update app

app.py

@@ -26,28 +26,28 @@ def load_model(text):
     #result_sentence = nlp(masked_text)
     #return result_sentence[0]["sequence"], result_sentence[0]["token_str"]
 
+import re
 def app():
     st.title("OGBV-BERT")
-    #st.markdown("This demo uses multiple hindi transformer models for Masked Language Modelling (MLM).")
-    #models_list = list(cfg["models"].keys())
-    #models = st.multiselect("Choose models", models_list, models_list)
-    
+     
     target_text_path = "./input/tweet_list.csv"
     target_text_df = pd.read_csv(target_text_path)
     texts = target_text_df["text"]
     st.sidebar.title("Place")
     pick_random = st.sidebar.checkbox("Pick any random text")
     
-    #results_df = pd.DataFrame(columns=["Model Name", "Filled Token", "Filled Text"])
-    #model_names = []
-    ##filled_masked_texts = []
-    #filled_tokens = []
+   
     if pick_random:
         random_text = texts[random.randint(0, texts.shape[0] - 1)]
-        masked_text = st.text_area("Please type a sentence to classify", random_text)
+        text = re.sub('@[^\s]+','',random_text)
+        text = text[3:]
+  
+        masked_text = st.text_area("Please type a sentence to classify", text)
     else:
         select_text = st.sidebar.selectbox("Select any of the following text", texts)
-        masked_text = st.text_area("Please type a sentence to classify", select_text)
+        text = re.sub('@[^\s]+','',select_text)
+        text = text[3:]
+        masked_text = st.text_area("Please type a sentence to classify", text)
     
     
     # pd.set_option('max_colwidth',30)
@@ -56,12 +56,6 @@ def app():
             pred = load_model(masked_text)
             st.write(pred)
 
-            # for selected_model in models:
-            #     filled_sentence, filled_token = load_model(masked_text, cfg["models"][selected_model])
-            #     model_names.append(selected_model)
-            #     filled_tokens.append(filled_token)
-            #     filled_masked_texts.append(filled_sentence)
-            # results_df["Model Name"] = model_names
-            # results_df["Filled Token"] = filled_tokens
-            # results_df["Filled Text"] = filled_masked_texts
-            # st.table(results_df)
+
+if __name__ == "__main__":
+    app()