Update app.py

app.py

@@ -9,21 +9,33 @@ 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 = ['Option 1', 'Option 2']
+options = ['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 an option", options)
+selected_option = st.selectbox("Select a preset option", options)
 
 # Define URLs for different options
-url_option1 = "https://raw.githubusercontent.com/peteciank/public_files/main/jd_sm.txt"
-url_option2 = "https://raw.githubusercontent.com/peteciank/public_files/main/jd_controller.txt"
+url_option1 = "https://raw.githubusercontent.com/peteciank/public_files/main/Transformers/Appreciation_Letter.txt"
+url_option2 = "https://raw.githubusercontent.com/peteciank/public_files/main/Transformers/Regret_Letter.txt"
+url_option3 = "https://raw.githubusercontent.com/peteciank/public_files/main/Transformers/Kindness_Tale.txt"
+url_option4 = "https://raw.githubusercontent.com/peteciank/public_files/main/Transformers/Lost_Melody_Tale.txt"
+url_option5 = "https://raw.githubusercontent.com/peteciank/public_files/main/Transformers/Twitter_Example_1.txt"
+url_option6 = "https://raw.githubusercontent.com/peteciank/public_files/main/Transformers/Twitter_Example_2.txt"
 
 # Function to fetch text content based on selected option
 def fetch_text_content(selected_option):
-    if selected_option == 'Option 1':
+    if selected_option == 'Apprecitation Letter':
         return requests.get(url_option1).text
-    elif selected_option == 'Option 2':
+    elif selected_option == 'Regret Letter':
         return requests.get(url_option2).text
+    elif selected_option == 'Kindness Tale':
+        return requests.get(url_option3).text
+    elif selected_option == 'Lost Melody Tale':
+        return requests.get(url_option4).text
+    elif selected_option == 'Twitter Example 1':
+        return requests.get(url_option5).text
+    elif selected_option == 'Twitter Example 2':
+        return requests.get(url_option6).text
     else:
         return ""
 
@@ -77,24 +89,28 @@ def extract_keywords(text):
 text = st.text_area('Enter the text to analyze', jd)
 
 if text:
-    # Sentiment analysis
-    st.write("Sentiment Analysis")
-    out_sentiment = pipe_sent(text)
-    # Display sentiment analysis result
-    sentiment_score = out_sentiment[0]['score']
-    sentiment_label = out_sentiment[0]['label']
-    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)
 
-    # Summarization
-    st.write("Summarization")
-    out_summ = pipe_summ(text)
-    summarized_text = out_summ[0]['summary_text']
-    st.write(summarized_text)
+    with st.expander("Sentiment Analysis", expanded=True):
+        # Sentiment analysis
+        out_sentiment = pipe_sent(text)
+        # Display sentiment analysis result
+        sentiment_score = out_sentiment[0]['score']
+        sentiment_label = out_sentiment[0]['label']
+        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):        
+        # Summarization
+        out_summ = pipe_summ(text)
+        summarized_text = out_summ[0]['summary_text']
+        st.write(summarized_text)
+        st.write("✅ Completed")
 
-    # Keyword extraction
-    st.write("Keywords")
-    keywords = extract_keywords(text)
-    keyword_list = [keyword[1] for keyword in keywords]
-    st.write(keyword_list)
+    with st.expander("Keywords Extraction", expanded=True):
+        # Keyword extraction
+        keywords = extract_keywords(text)
+        keyword_list = [keyword[1] for keyword in keywords]
+        st.write(keyword_list)
+        st.write("✅ Completed")