| import streamlit as st |
| from utils.inference import predict_theme |
|
|
| EXAMPLES = { |
| "Broken on arrival": "barang sampai dalam keadaan pecah, packing asal asalan", |
| "Slow delivery": "pengiriman lama banget, sudah seminggu barang belum sampai juga", |
| "Happy customer": "barangnya bagus banget, pengiriman cepat, seller ramah, recommended", |
| "Wrong item": "warna yang dikirim tidak sesuai dengan yang saya pesan", |
| } |
|
|
|
|
| @st.cache_resource(show_spinner=False) |
| def _warm(): |
| predict_theme("warm up") |
| return True |
|
|
|
|
| def run(): |
| st.header("🔮 Predict a Review's Theme") |
| st.write( |
| "Paste an Indonesian product review. The model embeds it, finds the closest theme among " |
| "the topics it learned from Tokopedia reviews, and tells you whether it reads as " |
| "**praise** or a **complaint**." |
| ) |
|
|
| with st.spinner("Loading the language model..."): |
| _warm() |
|
|
| pick = st.selectbox("Try an example (optional)", ["-- type my own --", *EXAMPLES.keys()]) |
| default = "" if pick == "-- type my own --" else EXAMPLES[pick] |
| text = st.text_area("Customer review", value=default, height=130, |
| placeholder="contoh: barang sampai pecah, packing asal-asalan...") |
|
|
| if st.button("Predict theme", type="primary"): |
| if not text.strip(): |
| st.warning("Please paste a review first.") |
| return |
| with st.spinner("Analyzing..."): |
| cleaned, ranked = predict_theme(text) |
|
|
| if not ranked: |
| st.error("No usable text after cleaning - try a longer review.") |
| return |
|
|
| side, theme, sim = ranked[0] |
| if side == "Positive": |
| st.success(f"👍 **{theme}** - reads as a positive review") |
| else: |
| st.error(f"👎 **{theme}** - reads as a negative review") |
| st.caption(f"Confidence (cosine similarity): {sim:.2f}") |
|
|
| if len(ranked) > 1: |
| st.markdown("**Other close themes:**") |
| for s, th, sm in ranked[1:]: |
| tag = "praise" if s == "Positive" else "complaint" |
| st.write(f"- {th} *({tag}, {sm:.2f})*") |
|
|
| with st.expander("What the model actually read (after cleaning)"): |
| st.code(cleaned or "(empty)") |
|
|
| st.caption( |
| "Themes were learned separately for positive and negative reviews; the app picks the " |
| "single closest theme across both. Mixed Indonesian-English terms (e.g. 'fast charging') " |
| "can occasionally be matched to a delivery/speed theme." |
| ) |
|
|
