from huggingface_hub import from_pretrained_keras
from keras_cv import models
import gradio as gr

sd_dreambooth_model = models.StableDiffusion(
    img_width=512, img_height=512
)
db_diffusion_model = from_pretrained_keras("bharat-raghunathan/dreambooth_dosa")
sd_dreambooth_model._diffusion_model = db_diffusion_model
gr.HTML("<h2 style=\"font-size: 2em; font-weight: bold\" align=\"center\">Keras Dreambooth - The Humble Dosa</h2>")
gr.HTML("<p style=\"font-size: 14; font-weight: normal\" align=\"left\">This model has been fine-tuned to learn the concept of a dosa.")
# generate images
def infer(prompt):
    generated_images = sd_dreambooth_model.text_to_image(
        prompt
    )
    return generated_images 
    

output = gr.Gallery(label="Outputs").style(grid=(2,2))

gr.Examples(["realistic picture of a man eating a dosa",
             "realistic picture of a dosa on a plate",
             "realistic picture of a dosa in a restaurant"
            ],
            prompt, gallery, generate_images, cache_examples=True)
gr.Markdown('Demo created by [Bharat Raghunathan](https://huggingface.co/bharat-raghunathan/)')
# pass function, input type for prompt, the output for multiple images
gr.Interface(infer, inputs=["text"], outputs=[output]).launch()