add warning for russian language

app.py

@@ -68,6 +68,29 @@ PROMPT_EXAMPLE = {
     "Russian": "Привет, сегодня я"
 }
 
+WARNING_TEXT = {
+    "English": """
+    **Warning!**
+    
+    If you are clicking checkbox bellow positive alphas for CAIF sampling become available.
+    It means that language model will be forced to produce toxic or/and abusive text.
+    This space is only a demonstration of our method for controllable text generation 
+    and we are not responsible for the content produced by this method.
+    
+    **Please use it carefully and with positive intentions!**
+    """,
+    "Russian": """
+    ***Внимание!**
+    
+    После нажатия на чекбокс ниже положительные $\alpha$ станут доступны.
+    Это означает, что языковая модель будет генерировать токсичные тексты.
+    Это демо служит лишь демонстрацией нашего метода контролируемой генерации.
+    Мы не несем ответственности за полученные тексты.
+    
+    **Используйте этот метод осторожно и с положительными намерениями!**
+    """
+}
+
 
 def main():
     st.header("CAIF")
@@ -110,15 +133,7 @@ def main():
         print(list(label2id.keys()))
         label_key = st.selectbox(ATTRIBUTE_LABEL[language], [list(label2id.keys())[-1]])
         target_label_id = 1
-    st.write("""
-    **Warning!**
-    
-    If you are clicking checkbox bellow positive alphas for CAIF sampling become available.
-    It means that language model will be forced to generate toxic or/and abusive text.
-    This space is only a demonstration of our method and we are not responsible for the content of generated text.
-    
-    **Please use it carefully!**
-    """)
+    st.write(WARNING_TEXT[language])
     show_pos_alpha = st.checkbox("Show positive alphas", value=False)
     prompt = st.text_input(TEXT_PROMPT_LABEL[language], PROMPT_EXAMPLE[language])
     st.latex(r"p(x_i|x_{<i}, c) \propto p(x_i|x_{<i})p(c|x_{\leq i})^{\alpha}")
@@ -178,7 +193,8 @@ def inference(
         fp16: bool = True,
         alpha: float = 5,
         target_label_id: int = 0,
-        entropy_threshold: float = 0
+        entropy_threshold: float = 0,
+        act_type: str = "sigmoid"
 ) -> str:
     torch.set_grad_enabled(False)
     generator = load_generator(lm_model_name=lm_model_name)
@@ -198,7 +214,8 @@ def inference(
         "temperature": 1.0,
         "top_k_classifier": 100,
         "classifier_weight": alpha,
-        "target_cls_id": target_label_id
+        "target_cls_id": target_label_id,
+        "act_type": act_type
     }
     generator.set_ordinary_sampler(ordinary_sampler)
     if device == "cpu":