File size: 2,593 Bytes
62d0c8c 75cb516 62d0c8c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | 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") # load the model once so the first real prediction is fast
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."
)
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