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app.py

@@ -223,63 +223,66 @@ def pluck_img(user_id, user_emb):
 
 def background_next_image():
     global prevs_df
-    while True:
-        # 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. 
-            
-            unrated_from_user = not_rated_rows[[i[1]['from_user_id'] == uid for i in not_rated_rows.iterrows()]]
-            rated_from_user = rated_rows[[i[1]['from_user_id'] == uid for i in rated_rows.iterrows()]]
-
-            # we pop previous ratings if there are > 10
-            if len(rated_from_user) >= 10:
-                oldest = rated_from_user.iloc[0]['paths']
-                prevs_df = prevs_df[prevs_df['paths'] != oldest]
-                print('popping 10th rated')
-            # we don't compute more after 10 are in the queue for them
-            if len(unrated_from_user) >= 10:
-                print('skipping because >=10 in queue unrated for user')
-                continue
-            
-            if len(rated_from_user) < 4:
-                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:]))
+    global is_started
+    while not is_started:
+        time.time(.3)
+
+    # 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. 
+        
+        unrated_from_user = not_rated_rows[[i[1]['from_user_id'] == uid for i in not_rated_rows.iterrows()]]
+        rated_from_user = rated_rows[[i[1]['from_user_id'] == uid for i in rated_rows.iterrows()]]
+
+        # we pop previous ratings if there are > 10
+        if len(rated_from_user) >= 10:
+            oldest = rated_from_user.iloc[0]['paths']
+            prevs_df = prevs_df[prevs_df['paths'] != oldest]
+            print('popping 10th rated')
+        # we don't compute more after 10 are in the queue for them
+        if len(unrated_from_user) >= 10:
+            print('skipping because >=10 in queue unrated for user')
+            continue
+        
+        if len(rated_from_user) < 4:
+            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):