remove russian language

app.py

@@ -123,7 +123,7 @@ def main():
         "template": "plotly_white",
     })
 
-    language = st.selectbox("Language", ("English", "Russian"))
+    language = "English"
     cls_model_name = st.selectbox(
         ATTRIBUTE_MODEL_LABEL[language],
         ATTRIBUTE_MODELS[language]
@@ -136,15 +136,7 @@ def main():
     cls_model_config = AutoConfig.from_pretrained(cls_model_name)
     if cls_model_config.problem_type == "multi_label_classification":
         label2id = cls_model_config.label2id
-        if "rubert-tiny-toxicity" in cls_model_name:
-            idx = 0
-            for i, k in enumerate(label2id.keys()):
-                if k == 'threat':
-                    idx = i
-
-            label_key = st.selectbox(ATTRIBUTE_LABEL[language], label2id.keys(), index=idx)
-        else:
-            label_key = st.selectbox(ATTRIBUTE_LABEL[language], label2id.keys())
+        label_key = st.selectbox(ATTRIBUTE_LABEL[language], label2id.keys())
         target_label_id = label2id[label_key]
         act_type = "sigmoid"
     elif cls_model_config.problem_type == "single_label_classification":
@@ -154,20 +146,17 @@ def main():
         act_type = "sigmoid"
     else:
         label2id = cls_model_config.label2id
+        filtered_label2id = {k: v  for k, v in label2id.items() if "negative" in k}
         label_key = st.selectbox(ATTRIBUTE_LABEL[language], label2id.keys())
         target_label_id = label2id[label_key]
         act_type = "softmax"
     st.write(WARNING_TEXT[language])
     show_pos_alpha = st.checkbox("Show positive alphas", value=False)
-    if "sst" in cls_model_name:
-        prompt = st.text_input(TEXT_PROMPT_LABEL[language], "The movie")
-    else:
-        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}")
     if act_type == "softmax":
         alpha = st.slider("α", min_value=-40, max_value=40 if show_pos_alpha else 0, step=1, value=0)
     else:
-        alpha = st.slider("α", min_value=-10, max_value=10 if show_pos_alpha else 0, step=1, value=0)
+        alpha = st.slider("α", min_value=-5, max_value=5 if show_pos_alpha else 0, step=1, value=0)
     entropy_threshold = st.slider("Entropy threshold", min_value=0., max_value=10., step=.1, value=2.)
     plot_idx = np.argmin(np.abs(entropy_threshold - x_s))
     scatter_tip = go.Scatter({
@@ -190,6 +179,10 @@ def main():
     auth_token = os.environ.get('TOKEN') or True
     fp16 = st.checkbox("FP16", value=True)
     st.session_state["generated_text"] = None
+    if "sst" in cls_model_name:
+        prompt = st.text_input(TEXT_PROMPT_LABEL[language], "The movie")
+    else:
+        prompt = st.text_input(TEXT_PROMPT_LABEL[language], PROMPT_EXAMPLE[language])
     st.subheader("Generated text:")
 
     def generate():