Create app.py

app.py

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+import sentencepiece as spm
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+import re
+from valx import detect_profanity, detect_hate_speech
+import gradio as gr
+
+sp = spm.SentencePieceProcessor()
+sp.Load("dungen_dev_preview.model")
+
+model = tf.keras.models.load_model("dungen_dev_preview_model.keras")
+
+max_seq_len = 25
+
+def generate_text(seed_text, next_words=5, temperature=1.0):
+    seed_text = seed_text.lowercase() + ' | '
+    hate_speech = detect_hate_speech(seed_text)
+    profanity = detect_profanity([seed_text], language='All')
+    
+    if len(profanity) > 0:
+        gr.Warning("Profanity detected in the prompt, using the default prompt.")
+        seed_text = 'game name | '
+    else:
+        if hate_speech == ['Hate Speech']:
+            gr.Warning('Hate speech detected in the seed text, using an empty seed text.')
+            seed_text = 'game name | '
+        elif hate_speech == ['Offensive Speech']:
+            gr.Warning('Offensive speech detected in the seed text, using an empty seed text.')
+            seed_text = 'game name | '
+            
+    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]
+
+        # Apply temperature
+        predicted = np.asarray(predicted).astype("float64")
+        predicted = np.log(predicted) / 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
+
+        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>", "")
+    decoded = decoded.replace("<unk>", "")
+    cleaned_text = decoded.strip()
+
+    hate_speech2 = detect_hate_speech(cleaned_text)
+    profanity2 = detect_profanity([cleaned_text], language='All')
+    
+    if len(profanity2) > 0:
+        gr.Warning("Flagged potentially harmful output.")
+        cleaned_text = 'Flagged Output'
+    else:
+        if hate_speech2 == ['Hate Speech']:
+            gr.Warning('Flagged potentially harmful output.')
+            cleaned_text = 'Flagged Output'
+        elif hate_speech2 == ['Offensive Speech']:
+            gr.Warning('Flagged potentially harmful output.')
+            cleaned_text = 'Flagged Output'
+    
+    return cleaned_text
+
+demo = gr.Interface(
+    fn=generate_text,
+    inputs=[label="Prompt", value="a female character name", max_lines=1), 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.Dataframe(row_count = (2, "dynamic"), col_count=(1, "fixed"), label="Generated Names", headers=["Names"])],
+    title='Dungen Dev - Name Generator',
+    description='A prompt-based name generator for game developers.'
+)
+
+demo.launch()