Update app.py

app.py

@@ -137,22 +137,22 @@ if st.button("Scrape Reddit"):
 
 if st.button("Sentiment Analysis"): 
     df = st.session_state.get("df")
-    progress_text.text("df loaded...")
+    st.write("df loaded...")
     
     # --- Sentiment Analysis Section ---
-    progress_text.text("sentiment pipeline loading...")
-    tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment-latest")
-    model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment-latest",
-                                                              use_auth_token=st.secrets["hugging_face_token"])
+    with st.spinner("Loading Sentiment Pipeline..."):
+        tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment-latest")
+        model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment-latest",
+                                                                  use_auth_token=st.secrets["hugging_face_token"])
         
-    sentiment_pipeline = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer, device=-1)
-    progress_text.text("sentiment pipeline loaded...")
+        sentiment_pipeline = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer, device=-1)
+        st.write("sentiment pipeline loaded...")
 
-    max_tokens = tokenizer.model_max_length
-    if max_tokens > 10000:
-        max_tokens = 512
+        max_tokens = tokenizer.model_max_length
+        if max_tokens > 10000:
+            max_tokens = 512
         
-    with st.spinner("Doing sentiment analysis..."):
+    with st.spinner("Doing Sentiment Analysis..."):
         # Apply sentiment analysis to Title directly (assuming Title is short)
         df['title_sentiment'] = df['Title'].apply(lambda x: safe_sentiment(preprocess_text(x)) if x else None)