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"]]

# Beschreibung hier korrekt eingerückt
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,
    theme="ParityError/Interstellar"  # Hinzugefügtes Theme
).launch()