Rename app.py to ap.py

app.py → ap.py

@@ -26,16 +26,17 @@ st.subheader(header_tr)
 tr_input = st.text_area("Enter your text here:", height=50, key="tr_input")  #height=30
 if st.button("Click for predictions!", key="tr_predict"):
     with st.spinner('Generating predictions...'):
+        st.write("  ")
         result_tr = sentiment_pipeline_tr(tr_input)
         sentiment_tr = result_tr[0]["label"]
-        label_dict = {'LABEL_1': 'Hate ❌ ', 'LABEL_0': 'Non-hate ✅ '}  #🚫
+        label_dict = {'LABEL_1': 'Hate ❌  ', 'LABEL_0': 'Non-hate ✅  '}  #🚫
         sentiment_tr = label_dict[sentiment_tr]
 
         strength_tr = " "
         st.write(f"Detection: {sentiment_tr},  Strength: {strength_tr}")
 
 
-st.write(' ')
+st.write("  ")
 # Arabic
 sentiment_pipeline_ar = pipeline(task = "text-classification", model = "SoDehghan/BERTurk-hate-speech")
 header_ar = r"$\textsf{\scriptsize HSD in Arabic}$"
@@ -44,9 +45,10 @@ st.subheader(header_ar)
 ar_input = st.text_area("Enter your text here:", height=50 , key="ar_input")
 if st.button("Click for predictions!", key="ar_predict"):
     with st.spinner('Generating predictions...'):
+        st.write("  ")
         result_ar = sentiment_pipeline_ar(ar_input)
         sentiment_ar = result_ar[0]["label"]
-        label_dict = {'LABEL_1': 'Hate ❌ ', 'LABEL_0': 'Non-hate ✅ '}
+        label_dict = {'LABEL_1': 'Hate ❌  ', 'LABEL_0': 'Non-hate ✅  '}
         sentiment_ar = label_dict[sentiment_ar]
 
         strength_tr = " "