Update app.py

app.py

@@ -332,6 +332,402 @@ def generateNames(type, amount, max_length=30, temperature=0.5, seed_text=""):
             names.append(name)
         return pd.DataFrame(names, columns=['Names'])
 
+    elif type == "Final Fantasy":
+        max_seq_len = 14
+        sp = spm.SentencePieceProcessor()
+        sp.load("models/final_fantasy_names.model")
+        amount = int(amount)
+        max_length = int(max_length)
+
+        names = []
+
+        # Define necessary variables
+        vocab_size = sp.GetPieceSize()
+
+        # Load TFLite model
+        interpreter = tf.lite.Interpreter(model_path="models/dungen_final_fantasy_model.tflite")
+        interpreter.allocate_tensors()
+
+        # 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)
+            stripped = generated_name.strip()
+            hate_speech = detect_hate_speech(stripped)
+            profanity = detect_profanity([stripped], language='All')
+            name = ''
+    
+            if len(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'])
+
+    elif type == "Elden Ring":
+        max_seq_len = 18
+        sp = spm.SentencePieceProcessor()
+        sp.load("models/elden_ring_names.model")
+        amount = int(amount)
+        max_length = int(max_length)
+
+        names = []
+
+        # Define necessary variables
+        vocab_size = sp.GetPieceSize()
+
+        # Load TFLite model
+        interpreter = tf.lite.Interpreter(model_path="models/dungen_elden_ring_model.tflite")
+        interpreter.allocate_tensors()
+
+        # 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)
+            stripped = generated_name.strip()
+            hate_speech = detect_hate_speech(stripped)
+            profanity = detect_profanity([stripped], language='All')
+            name = ''
+    
+            if len(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'])
+
+    elif type == "Zelda":
+        max_seq_len = 15
+        sp = spm.SentencePieceProcessor()
+        sp.load("models/zelda_names.model")
+        amount = int(amount)
+        max_length = int(max_length)
+
+        names = []
+
+        # Define necessary variables
+        vocab_size = sp.GetPieceSize()
+
+        # Load TFLite model
+        interpreter = tf.lite.Interpreter(model_path="models/dungen_zelda_model.tflite")
+        interpreter.allocate_tensors()
+
+        # 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)
+            stripped = generated_name.strip()
+            hate_speech = detect_hate_speech(stripped)
+            profanity = detect_profanity([stripped], language='All')
+            name = ''
+    
+            if len(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'])
+
+    elif type == "Dragon Age":
+        max_seq_len = 16 # For skyrim = 13, for terraria = 12
+        sp = spm.SentencePieceProcessor()
+        sp.load("models/dragon_age_names.model")
+        amount = int(amount)
+        max_length = int(max_length)
+
+        names = []
+
+        # Define necessary variables
+        vocab_size = sp.GetPieceSize()
+
+        # Load TFLite model
+        interpreter = tf.lite.Interpreter(model_path="models/dungen_dragon_age_model.tflite")
+        interpreter.allocate_tensors()
+
+        # 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)
+            stripped = generated_name.strip()
+            hate_speech = detect_hate_speech(stripped)
+            profanity = detect_profanity([stripped], language='All')
+            name = ''
+    
+            if len(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'])
+
+    elif type == "Fallout":
+        max_seq_len = 13
+        sp = spm.SentencePieceProcessor()
+        sp.load("models/fallout_names.model")
+        amount = int(amount)
+        max_length = int(max_length)
+
+        names = []
+
+        # Define necessary variables
+        vocab_size = sp.GetPieceSize()
+
+        # Load TFLite model
+        interpreter = tf.lite.Interpreter(model_path="models/dungen_fallout_model.tflite")
+        interpreter.allocate_tensors()
+
+        # 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)
+            stripped = generated_name.strip()
+            hate_speech = detect_hate_speech(stripped)
+            profanity = detect_profanity([stripped], language='All')
+            name = ''
+    
+            if len(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'])
+
+    elif type == "Darkest Dungeon":
+        max_seq_len = 14
+        sp = spm.SentencePieceProcessor()
+        sp.load("models/darkest_dungeon_names.model")
+        amount = int(amount)
+        max_length = int(max_length)
+
+        names = []
+
+        # Define necessary variables
+        vocab_size = sp.GetPieceSize()
+
+        # Load TFLite model
+        interpreter = tf.lite.Interpreter(model_path="models/dungen_darkest_dungeon_model.tflite")
+        interpreter.allocate_tensors()
+
+        # 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)
+            stripped = generated_name.strip()
+            hate_speech = detect_hate_speech(stripped)
+            profanity = detect_profanity([stripped], language='All')
+            name = ''
+    
+            if len(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'])
+
+    elif type == "Monster Hunter":
+        max_seq_len = 15
+        sp = spm.SentencePieceProcessor()
+        sp.load("models/monster_hunter_names.model")
+        amount = int(amount)
+        max_length = int(max_length)
+
+        names = []
+
+        # Define necessary variables
+        vocab_size = sp.GetPieceSize()
+
+        # Load TFLite model
+        interpreter = tf.lite.Interpreter(model_path="models/dungen_monster_hunter_model.tflite")
+        interpreter.allocate_tensors()
+
+        # 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)
+            stripped = generated_name.strip()
+            hate_speech = detect_hate_speech(stripped)
+            profanity = detect_profanity([stripped], language='All')
+            name = ''
+    
+            if len(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'])
+
+    elif type == "Bloodborne":
+        max_seq_len = 12
+        sp = spm.SentencePieceProcessor()
+        sp.load("models/bloodborne_names.model")
+        amount = int(amount)
+        max_length = int(max_length)
+
+        names = []
+
+        # Define necessary variables
+        vocab_size = sp.GetPieceSize()
+
+        # Load TFLite model
+        interpreter = tf.lite.Interpreter(model_path="models/dungen_bloodborne_model.tflite")
+        interpreter.allocate_tensors()
+
+        # 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)
+            stripped = generated_name.strip()
+            hate_speech = detect_hate_speech(stripped)
+            profanity = detect_profanity([stripped], language='All')
+            name = ''
+    
+            if len(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'])
+
+    elif type == "Hollow Knight":
+        max_seq_len = 15
+        sp = spm.SentencePieceProcessor()
+        sp.load("models/hollow_knight_names.model")
+        amount = int(amount)
+        max_length = int(max_length)
+
+        names = []
+
+        # Define necessary variables
+        vocab_size = sp.GetPieceSize()
+
+        # Load TFLite model
+        interpreter = tf.lite.Interpreter(model_path="models/dungen_hollow_knight_model.tflite")
+        interpreter.allocate_tensors()
+
+        # 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)
+            stripped = generated_name.strip()
+            hate_speech = detect_hate_speech(stripped)
+            profanity = detect_profanity([stripped], language='All')
+            name = ''
+    
+            if len(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'])
+
+    elif type == "Assassin's Creed":
+        max_seq_len = 15
+        sp = spm.SentencePieceProcessor()
+        sp.load("models/dark_souls_names.model")
+        amount = int(amount)
+        max_length = int(max_length)
+
+        names = []
+
+        # Define necessary variables
+        vocab_size = sp.GetPieceSize()
+
+        # Load TFLite model
+        interpreter = tf.lite.Interpreter(model_path="models/dungen_assassins_creed_model.tflite")
+        interpreter.allocate_tensors()
+
+        # 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)
+            stripped = generated_name.strip()
+            hate_speech = detect_hate_speech(stripped)
+            profanity = detect_profanity([stripped], language='All')
+            name = ''
+    
+            if len(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'])
+
+    elif type == "Baldur's Gate":
+        max_seq_len = 14
+        sp = spm.SentencePieceProcessor()
+        sp.load("models/baldurs_gate_names.model")
+        amount = int(amount)
+        max_length = int(max_length)
+
+        names = []
+
+        # Define necessary variables
+        vocab_size = sp.GetPieceSize()
+
+        # Load TFLite model
+        interpreter = tf.lite.Interpreter(model_path="models/dungen_baldurs_gate_model.tflite")
+        interpreter.allocate_tensors()
+
+        # 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)
+            stripped = generated_name.strip()
+            hate_speech = detect_hate_speech(stripped)
+            profanity = detect_profanity([stripped], language='All')
+            name = ''
+    
+            if len(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'])
+
     elif type == "Fantasy":
         max_seq_len = 16 # For fantasy, 16
         sp = spm.SentencePieceProcessor()
@@ -370,10 +766,10 @@ def generateNames(type, amount, max_length=30, temperature=0.5, seed_text=""):
 
