Testing logging

app.py

@@ -1,17 +1,110 @@
+# 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 logging
+import csv
+import os
+from datetime import datetime
 
+# Model and SentencePiece loading
 sp = spm.SentencePieceProcessor()
 sp.Load("dungen_dev_preview.model")
-
 model = tf.keras.models.load_model("dungen_dev_preview_model.keras")
-
 max_seq_len = 25
 
+# Logging setup
+CSV_FILE = "flagged_outputs.csv"
+if not os.path.exists(CSV_FILE):
+    with open(CSV_FILE, "w", newline="", encoding="utf-8") as csvfile: #added encoding
+        writer = csv.writer(csvfile)
+        writer.writerow(["Timestamp", "Flagged Text"])
+
+logging.basicConfig(filename="app.log", level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+
 def generate_text(seed_text, next_words=30, temperature=0.5):
     seed_text = seed_text.strip().lower()
 
@@ -28,7 +121,7 @@ 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 | ':
         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')
@@ -58,23 +151,44 @@ def generate_text(seed_text, next_words=30, temperature=0.5):
 
     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=[
+def flag_output(text):
+    logging.info(f"Output flagged: {text}")
+    timestamp = datetime.now().isoformat()
+    with open(CSV_FILE, "a", newline="", encoding="utf-8") as csvfile: #added encoding
+        writer = csv.writer(csvfile)
+        writer.writerow([timestamp, text])
+    return "Output Flagged. Thank you for your feedback."
+
+with gr.Blocks() as demo:
+    gr.Markdown("""# Dungen Dev - Name Generator
+    A prompt-based name generator for game developers.""")
+
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label="Prompt", value="a female character name", max_lines=1)
+            with gr.Row():
+                next_words_slider = gr.Slider(1, 100, step=1, label='Next Words', value=30)
+                temperature_slider = gr.Slider(0.1, 1, value=0.5, label='Temperature', info='Controls randomness of generation, higher values = more creative, lower values = more probalistic')
+            generate_button = gr.Button("Generate")
+        with gr.Column():
+            output_text = gr.Textbox(label="Generated Names", interactive=False)
+            flag_button = gr.Button("Flag Output")
+
+    gr.Markdown("""Dungen Dev is an experimental model, and may produce outputs that are inappropriate, biased, or potentially harmful and inaccurate. Caution is advised.""")
+
+    generate_button.click(
+        fn=generate_text,
+        inputs=[prompt, next_words_slider, temperature_slider],
+        outputs=output_text
+    )
+    flag_button.click(flag_output, inputs=output_text, outputs=gr.Textbox(label="Flag Status", interactive=False))
+
+    demo.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()