# 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))))

import gradio as gr
from transformers import pipeline
pipe = pipeline("Marketing", model="adamtappis/marketing_emails_model")
demo = gr.Interface.from_pipeline(pipe)
demo.launch()

# def predict(text):
#   return pipe(text)[0]["translation_text"]


# 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()