| 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"], ["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, output=output_component, title = "ChatGPT Prompt Generator", description=description).launch() | |