Countdown timer

app.py

@@ -7,6 +7,7 @@ from thefuzz import process, fuzz
 import numpy as np
 import re
 from string import punctuation
+import time
 
 
 API_URL = "https://api-inference.huggingface.co/models/Dabid/abusive-tagalog-profanity-detection"
@@ -37,29 +38,18 @@ obj_pronouns = read_text('obj_pronouns')
 profanities = read_text('profanities', 'json')
 
 
-# for profanity in profanities:
-#     print(profanity, process.extractOne(profanity, tweet.split(), scorer=fuzz.ratio))
-
 
 def fuzzy_lookup(tweet):
 
     matched_profanity = dict()
-
-    # Convert Profanity Dict to List
     lookup_profanity = np.concatenate([np.hstack(list(profanities.values())), list(profanities.keys())])
 
-    # Loop each word in tweet
     for word in tweet.split():
         scores = []
         matched_words = []
-
-        # Remove punctuations
         word = word.strip(punctuation)
-
-        # Only get digits and letters then lowercase
         processed_word = re.sub("[^a-zA-Z0-9@]", "", word)
-        
-        # If word > 4 chars
+
         if len(processed_word) >= 4:
             # Get fuzzy ratio
             for lookup_word in lookup_words:
@@ -134,6 +124,19 @@ 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):
     
     preprocessed_tweet, matched_profanity = preprocess(tweet)
@@ -142,8 +145,9 @@ def predict(tweet):
 
     if type(prediction) is dict:
         # return "Model is still loading. Try again."
-        print(prediction)
-        return "Model is 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 bool(matched_profanity) == False:
         return "No profanity"