Update app.py

app.py

@@ -212,7 +212,7 @@ def set_seed():
 
 
 
-
+ 
 with st.sidebar:
 
     st.image("Koya_Presentation-removebg-preview.png")
@@ -236,39 +236,38 @@ with st.sidebar:
         )
     url = "https://drive.google.com/file/d/1eWat34ot3j8onIeKDnJscKalp2oYnn8O/view"
     st.write("check out the paper [here](%s)" % url)
+with st.sidebar:
+    footer()
+    
+    sentence = st.text_input("Please input a sample sentence in the target language")
+    
+    models = get_model_infos(multilingual=None)
+    selected_models = st.multiselect(
+        "Select of number of models you would like to compare", models["id"], max_selections=5
+    )
+    
+    run = st.button("Get Scores")
+    if run:
+    
+        progress_text = "Computing recommendation Scores"
+        st.write(progress_text)
+        my_bar = st.progress(0)
+    
+        scores = {}
+        for index, model_id in enumerate(selected_models):
+            tokenizer = AutoTokenizer.from_pretrained(model_id)
+            model = AutoModelWithLMHead.from_pretrained(model_id)
+            if model_id.startswith("castorini"):
+                tokenizer.model_max_length = 512
+            MLM_MASK_TOKEN = tokenizer.mask_token_id  # [(103, '[MASK]')]
+            MLM_UNK_TOKEN = tokenizer.unk_token_id
+    
+            BATCH_SIZE = 1
+            score = get_sense_score_batched(
+                sentence, tokenizer, model, MLM_MASK_TOKEN, MLM_UNK_TOKEN, BATCH_SIZE
+            )
+            scores[model_id] = score
+            my_bar.progress(index + 1 / len(selected_models))
+        scores = sort_dictionary(scores)
+        st.write("Our recommendation is:", scores)
     
-
-footer()
-
-sentence = st.text_input("Please input a sample sentence in the target language")
-
-models = get_model_infos(multilingual=None)
-selected_models = st.multiselect(
-    "Select of number of models you would like to compare", models["id"], max_selections=5
-)
-
-run = st.button("Get Scores")
-if run:
-
-    progress_text = "Computing recommendation Scores"
-    st.write(progress_text)
-    my_bar = st.progress(0)
-
-    scores = {}
-    for index, model_id in enumerate(selected_models):
-        tokenizer = AutoTokenizer.from_pretrained(model_id)
-        model = AutoModelWithLMHead.from_pretrained(model_id)
-        if model_id.startswith("castorini"):
-            tokenizer.model_max_length = 512
-        MLM_MASK_TOKEN = tokenizer.mask_token_id  # [(103, '[MASK]')]
-        MLM_UNK_TOKEN = tokenizer.unk_token_id
-
-        BATCH_SIZE = 1
-        score = get_sense_score_batched(
-            sentence, tokenizer, model, MLM_MASK_TOKEN, MLM_UNK_TOKEN, BATCH_SIZE
-        )
-        scores[model_id] = score
-        my_bar.progress(index + 1 / len(selected_models))
-    scores = sort_dictionary(scores)
-    st.write("Our recommendation is:", scores)
-