Update app.py

app.py

@@ -286,50 +286,50 @@ if st.button("Analysis") and input_option == "Teks Manual" and user_input:
         analisis = True
 
 elif input_option == "Unggah Berkas XLSX" and uploaded_file is not None:
-if 'Text' in df.columns:
-    if 'Date' in df.columns:
-        for text, date in zip(texts, df['Date']):
-            sentiment_label = predict_sentiment(text, sentiment_model, tfidf_vectorizer, lookp_dict)
-            emoticon = get_emoticon(sentiment_label)
-            cleaned_text = clean_text(text)
-            norm_slang_text = normalize_slang(cleaned_text, lookp_dict)
-            tanpa_stopwords = remove_stopwords(norm_slang_text, stop_words)
-
-            result_entry = {
-                'Text': text,
-                'cleaned-text': cleaned_text,
-                'normalisasi-text': norm_slang_text,
-                'stopwords-remove': tanpa_stopwords,
-                'label': sentiment_label,
-                'emotikon': emoticon,
-                'Date': date,
-            }
-
-            results.append(result_entry)
-
-        analisis = True
+    if 'Text' in df.columns:
+        if 'Date' in df.columns:
+            for text, date in zip(texts, df['Date']):
+                sentiment_label = predict_sentiment(text, sentiment_model, tfidf_vectorizer, lookp_dict)
+                emoticon = get_emoticon(sentiment_label)
+                cleaned_text = clean_text(text)
+                norm_slang_text = normalize_slang(cleaned_text, lookp_dict)
+                tanpa_stopwords = remove_stopwords(norm_slang_text, stop_words)
+    
+                result_entry = {
+                    'Text': text,
+                    'cleaned-text': cleaned_text,
+                    'normalisasi-text': norm_slang_text,
+                    'stopwords-remove': tanpa_stopwords,
+                    'label': sentiment_label,
+                    'emotikon': emoticon,
+                    'Date': date,
+                }
+    
+                results.append(result_entry)
+    
+            analisis = True
+        else:
+            for text in texts:
+                sentiment_label = predict_sentiment(text, sentiment_model, tfidf_vectorizer, lookp_dict)
+                emoticon = get_emoticon(sentiment_label)
+                cleaned_text = clean_text(text)
+                norm_slang_text = normalize_slang(cleaned_text, lookp_dict)
+                tanpa_stopwords = remove_stopwords(norm_slang_text, stop_words)
+    
+                result_entry = {
+                    'Text': text,
+                    'cleaned-text': cleaned_text,
+                    'normalisasi-text': norm_slang_text,
+                    'stopwords-remove': tanpa_stopwords,
+                    'label': sentiment_label,
+                    'emotikon': emoticon,
+                }
+    
+                results.append(result_entry)
+    
+            analisis = True   
     else:
-        for text in texts:
-            sentiment_label = predict_sentiment(text, sentiment_model, tfidf_vectorizer, lookp_dict)
-            emoticon = get_emoticon(sentiment_label)
-            cleaned_text = clean_text(text)
-            norm_slang_text = normalize_slang(cleaned_text, lookp_dict)
-            tanpa_stopwords = remove_stopwords(norm_slang_text, stop_words)
-
-            result_entry = {
-                'Text': text,
-                'cleaned-text': cleaned_text,
-                'normalisasi-text': norm_slang_text,
-                'stopwords-remove': tanpa_stopwords,
-                'label': sentiment_label,
-                'emotikon': emoticon,
-            }
-
-            results.append(result_entry)
-
-        analisis = True   
-else:
-    st.warning("Berkas XLSX harus memiliki kolom bernama 'Text' untuk analisis sentimen.")
+        st.warning("Berkas XLSX harus memiliki kolom bernama 'Text' untuk analisis sentimen.")
 
         
 st.info('Tekan "Analysis" kembali jika tampilan menghilang', icon = 'ℹ️')