 demo = gr.Interface(
     fn=generateNames,
-    inputs=[gr.Radio(choices=["Terraria", "Skyrim", "Witcher", "WOW", "Minecraft", "Dark Souls", "Fantasy"], label="Choose a model for your request", value="Terraria"), gr.Slider(1,100, step=1, label='Amount of Names', info='How many names to generate, must be greater than 0'), gr.Slider(10, 60, value=30, step=1, label='Max Length', info='Max length of the generated word'), gr.Slider(0.1, 1, value=0.5, label='Temperature', info='Controls randomness of generation, higher values = more creative, lower values = more probalistic'), gr.Textbox('', label='Seed text (optional)', info='The starting text to begin with', max_lines=1, )],
+    inputs=[gr.Radio(choices=["Terraria", "Skyrim", "Witcher", "WOW", "Minecraft", "Dark Souls", "Final Fantasy", "Elden Ring", "Zelda", "Dragon Age", "Fallout", "Darkest Dungeon", "Monster Hunter", "Bloodborne", "Hollow Knight", "Assassin's Creed", "Baldur's Gate", "Fantasy"], label="Choose a model for your request", value="Terraria"), gr.Slider(1,100, step=1, label='Amount of Names', info='How many names to generate, must be greater than 0'), gr.Slider(10, 60, value=30, step=1, label='Max Length', info='Max length of the generated word'), gr.Slider(0.1, 1, value=0.5, label='Temperature', info='Controls randomness of generation, higher values = more creative, lower values = more probalistic'), gr.Textbox('', label='Seed text (optional)', info='The starting text to begin with', max_lines=1, )],
     outputs=[gr.Dataframe(row_count = (2, "dynamic"), col_count=(1, "fixed"), label="Generated Names", headers=["Names"])],
     title='Dungen - Name Generator',
-    description='A fun game-inspired name generator. For an example of how to create, and train your model, similar to this one, head over to: https://github.com/Infinitode/OPEN-ARC/tree/main/Project-5-TWNG. There you will find our base model, the dataset we used, and implementation code in the form of a Jupyter Notebook (exported from Kaggle).'
+    description='A fun game-inspired name generator. For an example of how to create, and train your model, like this one, head over to: https://github.com/Infinitode/OPEN-ARC/tree/main/Project-5-TWNG. There you will find our base model, the dataset we used, and implementation code in the form of a Jupyter Notebook (exported from Kaggle).'
 )
 
 demo.launch()