuda:0') for key, value in batch.items()}
with torch.cuda.amp.autocast():
output_tokens = model.generate(**batch, max_new_tokens=50)
return 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.Textbox(lines=2, label="Product Name"),
gr.Textbox(lines=5, label="Product Description"),
],
gr.Textbox(label="Ad"),
title="GenerAd-AI",
description="GenerAd-AI is a generative model that generates ads for products.",
).launch()