import gradio as gr
import numpy as np
from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline

model_id = "google/ddpm-cat-256"
ddpm = DDPMPipeline.from_pretrained(model_id)

def flip_text(text):
    return text[::-1]

def flip_img(img):
    return np.flipud(img)

def show_cat():
    return ddpm(num_inference_steps=5).images[0]

with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("flip the text or image using this demo")
    with gr.Tab("Flip Text"):
        text_input = gr.Textbox();
        text_output = gr.Textbox();
        text_btn = gr.Button("Flip");
    with gr.Tab("Flip Img"):
        with gr.Row():
            image_input = gr.Image(source="webcam");
            image_outpt = gr.Image();
        image_btn = gr.Button("Flip");
    with gr.Tab("Goolg Cat"):
        img_cat = gr.Image()
        cat_btn = gr.Button("Show Cat")
    with gr.Accordion("Open for more"):
        gr.Markdown("Look at me");
    
    text_btn.click(flip_text, inputs=text_input, outputs=text_output)
    image_btn.click(flip_img, inputs=image_input, outputs=image_outpt)
    cat_btn.click(show_cat, outputs=img_cat)


demo.launch()