import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from gradio.themes.utils import sizes


theme = gr.themes.Default(radius_size=sizes.radius_none).set(
    block_label_text_color = '#4D63FF',
    block_title_text_color = '#4D63FF',
    button_primary_text_color = '#4D63FF',
    button_primary_background_fill='#FFFFFF',
    button_primary_border_color='#4D63FF',
    button_primary_background_fill_hover='#EDEFFF',
)


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]

with gr.Blocks(theme=theme, css="footer {visibility: hidden}") as demo:
    with gr.Row():
        with gr.Column():
            box1 = gr.Textbox(label="文本")
            with gr.Row():
                button = gr.Button("提交", variant="primary")
                clear = gr.Button("清除", variant="primary")
        box2 = gr.Textbox(label="文本")

    button.click(fn=generate, inputs=box1, outputs=box2)
    clear.click(lambda x: gr.update(value=''), [], [box1])
    examples = gr.Examples(examples=[["photographer"], ["developer"]],  inputs=[box1], label="例子")


if __name__ == "__main__":
    demo.queue(concurrency_count=3).launch(server_name = "0.0.0.0", server_port = 7020)