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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import gradio as gr
tokenizer = AutoTokenizer.from_pretrained("merve/chatgpt-prompts-bart-long")
model = AutoModelForSeq2SeqLM.from_pretrained("merve/chatgpt-prompts-bart-long", from_tf=True)
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"], ["Linux Admin"]]
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()
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