Update app.py

app.py

@@ -1051,6 +1051,222 @@ def generateNames(type, amount, max_length=30, temperature=0.5, seed_text=""):
                     name = stripped
             names.append(name)
         return pd.DataFrame(names, columns=['Names'])
+
+    elif type == "Warframe":
+        max_seq_len = 13 # For skyrim = 13, for terraria = 12
+        sp = spm.SentencePieceProcessor()
+        sp.load("models/warframe_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_warframe_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 == "Call of Duty":
+        max_seq_len = 11 # For skyrim = 13, for terraria = 12
+        sp = spm.SentencePieceProcessor()
+        sp.load("models/call_of_duty_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_call_of_duty_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 == "Forza Horizon":
+        max_seq_len = 10 # For skyrim = 13, for terraria = 12
+        sp = spm.SentencePieceProcessor()
+        sp.load("models/forza_horizon_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_forza_horizon_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 == "Halo":
+        max_seq_len = 14 # For skyrim = 13, for terraria = 12
+        sp = spm.SentencePieceProcessor()
+        sp.load("models/halo_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_halo_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 == "Overwatch":
+        max_seq_len = 9 # For skyrim = 13, for terraria = 12
+        sp = spm.SentencePieceProcessor()
+        sp.load("models/overwatch_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_overwatch_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 == "Subnautica":
+        max_seq_len = 14 # For skyrim = 13, for terraria = 12
+        sp = spm.SentencePieceProcessor()
+        sp.load("models/subnautica_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_subnautica_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
@@ -1090,7 +1306,7 @@ 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", "Final Fantasy", "Elden Ring", "Zelda", "Dragon Age", "Fallout", "Darkest Dungeon", "Monster Hunter", "Bloodborne", "Hollow Knight", "Assassin's Creed", "Baldur's Gate", "Cyberpunk", "Mass Effect", "God Of War", "Last Of Us", "Factorio", "The Sims", "Fortnite", "League Of Legends", "Among Us", "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", "Cyberpunk", "Mass Effect", "God Of War", "Last Of Us", "Factorio", "The Sims", "Fortnite", "League Of Legends", "Among Us", "Warframe", "Call of Duty", "Forza Horizon", "Halo", "Overwatch", "Subnautica" "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, 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).'