Update app.py

app.py

@@ -39,9 +39,13 @@ def chi2eng(filtered_data):
       st.write('▶️ Translation model start downing, loading model may takes time, please wait...')
       trans_pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-zh-en")
       st.write('⏺️ Translation model successfully loaded')
+      st.write('▶️ Start translating...')
+      translation_progress_count = 0
+      translation_bar = st.progress(0)
       for i in filtered_data:
-        st.write(trans_pipe(i)[0]['translation_text'])
         translated_data.append(trans_pipe(i)[0]['translation_text'])
+        translation_progress_count += 1/len(filtered_data)
+        translation_bar.progress(translation_progress_count)
     elif language_Classification == 'en':
         st.write("Your input is English, moving to next stage...")
         translated_data = [i for i in filtered_data]
@@ -56,11 +60,14 @@ def emotion_classification(translated_data):
     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 ...')
+    emotion_bar = st.progress(0)
     for i in translated_data:
       labelled_result = emo_pipe(i)[0]['label']
-      st.write('Text: ',i)
-      st.write('Label: ',labelled_result)
-      st.write(' ')
+      # st.write('Text: ',i)
+      # st.write('Label: ',labelled_result)
+      # st.write(' ')
       if labelled_result == 'negative':
         negative_dict[i] = emo_pipe(i)[0]['score']
         negative_count += 1
@@ -68,6 +75,8 @@ def emotion_classification(translated_data):
         neutral_count += 1
       if labelled_result == 'positive':
         positive_count += 1
+      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']
     # 创建饼状图
@@ -101,6 +110,7 @@ def summarization(top10_negative_str):
     st.write('▶️ Summarizatio model start downing, loading model may takes time, please wait...')
     summarize_pipe = pipeline("text2text-generation", model="deeplearningwithpython5240/summarisation-t5-finetuned-model", max_new_tokens =512)
     st.write('⏺️ Summarization model successfully loaded')
+    st.write('▶️ Summarizing...')
     summarized_text = summarize_pipe(top10_negative_str)
     return summarized_text