import gradio as gr
import requests
import os
import torch as th
from torch import autocast
from diffusers import StableDiffusionPipeline

HF_TOKEN = os.environ["HF_TOKEN"]
#HF_TOKEN = os.environ.get("diffuse_new") or True
has_cuda = th.cuda.is_available()
device = th.device('cpu' if not th.cuda.is_available() else 'cuda')
print(f"device is :{device}")

# init stable diffusion model
#pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4",  torch_dtype=th.float32, use_auth_token= HF_TOKEN).to(device)  #revision="fp16",

def get_sd_old(translated_txt):
    scale=7.5 
    steps=45
    with autocast('cpu' if not th.cuda.is_available() else 'cuda'):
        image = pipe(translated_txt, guidance_scale=scale, num_inference_steps=steps)["sample"][0]
    return image

#API_URL = "https://api-inference.huggingface.co/models/bigscience/bloom"
#HF_TOKEN = os.environ.get("diffuse_new") or True

#headers = {"Authorization": f"Bearer {HF_TOKEN}"}
sd_inf = gr.Interface.load(name="spaces/stabilityai/stable-diffusion", api_key = HF_TOKEN, use_auth_token=HF_TOKEN )#'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')

def get_sd(translated_txt):
  print("******** Inside get_SD ********")
  print(f"translated_txt is : {translated_txt}")
  #sd_inf = gr.Blocks.load(name="spaces/stabilityai/stable-diffusion", use_auth_token='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')
  print(f"stable Diff inf is : {sd_inf}")
  sd_img_gallery = sd_inf(translated_txt, float(4),float(45), float(7.5),1024, fn_index=2) # fn_index=2)[0]  #(prompt, samples, steps, scale, seed) #translated_txt
  
  return sd_img_gallery[0]

demo = gr.Blocks()

with demo:
  gr.Markdown("Testing Diffusion models. STILL VERY MUCH WORK IN PROGRESS !!!!!!!!")
  
  with gr.Row():
    in_text_prompt = gr.Textbox(label="Enter English text here")
    #out_text_chinese = gr.Textbox(label="Your Chinese language output")
    b1 = gr.Button("Generate SD")

    out_sd = gr.Image(type="pil", label="SD output for the given prompt")
    
    b1.click(get_sd, in_text_prompt, out_sd) #out_gallery )  
    
demo.launch(enable_queue=True, debug=True)