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| 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 | |
| with st.expander("#### 📌 **Click here to see what topics this model was trained on**"): | |
| st.markdown(""" | |
| - **heartwarming reunion, kindness stories, feel-good moments** | |
| - **bad customer experience, airline disaster, controversial incidents** | |
| - **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") |