Update app.py

app.py

@@ -22,15 +22,13 @@ def visualize(text):
 @st.cache
 def load_model(text):
     
-
     checkpoint = 'mlkorra/OGBV-gender-bert-hi-en'
 
     tokenizer = AutoTokenizer.from_pretrained(checkpoint)
     model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
 
     nlp = pipeline('sentiment-analysis',model=model,tokenizer=tokenizer)
-
-  
+ 
     results = nlp(text)
     
     return results
@@ -42,11 +40,20 @@ def load_model(text):
 import re
 def app():
     st.title("OGBV-BERT")
-     
-    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")
+    
+    data = st.sidebar.radio("Pick the evaluation data :",('Twitter','Trac2020'))
+    
+    if data=="Twitter":
+    
+        target_text_path = "./input/tweet_list.csv"
+        target_text_df = pd.read_csv(target_text_path)
+        texts = target_text_df["text"]
+        
+    else:
+    
+        target_text_path = "./input/trac2_hin_test.csv"
+        target_text_df = pd.read_csv(target_text_path)
+        texts = target_text_df["Text"]
     
     pick_random = st.sidebar.checkbox("Pick any random text")