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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import gradio as gr
import spaces

tokenizer = AutoTokenizer.from_pretrained("merve/chatgpt-prompts-bart-long")
model = AutoModelForSeq2SeqLM.from_pretrained("merve/chatgpt-prompts-bart-long", from_tf=True)

@spaces.GPU
def generate(prompt):

    batch = tokenizer(prompt, return_tensors="pt")
    generated_ids = model.generate(batch["input_ids"], max_new_tokens=150)
    output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
    return output[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). πŸ““ Simply enter a persona that you want the prompt to be generated based on. πŸ§™πŸ»πŸ§‘πŸ»β€πŸš€πŸ§‘πŸ»β€πŸŽ¨πŸ§‘πŸ»β€πŸ”¬πŸ§‘πŸ»β€πŸ’»πŸ§‘πŸΌβ€πŸ«πŸ§‘πŸ½β€πŸŒΎ"
gr.Interface(generate, inputs = input_component, outputs=output_component, examples=examples, title = "πŸ‘¨πŸ»β€πŸŽ€ ChatGPT Prompt Generator πŸ‘¨πŸ»β€πŸŽ€", description=description).launch()