Update app.py

app.py

@@ -36,10 +36,10 @@ def chi2eng(filtered_data):
     language_Classification = langid.classify(filtered_data[0])[0]
     if language_Classification == "zh":
       st.write("Your input is Chinese, translating to English")
-      st.write('▶️ Translation model start downing, loading model may takes time, please wait...')
+      st.write('▶️ Loading Translation Model...')
       trans_pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-zh-en")
-      st.write('⏺️ Translation model successfully loaded')
-      st.write('▶️ Start translating...')
+      st.write('⏺️ Translation Model Successfully Loaded')
+      st.write('▶️ Start Translating...')
       translation_progress_count = 0
       translation_bar = st.progress(0)
       for i in filtered_data:
@@ -55,13 +55,13 @@ def chi2eng(filtered_data):
 
 # Text Classification：Negative/Neutral/Positive
 def emotion_classification(translated_data):
-    st.write('▶️ Classification model start downing, loading model may takes time, please wait...')
+    st.write('▶️ Loading Classification Model...')
     emo_pipe = pipeline("text-classification", model="deeplearningwithpython5240/twitter_roberta_base_sentiment_fintune_with_app_reviews")
-    st.write('⏺️ Classification model successfully loaded')
+    st.write('⏺️ Classification Model Successfully Loaded')
     negative_count, neutral_count, positive_count = 0,0,0
     negative_dict = {}
     emotion_progress_count = 0
-    st.write('▶️ Data processing ...')
+    st.write('▶️ Data Processing ...')
     emotion_bar = st.progress(0)
     for i in translated_data:
       labelled_result = emo_pipe(i)[0]['label']
@@ -78,7 +78,7 @@ def emotion_classification(translated_data):
       emotion_progress_count += 1/len(translated_data)
       emotion_bar.progress(emotion_progress_count)
     sizes = [negative_count, neutral_count, positive_count]
-    labels = ['negative_review', 'neutral_review', 'positive_review']
+    labels = ['Negative_Reviews', 'Neutral_Reviews', 'Positive_Reviews']
     # 创建饼状图
     st.write('Number of Positive Reviews: ', positive_count)
     st.write('Number of Neutral Reviews: ', neutral_count)
@@ -107,9 +107,9 @@ def emotion_classification(translated_data):
 
 # Summarization
 def summarization(top10_negative_str):
-    st.write('▶️ Summarizatio model start downing, loading model may takes time, please wait...')
+    st.write('▶️ Loading Summarizatio Model...')
     summarize_pipe = pipeline("text2text-generation", model="deeplearningwithpython5240/summarisation-t5-finetuned-model", max_new_tokens =512)
-    st.write('⏺️ Summarization model successfully loaded')
+    st.write('⏺️ Summarization Model Successfully Loaded')
     st.write('▶️ Summarizing...')
     summarized_text = summarize_pipe(top10_negative_str)
     return summarized_text
@@ -149,7 +149,8 @@ def main():
         #stage 4：Summarization
         st.text('🔶 Processing Summarization 🔶')
         summarized_text = summarization(top10_negative_str)
-        st.write(summarized_text)
+        st.write('Summarized Negative Comments:')
+        st.write(summarized_text[0]["generated_text"])
         st.text('️️🟢 Summarization Finished 🟢')
     except:
         st.write("")