Update app.py

app.py

@@ -1,9 +1,91 @@
+# 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
+import csv
+from datetime import datetime
 
 sp = spm.SentencePieceProcessor()
 sp.Load("dungen_dev_preview.model")
@@ -28,7 +110,7 @@ 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')
@@ -58,16 +140,62 @@ def generate_text(seed_text, next_words=30, temperature=0.5):
 
     return decoded
 
+flagged_outputs = []
+
+def flag_output(prompt, generated_text, next_words, temperature):
+    if not generated_text.strip():
+        return "Cannot flag an empty output."
+    
+    timestamp = datetime.now().isoformat()
+    flagged_outputs.append({
+        "Prompt": prompt,
+        "Generated Text": generated_text,
+        "Next Words": next_words,
+        "Temperature": temperature,
+        "Timestamp": timestamp
+    })
+
+    with open("flagged_outputs.csv", "a", newline="") as file:
+        writer = csv.DictWriter(file, fieldnames=["Prompt", "Generated Text", "Next Words", "Temperature", "Timestamp"])
+        if file.tell() == 0:
+            writer.writeheader()
+        writer.writerow({
+            "Prompt": prompt,
+            "Generated Text": generated_text,
+            "Next Words": next_words,
+            "Temperature": temperature,
+            "Timestamp": timestamp
+        })
+
+    return "Output flagged successfully."
+
+def push_flagged_to_dataset():
+    import datasets
+    dataset = datasets.load_dataset("InfinitodeLTD/DungenDev-FlaggedOutputs", split="train")
+    dataset = dataset.add_item(flagged_outputs)
+    dataset.push_to_hub()
+    flagged_outputs.clear()
+    return "Flagged outputs pushed to public dataset."
+
+def display_disclaimer():
+    return ('Dungen Dev is an experimental model and may produce outputs that are inappropriate, biased, or potentially harmful and inaccurate. '
+            'Caution is advised.')
+
 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')
+        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 Names"),
+        gr.Button("Flag Output", elem_id="flag_button"),
+        gr.HTML(display_disclaimer())
     ],
-    outputs=gr.Textbox(label="Generated Names"),
+    live=True,
     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.',
+    description=None,
     examples=[
         ["a male character name", 30, 0.5],
         ["a futuristic city name", 30, 0.5],