hotfix

NeuralTextGenerator.py

@@ -140,6 +140,8 @@ class BertTextGenerator:
 
         sentences = []
 
+        print('SEED TEXT -------------------------', seed_text)
+
         for batch_n in range(n_batches):
             batch_sentence_len = np.round(np.random.normal(avg_len, std_len))
             batch_sentence_len = int(np.clip(batch_sentence_len, min_len, max_len))

app.py

@@ -21,7 +21,13 @@ tokenizer = en_model.tokenizer
 model = en_model.model
 device = model.device
 
-en_model.tokenizer.add_special_tokens({'additional_special_tokens': ['[POSITIVE-0]', '[POSITIVE-1]', '[POSITIVE-2]','[NEGATIVE-0]', '[NEGATIVE-1]', '[NEGATIVE-2]']})
+en_model.tokenizer.add_special_tokens({'additional_special_tokens': [
+    '[POSITIVE-0]', 
+    '[POSITIVE-1]', 
+    '[POSITIVE-2]',
+    '[NEGATIVE-0]', 
+    '[NEGATIVE-1]', 
+    '[NEGATIVE-2]']})
 en_model.model.resize_token_embeddings(len(en_model.tokenizer))
 
 # def classify(sentiment):
@@ -84,7 +90,7 @@ def sentence_builder(n_sentences, max_iter, sentiment, seed_text):
 demo = gr.Interface(
     sentence_builder,
     [
-        gr.Slider(1, 15, value=2, label="Num. Tweets", info="Number of tweets to be generated."),
+        gr.Slider(1, 15, value=2, label="Num. Tweets", step= 1, info="Number of tweets to be generated."),
         gr.Slider(50, 500, value=100, label="Max. iter", info="Maximum number of iterations for the generation."),
         gr.Radio(["POSITIVE", "NEGATIVE"], label="Sentiment to generate"),
         gr.Textbox('', label="Seed text", info="Seed text for the generation.")