Update app.py

app.py

@@ -13,47 +13,46 @@ model = tf.keras.models.load_model("dungen_dev_preview_model.keras")
 max_seq_len = 25
 
 def generate_text(seed_text, next_words=30, temperature=0.5):
-    seed_text = seed_text.strip()
-    seed_text = seed_text.lower() + ' | '
-    hate_speech = detect_hate_speech(seed_text)
-    profanity = detect_profanity([seed_text], language='All')
+    seed_text = seed_text.strip().lower()
 
-    if profanity:
+    if "|" in seed_text: # check for | in seed_text
+        gr.Warning("The prompt should not contain the '|' character. Using default prompt.")
+        seed_text = 'game name | '
+    elif detect_profanity([seed_text], language='All'):
         gr.Warning("Profanity detected in the prompt, using the default prompt.")
         seed_text = 'game name | '
-    elif hate_speech and hate_speech[0] in ['Hate Speech', 'Offensive Speech']:
-        gr.Warning('Harmful speech detected in the seed text, using default prompt.')
+    elif detect_hate_speech(seed_text) and detect_hate_speech(seed_text)[0] in ['Hate Speech', 'Offensive Speech']:
+        gr.Warning('Harmful speech detected in the prompt, using default prompt.')
         seed_text = 'game name | '
+    else:
+        seed_text += ' | '
 
     generated_text = seed_text
-    for _ in range(next_words):
-        token_list = sp.encode_as_ids(generated_text)
-        token_list = pad_sequences([token_list], maxlen=max_seq_len - 1, padding='pre')
-        predicted = model.predict(token_list, verbose=0)[0]
+    if generated_text != 'game name | ': # only generate if not the default prompt
+        for _ in range(next_words):
+            token_list = sp.encode_as_ids(generated_text)
+            token_list = pad_sequences([token_list], maxlen=max_seq_len - 1, padding='pre')
+            predicted = model.predict(token_list, verbose=0)[0]
 
-        predicted = np.asarray(predicted).astype("float64")
-        predicted = np.log(predicted + 1e-8) / temperature
-        exp_preds = np.exp(predicted)
-        predicted = exp_preds / np.sum(exp_preds)
+            predicted = np.asarray(predicted).astype("float64")
+            predicted = np.log(predicted + 1e-8) / temperature
+            exp_preds = np.exp(predicted)
+            predicted = exp_preds / np.sum(exp_preds)
 
-        next_index = np.random.choice(len(predicted), p=predicted)
-        next_token = sp.id_to_piece(next_index)
-        generated_text += next_token
+            next_index = np.random.choice(len(predicted), p=predicted)
+            next_token = sp.id_to_piece(next_index)
+            generated_text += next_token
 
-        if next_token.endswith('</s>') or next_token.endswith('<unk>'):
-            break
+            if next_token.endswith('</s>') or next_token.endswith('<unk>'):
+                break
 
     decoded = sp.decode_pieces(sp.encode_as_pieces(generated_text))
     decoded = decoded.replace("</s>", "").replace("<unk>", "").strip()
 
-    # Remove the prompt from the generated text
     if '|' in decoded:
-        decoded = decoded.split('|', 1)[1].strip() #Split at the first occurence of '|' and take the second part
-
-    hate_speech2 = detect_hate_speech(decoded)
-    profanity2 = detect_profanity([decoded], language='All')
+        decoded = decoded.split('|', 1)[1].strip()
 
-    if profanity2 or (hate_speech2 and hate_speech2[0] in ['Hate Speech', 'Offensive Speech']):
+    if detect_profanity([decoded], language='All') or (detect_hate_speech(decoded) and detect_hate_speech(decoded)[0] in ['Hate Speech', 'Offensive Speech']):
         gr.Warning("Flagged potentially harmful output.")
         decoded = 'Flagged Output'
 
@@ -63,7 +62,7 @@ demo = gr.Interface(
     fn=generate_text,
     inputs=[
         gr.Textbox(label="Prompt", value="a female character name", max_lines=1),
-        gr.Slider(1, 50, step=1, label='Next Words', value=30),
+        gr.Slider(1, 100, step=1, label='Next Words', value=30),
         gr.Slider(0.1, 1, value=0.5, label='Temperature', info='Controls randomness of generation, higher values = more creative, lower values = more probalistic')
     ],
     outputs=gr.Textbox(label="Generated Names"),