Update app.py

app.py

@@ -9,7 +9,7 @@ from fuzzywuzzy import fuzz
 st.title("Exploring Torch, Transformers, Rake, and Others analyzing Text")
 
 # Define the options for the dropdown menu, Selecting a remote txt file already created to analyze the text
-options = ['Apprecitation Letter', 'Regret Letter', 'Kindness Tale', 'Lost Melody Tale', 'Twitter Example 1', 'Twitter Example 2']
+options = ['None','Apprecitation Letter', 'Regret Letter', 'Kindness Tale', 'Lost Melody Tale', 'Twitter Example 1', 'Twitter Example 2']
 
 # Create a dropdown menu to select options
 selected_option = st.selectbox("Select a preset option", options)
@@ -40,10 +40,10 @@ def fetch_text_content(selected_option):
         return ""
 
 # Fetch text content based on selected option
-text_content = fetch_text_content(selected_option)
+jd = fetch_text_content(selected_option)
 
 # Display text content in a text area
-jd = st.text_area("Text File Content", text_content)
+#jd = st.text_area("Text File Content", text_content)
 
 
 # Download NLTK resources
@@ -90,7 +90,7 @@ text = st.text_area('Enter the text to analyze', jd)
 
 if text:
 
-    with st.expander("Sentiment Analysis", expanded=True):
+    with st.expander("Sentiment Analysis - ✅ Completed", expanded=False):
         # Sentiment analysis
         out_sentiment = pipe_sent(text)
         # Display sentiment analysis result
@@ -99,18 +99,15 @@ if text:
         sentiment_emoji = '😊' if sentiment_label == 'POSITIVE' else '😞'
         sentiment_text = f"Sentiment Score: {sentiment_score}, Sentiment Label: {sentiment_label.capitalize()} {sentiment_emoji}"
         st.write(sentiment_text)
-        st.write("✅ Completed")
         
-    with st.expander("Summarization", expanded=True):        
+    with st.expander("Summarization - ✅ Completed", expanded=False):        
         # Summarization
         out_summ = pipe_summ(text)
         summarized_text = out_summ[0]['summary_text']
         st.write(summarized_text)
-        st.write("✅ Completed")
 
-    with st.expander("Keywords Extraction", expanded=True):
+    with st.expander("Keywords Extraction - ✅ Completed", expanded=False):
         # Keyword extraction
         keywords = extract_keywords(text)
         keyword_list = [keyword[1] for keyword in keywords]
         st.write(keyword_list)
-        st.write("✅ Completed")