Update app.py

app.py

@@ -1,92 +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
-# import csv
-# from datetime import datetime
 
 # sp = spm.SentencePieceProcessor()
 # sp.Load("dungen_dev_preview.model")
@@ -111,7 +28,7 @@ demo.launch()
 #         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')
@@ -139,52 +56,16 @@ demo.launch()
 #         gr.Warning("Flagged potentially harmful output.")
 #         decoded = 'Flagged Output'
 
-#     return decoded, gr.update(interactive=True)
-
-# 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 enable_flag_button(state):
-#     return gr.update(interactive=state)
+#     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')
+#         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 Name", interactive=True),
-#         gr.Button("Flag Output", interactive=False)
-#     ],
-#     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=[
@@ -193,10 +74,137 @@ demo.launch()
 #         ["an item name", 30, 0.5],
 #         ["a dark and mysterious forest name", 30, 0.5],
 #         ["an evil character name", 30, 0.5]
-#     ],
-#     theme="default",
-#     allow_flagging=False
+#     ]
 # )
 
-# demo.queue()
-# demo.launch()
+# 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")
+
+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, gr.update(interactive=True)
+
+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()
+
+    dataset_id = "InfinitodeLTD/DungenDev-FlaggedOutputs"
+    
+    # Load the existing dataset (if it exists) or create an empty DatasetDict
+    try:
+        dataset = load_dataset(dataset_id)
+    except:
+        dataset = DatasetDict()
+
+    # Prepare new data to append
+    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(dataset_id)
+
+    return "Output flagged successfully."
+
+def enable_flag_button(state):
+    return gr.update(interactive=state)
+
+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")
+    ],
+    live=False,
+    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]
+    ],
+    theme="default",
+    allow_flagging=False
+)
+
+demo.queue()
+demo.launch()