Update app.py

app.py

@@ -40,33 +40,9 @@ if text and option:
     # for example, if toxic = 1, then we can say the tweet is toxic, if threat is 0, then we can say there is no threat. 
     # if the value given by the prediction is above threshold, we put 1, 0 otherwise. 
     with col2:
-        dd = {
-            "category": labels,
-            "values": vals
-        }
         st.header("Toxicity class")
-        #out = pipe(text)
-        thresh = 0.2
-        cate_d = dict()
-        cate_d["category"] = labels
-        cate_d["values"] = []
-        for i in range(len(labels)):
-            if values[i] > thresh:
-                cate_d["values"].append(1)
-            else:
-                cate_d["values"].append(0)
-        df2 = pd.DataFrame(
-            data=cate_d
-        ).sort_values(by=['values'], ascending=False)
-        st.write(df2)
+        st.write(dd)
     # in the third and last collumn, we display the probability of each category, sorted in descending order
     with col3:
-        dd = {
-            "category": labels,
-            "values": vals
-        }
         st.header("Probability")
-        df3 = pd.DataFrame(            
-            data=dd
-        ).sort_values(by=['values'], ascending=False)
-        st.write(df3)
+        st.write(dd)