import gradio as gr
import os

title="Prompt Converter"
description="""
Stable Diffusion 2 uses OpenCLIP ViT-H model trained LAION dataset so it knows different things than the OpenAI ViT-L we're all used to prompting.
<br />This demo converts a v1.x stable diffusion prompt to a stable diffusion 2.x prompt, by generating an image through RunwayML Stable Diffusion 1.5, then Interrogate the resulting image through CLIP Interrogator 2 to give you a Stable Diffusion 2 equivalent prompt.
"""
stable_diffusion = gr.Blocks.load(name="spaces/runwayml/stable-diffusion-v1-5")
clip_interrogator_2 = gr.Blocks.load(name="spaces/fffiloni/CLIP-Interrogator-2")

def get_images(prompt):
    gallery_dir = stable_diffusion(prompt, fn_index=2)
    
    img_results = [os.path.join(gallery_dir, img) for img in os.listdir(gallery_dir)]
  
    return img_results[0]

def get_new_prompt(img):
    interrogate = clip_interrogator_2(img, 'best', 4, api_name="clipi2")
    #print(interrogate)
    return interrogate

def infer(prompt):
    img = get_images(prompt)
    
    result = get_new_prompt(img)
    #print(result)
    return result[0]
    
prompt_input = gr.Textbox(lines=4, label="Input v1.x Stable Diffusion prompt")
prompt_output = gr.Textbox(lines=4, label="Converted v2.x Stable Diffusion prompt")

gr.Interface(fn=infer, inputs=[prompt_input], outputs=[prompt_output],title=title,description=description).queue(max_size=10).launch(enable_queue=True)