Loading time

app.py

@@ -37,7 +37,7 @@ lookup_words = read_text('lookup_words')
 obj_pronouns = read_text('obj_pronouns')
 profanities = read_text('profanities', 'json')
 
-
+loading_countdown = 0
 
 def fuzzy_lookup(tweet):
 
@@ -84,16 +84,12 @@ def fuzzy_lookup(tweet):
 
 def preprocess(tweet):
     
-    # Lowercase
     tweet = tweet.lower()
-
-    # Remove emojis
     tweet = emoji.replace_emoji(tweet, replace='')
 
-    # Replace elongated words 'grabeee' -> 'grabe' (not applicable on 2 corresponding letter)
+    # Elongated words conversion
     tweet = re.sub(r'(.)\1{2,}', r'\1', tweet)
 
-    # Split sentence into list of words
     row_split = tweet.split()
 
     for index, word in enumerate(row_split):
@@ -113,7 +109,6 @@ def preprocess(tweet):
     # Fuzzy Lookup
     preprocessed_tweet, matches = fuzzy_lookup(preprocessed_tweet)
 
-    # Check if output contains single word then return null
     if len(preprocessed_tweet.split()) == 1:
         return preprocessed_tweet, matches
 
@@ -124,33 +119,22 @@ def preprocess(tweet):
     return preprocessed_tweet, matches 
 
 
-def countdown(seconds):
-    start_time = time.time()
-    while True:
-        elapsed_time = int(time.time() - start_time)
-        remaining_time = max(seconds - elapsed_time, 0)
-        if remaining_time == 0:
-            print("Time's up!")
-            break
-        print(remaining_time)
-        yield remaining_time
-        time.sleep(1)
-
 
 def predict(tweet):
+    global loading_countdown 
     
     preprocessed_tweet, matched_profanity = preprocess(tweet)
 
     prediction = query(preprocessed_tweet)
 
-    if type(prediction) is dict:
-        # return "Model is still loading. Try again."
-        print(prediction['estimated_time'])
-        cd = countdown(prediction['estimated_time']) # TODO build a countdown
-        return f"Model is loading. Try again after {next(cd)} seconds."
+    if type(prediction) == dict: 
+        loading_time = prediction['estimated_time']
+        return f"Loading Model (Estimated Time: {loading_time} Seconds)"
+
     
     if bool(matched_profanity) == False:
-        return "No profanity"
+        return "No Profanity"
+        
     
     prediction = [tuple(i.values()) for i in prediction[0]]
     prediction = dict((x, y) for x, y in prediction)
@@ -161,6 +145,7 @@ def predict(tweet):
 
     return prediction
 
+
 demo = gr.Interface(
     fn=predict,