Removed flagging

app.py

@@ -1,93 +1,9 @@
-# import sentencepiece as spm
-# import numpy as np
-# import tensorflow as tf
-# from tensorflow.keras.preprocessing.sequence import pad_sequences
-# 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=30, temperature=0.5):
-#     seed_text = seed_text.strip().lower()
-
-#     if "|" 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_result := detect_hate_speech(seed_text)) and hate_speech_result[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
-#     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)
-
-#             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>", "").replace("<unk>", "").strip()
-
-#     if '|' in decoded:
-#         decoded = decoded.split('|', 1)[1].strip()
-
-#     if any(detect_profanity([decoded], language='All')) or (hate_speech_result := detect_hate_speech(decoded)) and hate_speech_result[0] in ['Hate Speech', 'Offensive Speech']:
-#         gr.Warning("Flagged potentially harmful output.")
-#         decoded = 'Flagged Output'
-
-#     return decoded
-
-# demo = gr.Interface(
-#     fn=generate_text,
-#     inputs=[
-#         gr.Textbox(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.Textbox(label="Generated Names"),
-#     title='Dungen Dev - Name Generator',
-#     description='A prompt-based name generator for game developers. Dungen Dev is an experimental model, and may produce outputs that are inappropriate, biased, or potentially harmful and inaccurate. Caution is advised.',
-#     examples=[
-#         ["a male character name", 30, 0.5],
-#         ["a futuristic city name", 30, 0.5],
-#         ["an item name", 30, 0.5],
-#         ["a dark and mysterious forest name", 30, 0.5],
-#         ["an evil character name", 30, 0.5]
-#     ]
-# )
-
-# demo.launch()
-
 import sentencepiece as spm
 import numpy as np
 import tensorflow as tf
 from tensorflow.keras.preprocessing.sequence import pad_sequences
 from valx import detect_profanity, detect_hate_speech
 import gradio as gr
-from datasets import load_dataset, DatasetDict, Dataset
-from huggingface_hub import HfApi
-from datetime import datetime
 
 sp = spm.SentencePieceProcessor()
 sp.Load("dungen_dev_preview.model")
@@ -112,7 +28,7 @@ def generate_text(seed_text, next_words=30, temperature=0.5):
         seed_text += ' | '
 
     generated_text = seed_text
-    if generated_text != 'game name | ':  # only generate if not the default prompt
+    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')
@@ -140,57 +56,16 @@ def generate_text(seed_text, next_words=30, temperature=0.5):
         gr.Warning("Flagged potentially harmful output.")
         decoded = 'Flagged Output'
 
-    return decoded #, gr.update(interactive=True)
-
-# Custom flagging callback to use the existing flagging logic
-class CustomFlaggingCallback(gr.FlaggingCallback):
-    def __init__(self, dataset_id):
-        self.dataset_id = dataset_id
-
-    def flag(self, flag_data, flag_option=None, username=None):
-        prompt, generated_text, next_words, temperature = flag_data
-        timestamp = datetime.now().isoformat()
-        
-        # Custom flagging logic
-        try:
-            dataset = load_dataset(self.dataset_id)
-        except:
-            dataset = DatasetDict()
-
-        new_data = [{
-            "Prompt": prompt,
-            "Generated Text": generated_text,
-            "Next Words": next_words,
-            "Temperature": temperature,
-            "Timestamp": timestamp
-        }]
-
-        new_dataset = Dataset.from_list(new_data)
-
-        if "train" in dataset:
-            dataset["train"] = concatenate_datasets([dataset["train"], new_dataset])  # Append to existing train
-        else:
-            dataset["train"] = new_dataset  # Create the train split
-
-        dataset.push_to_hub(self.dataset_id)
-        return "Output flagged successfully."
-
-# Initialize the custom flagging callback
-dataset_id = "InfinitodeLTD/DungenDev-FlaggedOutputs"
-custom_flag_callback = CustomFlaggingCallback(dataset_id)
+    return decoded
 
 demo = gr.Interface(
     fn=generate_text,
     inputs=[
         gr.Textbox(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 probabilistic')
-    ],
-    outputs=[
-        gr.Textbox(label="Generated Name", interactive=True),
-        # gr.Button("Flag Output", interactive=False, elem_id="flag-button")
+        gr.Slider(0.1, 1, value=0.5, label='Temperature', info='Controls randomness of generation, higher values = more creative, lower values = more probalistic')
     ],
-    live=False,
+    outputs=gr.Textbox(label="Generated Names"),
     title='Dungen Dev - Name Generator',
     description='A prompt-based name generator for game developers. Dungen Dev is an experimental model, and may produce outputs that are inappropriate, biased, or potentially harmful and inaccurate. Caution is advised.',
     examples=[
@@ -199,11 +74,7 @@ demo = gr.Interface(
         ["an item name", 30, 0.5],
         ["a dark and mysterious forest name", 30, 0.5],
         ["an evil character name", 30, 0.5]
-    ],
-    theme="default",
-    flagging_mode="manual",
-    flagging_callback=custom_flag_callback
+    ]
 )
 
-demo.queue()
 demo.launch()