seems generate may be blocking? unclear

app.py

@@ -25,7 +25,9 @@ from sklearn.svm import SVC
 from sklearn.inspection import permutation_importance
 from sklearn import preprocessing
 import pandas as pd
-from apscheduler.schedulers.background import BackgroundScheduler
+#from apscheduler.schedulers.background import BackgroundScheduler
+import sched
+import threading
 
 import random
 import time
@@ -223,57 +225,58 @@ def pluck_img(user_id, user_emb):
 
 def background_next_image():
     global prevs_df
-    
-    # only let it get N (maybe 3) ahead of the user
-    #not_rated_rows = prevs_df[[i[1]['user:rating'] == {' ': ' '} for i in prevs_df.iterrows()]]
-    rated_rows = prevs_df[[i[1]['user:rating'] != {' ': ' '} for i in prevs_df.iterrows()]]
-    while len(rated_rows) < 4:
-    #    not_rated_rows = prevs_df[[i[1]['user:rating'] == {' ': ' '} for i in prevs_df.iterrows()]]
-        rated_rows = prevs_df[[i[1]['user:rating'] != {' ': ' '} for i in prevs_df.iterrows()]]
-        time.sleep(.01)
-        print('all users have 4 or less rows rated')
-
-    user_id_list = set(rated_rows['latest_user_to_rate'].to_list())
-    for uid in user_id_list:
-        rated_rows = prevs_df[[i[1]['user:rating'].get(uid, None) is not None for i in prevs_df.iterrows()]]
-        not_rated_rows = prevs_df[[i[1]['user:rating'].get(uid, None) is None for i in prevs_df.iterrows()]]
-        
-        # we need to intersect not_rated_rows from this user's embed > 7. Just add a new column on which user_id spawned the 
-        #   media. 
-        
-        from_user = prevs_df[[i[1]['from_user_id'] == uid for i in prevs_df.iterrows()]]
-        if len(from_user) >= 10:
-            oldest = from_user.iloc[-1]['paths']
-            print(f'User has {len(from_user)} rows. Popping oldest: {oldest}')
-            prevs_df = prevs_df[prevs_df['paths'] != oldest]
-        
-        if len(rated_rows) < 4:
-            print(f'latest user {uid} has < 4 rows') # or > 7 unrated rows')
-            continue
-        
-        print(uid)
-        embs, ys = pluck_embs_ys(uid)
+    while True:
         
-        user_emb = get_user_emb(embs, ys)
-        img, embs = generate(user_emb)
-        print(img)
-        if img:
-            tmp_df = pd.DataFrame(columns=['paths', 'embeddings', 'ips', 'user:rating', 'latest_user_to_rate'])
-            tmp_df['paths'] = [img]
-            tmp_df['embeddings'] = [embs]
-            tmp_df['user:rating'] = [{' ': ' '}]
-            tmp_df['from_user_id'] = [uid]
-            prevs_df = pd.concat((prevs_df, tmp_df))
-            # we can free up storage by deleting the image
-            if len(prevs_df) > 50:
-                oldest_path = prevs_df.iloc[6]['paths']
-                if os.path.isfile(oldest_path):
-                    os.remove(oldest_path)
-                else:
-                    # If it fails, inform the user.
-                    print("Error: %s file not found" % oldest_path)
-                # only keep 50 images & embeddings & ips, then remove oldest besides calibrating
-                prevs_df = pd.concat((prevs_df.iloc[:6], prevs_df.iloc[7:]))
+        # only let it get N (maybe 3) ahead of the user
+        #not_rated_rows = prevs_df[[i[1]['user:rating'] == {' ': ' '} for i in prevs_df.iterrows()]]
+        rated_rows = prevs_df[[i[1]['user:rating'] != {' ': ' '} for i in prevs_df.iterrows()]]
+        while len(rated_rows) < 4:
+        #    not_rated_rows = prevs_df[[i[1]['user:rating'] == {' ': ' '} for i in prevs_df.iterrows()]]
+            rated_rows = prevs_df[[i[1]['user:rating'] != {' ': ' '} for i in prevs_df.iterrows()]]
+            time.sleep(.01)
+            print('all users have 4 or less rows rated')
+
+        user_id_list = set(rated_rows['latest_user_to_rate'].to_list())
+        for uid in user_id_list:
+            rated_rows = prevs_df[[i[1]['user:rating'].get(uid, None) is not None for i in prevs_df.iterrows()]]
+            not_rated_rows = prevs_df[[i[1]['user:rating'].get(uid, None) is None for i in prevs_df.iterrows()]]
+            
+            # we need to intersect not_rated_rows from this user's embed > 7. Just add a new column on which user_id spawned the 
+            #   media. 
+            
+            from_user = prevs_df[[i[1]['from_user_id'] == uid for i in prevs_df.iterrows()]]
+            if len(from_user) >= 10:
+                oldest = from_user.iloc[-1]['paths']
+                print(f'User has {len(from_user)} rows. Popping oldest: {oldest}')
+                prevs_df = prevs_df[prevs_df['paths'] != oldest]
+            
+            if len(rated_rows) < 4:
+                print(f'latest user {uid} has < 4 rows') # or > 7 unrated rows')
+                continue
+            
+            print(uid)
+            embs, ys = pluck_embs_ys(uid)
+            
+            user_emb = get_user_emb(embs, ys)
+            img, embs = generate(user_emb)
+            print(img)
+            if img:
+                tmp_df = pd.DataFrame(columns=['paths', 'embeddings', 'ips', 'user:rating', 'latest_user_to_rate'])
+                tmp_df['paths'] = [img]
+                tmp_df['embeddings'] = [embs]
+                tmp_df['user:rating'] = [{' ': ' '}]
+                tmp_df['from_user_id'] = [uid]
+                prevs_df = pd.concat((prevs_df, tmp_df))
+                # we can free up storage by deleting the image
+                if len(prevs_df) > 50:
+                    oldest_path = prevs_df.iloc[6]['paths']
+                    if os.path.isfile(oldest_path):
+                        os.remove(oldest_path)
+                    else:
+                        # If it fails, inform the user.
+                        print("Error: %s file not found" % oldest_path)
+                    # only keep 50 images & embeddings & ips, then remove oldest besides calibrating
+                    prevs_df = pd.concat((prevs_df.iloc[:6], prevs_df.iloc[7:]))
     
 
 def pluck_embs_ys(user_id):
@@ -475,9 +478,12 @@ Explore the latent space without text prompts based on your preferences. Learn m
 log = logging.getLogger('log_here')
 log.setLevel(logging.ERROR)
 
-scheduler = BackgroundScheduler()
-scheduler.add_job(func=background_next_image, trigger="interval", seconds=.1)
-scheduler.start()
+#scheduler = BackgroundScheduler()
+#scheduler.add_job(func=background_next_image, trigger="interval", seconds=.1)
+#scheduler.start()
+
+thread = threading.Thread(target=background_next_image,)
+thread.start()
 
 def encode_space(x):
     im_emb, _ = pipe.encode_image(