Nihal2003/customer_feedback_classification

1import streamlit as st2import pickle3 4# ---------------------------5# Load TF-IDF and Model6# ---------------------------7def load_artifacts():8    with open("tfidf.pkl", "rb") as f:9        tfidf = pickle.load(f)10 11    with open("model.pkl", "rb") as f:12        model = pickle.load(f)13 14    return tfidf, model15 16# ---------------------------17# Main UI18# ---------------------------19def main():20 21    st.set_page_config(page_title="Sentiment Classifier", page_icon="💬")22 23    st.title("💬 Customer Feedback Sentiment Classifier")24 25    st.write("Type your sentence and click **Proceed** for sentiment prediction.")26 27    tfidf, model = load_artifacts()28 29    text = st.text_area("Enter your sentence:", height=150)30 31    # LABEL MAPPING32    label_map = {33        0: "NEGATIVE",34        1: "NEUTRAL",35        2: "POSITIVE"36    }37 38    if st.button("Proceed"):39 40        if text.strip() == "":41            st.warning("⚠️ Please enter some text.")42        else:43            vec = tfidf.transform([text])44            prediction = model.predict(vec)[0]45 46            # Convert numeric → string label47            result = label_map[int(prediction)]48 49            # RED COLOR + BIG FONT50            st.markdown(51                f"""52                <h1 style='text-align:center; color:red; font-size:40px; font-weight:700;'>53                    {result}54                </h1>55                """,56                unsafe_allow_html=True57            )58 59if __name__ == "__main__":60    main()61