Update app.py

app.py

@@ -38,6 +38,24 @@ def generate_random_name(interpreter, vocab_size, sp, max_length=10, temperature
     decoded_name = ''
 
     if seed_text:
+            hate_speech = detect_hate_speech(seed_text)
+            profanity = detect_profanity([seed_text], language='All')
+            output = ''
+    
+            if profanity > 0:
+                gr.Warning("Profanity Detected in the seed text, using an empty seed text.")
+                seed_text = ''
+            else:
+                if hate_speech == ['Hate Speech']:
+                    gr.Warning('Hate Speech Detected in the seed text, using an empty seed text.')
+                    seed_text = ''
+                elif hate_speech == ['Offensive Speech']:
+                    gr.Warning('Offensive Speech Detected in the seed text, using an empty seed text.')
+                    seed_text = ''
+                # elif hate_speech == ['No Hate and Offensive Speech']:
+                
+            names.append(name)
+        return pd.DataFrame(names, columns=['Names'])
         generated_name = seed_text
     else:
         random_index = np.random.randint(1, vocab_size)
@@ -132,7 +150,7 @@ def generateNames(type, amount, max_length=30, temperature=0.5, seed_text=""):
                 elif hate_speech == ['Offensive Speech']:
                     name = 'Offensive Speech Detected'
                 elif hate_speech == ['No Hate and Offensive Speech']:
-                    name = generated_name
+                    name = stripped
             names.append(name)
         return pd.DataFrame(names, columns=['Names'])
 
@@ -155,7 +173,21 @@ def generateNames(type, amount, max_length=30, temperature=0.5, seed_text=""):
         # Use the function to generate a name
         for _ in range(amount):
             generated_name = generate_random_name(interpreter, vocab_size, sp, seed_text=seed_text, max_length=max_length, temperature=temperature, max_seq_len=max_seq_len)
-            names.append(generated_name)
+            stripped = generated_name.strip()
+            hate_speech = detect_hate_speech(stripped)
+            profanity = detect_profanity([stripped], language='All')
+            name = ''
+    
+            if profanity > 0:
+                name = "Profanity Detected"
+            else:
+                if hate_speech == ['Hate Speech']:
+                    name = 'Hate Speech Detected'
+                elif hate_speech == ['Offensive Speech']:
+                    name = 'Offensive Speech Detected'
+                elif hate_speech == ['No Hate and Offensive Speech']:
+                    name = stripped
+            names.append(name)
         return pd.DataFrame(names, columns=['Names'])
 
 demo = gr.Interface(