Update app.py

app.py

@@ -12,17 +12,11 @@ import requests
 from huggingface_hub import login
 
 import os
-
+import streamlit as st
 from transformers import  AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
 
 
 
-AUTH_TOKEN = os.environ["AUTH_TOKEN"]
-
-login(token=AUTH_TOKEN)
-
-
-
 processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
 model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined")
 
@@ -34,13 +28,15 @@ IPpipe = StableDiffusionInpaintPipeline.from_pretrained(
     IPmodel_path,
     revision="fp16", 
     torch_dtype=torch.float16,
+    use_auth_token=st.secrets["AUTH_TOKEN"],
+    num_images_per_prompt=num_samples,
 ).to(device)
 
 trans_tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
 trans_model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M")
 
 
-SDpipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", revision="fp16", torch_dtype=torch.float16, use_auth_token=True).to(device)
+SDpipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", revision="fp16", torch_dtype=torch.float16, use_auth_token=st.secrets["AUTH_TOKEN"]).to(device)
 
 
 def create_mask(image, prompt):
@@ -89,7 +85,6 @@ def generate_image(image, product_name, target_name):
       mask_image=mask,
       guidance_scale=guidance_scale,
       generator=generator,
-      num_images_per_prompt=num_samples,
   ).images
 
   return im