Update pages/3_Deployment.py

pages/3_Deployment.py

@@ -9,23 +9,62 @@ st.title("🎯 Live Sentiment Classifier")
 with open("text_classification.pkl", "rb") as f:
     pipeline = pickle.load(f)
 
-# User input (updated label, placeholder, and help)
-user_input = st.text_area(
-    "##### ✍️ Enter a YouTube comment related to one of the following topics:",
-    placeholder="E.g., That reunion video was so emotional!",
-    help="""
-**Trained Topics:**
-
-- 😊 Positive → heartwarming reunion, kindness stories, feel-good moments  
-- 😠 Negative → bad customer experience, airline disaster, controversial news  
-- 😐 Neutral → latest tech review, travel vlog, gaming walkthrough
-"""
-)
+# Helpful context for users
+st.markdown("#### 📌 *This model works best for YouTube comments related to the following topics:*")
+st.markdown("""
+- 😊 **Positive**: heartwarming reunion, kindness stories, feel-good moments  
+- 😠 **Negative**: bad customer experience, airline disaster, controversial incident  
+- 😐 **Neutral**: tech reviews, travel vlogs, gaming walkthroughs
+""")
 
+# User input
+user_input = st.text_area("##### **Enter a YouTube comment to analyze:**", 
+                          placeholder="E.g., That reunion video was so emotional!")
+
+# Predict button
 if st.button("Predict Sentiment"):
     if user_input.strip():
         prediction = pipeline.predict([user_input])[0]
+        st.markdown(f"## ✅ Prediction: `{prediction.capitalize()}`")
+
+        if prediction == "positive":
+            st.success("😊 Positive Sentiment")
+        elif prediction == "negative":
+            st.error("😠 Negative Sentiment")
+        else:
+            st.info("😐 Neutral Sentiment")
+    else:
+        st.warning("⚠️ Please enter a comment to analyze.")
+
+# Navigation
+if st.button("Go to Model Page"):
+    st.switch_page("pages/2_Model_Training.py")import streamlit as st
+import pickle
 
+st.set_page_config(page_title="🎯 YouTube Comment Sentiment Predictor")
+
+st.title("🎯 Live Sentiment Classifier")
+
+# Load the complete pipeline (TF-IDF + Model)
+with open("text_classification.pkl", "rb") as f:
+    pipeline = pickle.load(f)
+
+# Helpful context for users
+st.markdown("#### 📌 *This model works best for YouTube comments related to the following topics:*")
+st.markdown("""
+- 😊 **Positive**: heartwarming reunion, kindness stories, feel-good moments  
+- 😠 **Negative**: bad customer experience, airline disaster, controversial incident  
+- 😐 **Neutral**: tech reviews, travel vlogs, gaming walkthroughs
+""")
+
+# User input
+user_input = st.text_area("##### **Enter a YouTube comment to analyze:**", 
+                          placeholder="E.g., That reunion video was so emotional!")
+
+# Predict button
+if st.button("Predict Sentiment"):
+    if user_input.strip():
+        prediction = pipeline.predict([user_input])[0]
         st.markdown(f"## ✅ Prediction: `{prediction.capitalize()}`")
 
         if prediction == "positive":
@@ -37,5 +76,6 @@ if st.button("Predict Sentiment"):
     else:
         st.warning("⚠️ Please enter a comment to analyze.")
 
+# Navigation
 if st.button("Go to Model Page"):
     st.switch_page("pages/2_Model_Training.py")