app.py

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ashercn97/awesome-prompts-merged")
model = AutoModelForCausalLM.from_pretrained("ashercn97/awesome-prompts-merged")
pipeline = pipeline("text2text-generation", model=model, tokenizer=tokenizer)

def generate(prompt):

  form = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n
  ### Instruction:\n
  {}\n
  ### Response:
  """.format(prompt)
  prompts = [form]
  results = pipeline(prompts, max_length=150)
  output = results[0]
  return results[0]

input_component = gr.Textbox(label = "Input a persona, e.g. photographer", value = "photographer")
output_component = gr.Textbox(label = "Prompt")
examples = [["photographer"], ["developer"]]
description = "This app generates ChatGPT prompts, it's based on a BART model trained on [this dataset](https://huggingface.co/datasets/fka/awesome-chatgpt-prompts). 📓 Simply enter a persona that you want the prompt to be generated based on. 🧙🏻🧑🏻‍🚀🧑🏻‍🎨🧑🏻‍🔬🧑🏻‍💻🧑🏼‍🏫🧑🏽‍🌾"
gr.Interface(generate, inputs = input_component, outputs=output_component, examples=examples, title = "👨🏻‍🎤 ChatGPT Prompt Generator 👨🏻‍🎤", description=description).launch()