Update app.py

app.py

@@ -49,7 +49,7 @@ def load_summarize_pipeline(): # summarize_pipeline
     return summarize_pipeline
 
 
-def summarize_txt(summarize_pipeline, texts, length):
+def summarize_txt(summarize_pipeline, texts, length, progress_bar):
         
     summary = summarize_pipeline(texts, max_length=10, num_return_sequences=1)
     result = summary[0]["summary_text"]
@@ -105,8 +105,9 @@ st.write("Search Query:", search_query)
 # Button to trigger scraping and summarize
 if st.button("Scrape & Summarize"):
     with st.spinner("Scraping..."):
-        # progress_bar = st.progress(0)
+        progress_bar = st.progress(0)
         progress_text = st.empty()    
+        
         total_limit = 5000  # Maximum number of submissions to check
         df = scrape_reddit_data(search_query, total_limit)
         length = len(df)
@@ -115,7 +116,8 @@ if st.button("Scrape & Summarize"):
     with st.spinner("Loading Summarizing Pipeline"):
         summarize_pipeline = load_summarize_pipeline()
     with st.spinner("Summarizing txt data..."):
-        df["Detail_Summary"] = df["Detail"].apply(lambda x: summarize_txt(summarize_pipeline, x, length) if x else None)
+        df["Detail_Summary"] = df["Detail"].apply(lambda x: \
+                                        summarize_txt(summarize_pipeline, x, length, progress_bar) if x else None)
 
     st.session_state["df"] = df
    
@@ -129,9 +131,13 @@ if st.button("Sentiment Analysis"):
         st.write("Sentiment pipeline loaded...")
 
     with st.spinner("Doing Sentiment Analysis..."):
+        progress_bar = st.progress(0)
+        
         # title is short, so dont havwe to use batch processing
-        df['Title_Sentiment'] = df['Title'].apply(lambda x: safe_sentiment(sentiment_pipeline, preprocess_text(x), length) if x else None)
-        df['Detail_Sentiment'] = df['Detail_Summary'].apply(lambda x: safe_sentiment(sentiment_pipeline, preprocess_text(x), length) if x else None)
+        df['Title_Sentiment'] = df['Title'].apply(lambda x: \
+                                        safe_sentiment(sentiment_pipeline, preprocess_text(x), length, progress_bar) if x else None)
+        df['Detail_Sentiment'] = df['Detail_Summary'].apply(lambda x: \
+                                        safe_sentiment(sentiment_pipeline, preprocess_text(x), length, progress_bar) if x else None)
         
         # # palarell procsssing for each row of detail
         # with ThreadPoolExecutor() as executor: