change model version

app.py

@@ -1,12 +1,7 @@
 import streamlit as st # HF spaces at v1.2.0
 from demo import load_model,generate,get_dataset,embed,make_meme
-from PIL import Image
-import numpy as np
-import io
-# TODOs
-# Add markdown short readme project intro
-# Add link to wandb logs
 
+import io
 
 st.sidebar.subheader("This butterfly does not exist! ")
 st.sidebar.image("assets/logo.png", width=200)
@@ -36,8 +31,8 @@ def img2download(image):
         return imgByteArr
 
 model_name='ceyda/butterfly_cropped_uniq1K_512'
-# model_version='0edac54b81958b82ce9fd5c1f688c33ac8e4f223'
-model_version=None ##TBD
+model_version='57d36a15546909557d9f967f47713236c8288838'
+# model_version=None
 model=load_model_intocache(model_name,model_version)
 dataset=load_dataset()
 load_variables()
@@ -73,16 +68,6 @@ Based on [paper:](https://openreview.net/forum?id=1Fqg133qRaI) *Towards Faster a
             i=j%col_num
             cols[i].image(im)
             picks[j]=cols[i].button("Find Nearest",key="pick_"+str(j))
-            # meme_it=cols[i].button("What is this?",key="meme_"+str(j))
-            # if meme_it:
-            #     no_bg=st.checkbox("Remove background?",True)
-            #     meme_text=st.text_input("Meme text","Is this a pigeon?")
-            #     meme=make_meme(im,text=meme_text,show_text=True,remove_background=no_bg)
-            #     st.image(meme)
-            # if picks[j]:
-            #     scores, retrieved_examples=dataset.get_nearest_examples('beit_embeddings', embed(im), k=5)
-            #     for r in retrieved_examples["image"]:
-            #         st.image(r)
         
         if any(picks):
             # st.write("Nearest butterflies:")