Update pages/3_Deployment.py

pages/3_Deployment.py

@@ -17,46 +17,6 @@ st.markdown("""
 - 😐 **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!")