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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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import gradio as gr |
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import spaces |
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tokenizer = AutoTokenizer.from_pretrained("merve/chatgpt-prompts-bart-long") |
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model = AutoModelForSeq2SeqLM.from_pretrained("merve/chatgpt-prompts-bart-long", from_tf=True) |
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@spaces.GPU |
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def generate(prompt): |
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batch = tokenizer(prompt, return_tensors="pt") |
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generated_ids = model.generate(batch["input_ids"], max_new_tokens=150) |
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output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) |
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return output[0] |
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input_component = gr.Textbox(label = "Input a persona, e.g. photographer", value = "photographer") |
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output_component = gr.Textbox(label = "Prompt") |
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examples = [["photographer"], ["developer"]] |
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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. 🧙🏻🧑🏻🚀🧑🏻🎨🧑🏻🔬🧑🏻💻🧑🏼🏫🧑🏽🌾" |
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gr.Interface(generate, inputs = input_component, outputs=output_component, examples=examples, title = "👨🏻🎤 ChatGPT Prompt Generator 👨🏻🎤", description=description).launch() |
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