🎓 ACOS Course Evaluation System
Aspect-Category-Opinion-Sentiment structured extraction · 🇬🇧 English · 🇲🇾 Bahasa Melayu
""", unsafe_allow_html=True)
tab_single, tab_batch, tab_perf, tab_about = st.tabs(
["Single Review", "Batch Analysis", "Performance", "About"]
)
# ════════════════════════════════════════════════════════════
# TAB 1 · Single Review
# ════════════════════════════════════════════════════════════
with tab_single:
if st.session_state.model_loaded:
st.markdown('● Model ready — auto-detects English / Bahasa Melayu · bilingual 4-column results for Melayu input
',
unsafe_allow_html=True)
else:
st.error("Model not loaded. Ensure model files are in the same folder as app.py.")
st.markdown("**How it works**")
st.markdown("""
✍️
1 · Enter a Review
Paste any course review in English or Bahasa Melayu, or pick a built-in sample.
🌐
2 · Language Detection
Bahasa Melayu input is auto-detected and translated to English via Helsinki-NLP MarianMT.
⚙️
3 · ACOS Inference
FLAN-T5-base (fine-tuned on 27,470 quadruples) extracts Aspect · Category · Opinion · Sentiment.
📊
4 · Bilingual Results
4-column ACOS table — each cell shows the English term and its Bahasa Melayu equivalent below.
""", unsafe_allow_html=True)
st.markdown("---")
col_input, col_samples = st.columns([3, 1])
with col_samples:
st.markdown("**Sample Reviews**")
st.caption("🇬🇧 English")
labels_en = [PLACEHOLDER_EN] + [s[0] for s in SAMPLES_EN]
chosen_en = st.selectbox(
"EN", labels_en,
key=f"sel_en_{st.session_state.en_sel_ver}",
label_visibility="collapsed",
)
if chosen_en != PLACEHOLDER_EN and chosen_en != st.session_state.last_loaded_en:
idx = [s[0] for s in SAMPLES_EN].index(chosen_en)
set_review(SAMPLES_EN[idx][1], chosen_en)
st.session_state.last_loaded_en = chosen_en
st.session_state.last_loaded_ms = PLACEHOLDER_MS
st.session_state.ms_sel_ver += 1
st.rerun()
st.caption("🇲🇾 Bahasa Melayu")
labels_ms = [PLACEHOLDER_MS] + [s[0] for s in SAMPLES_MS]
chosen_ms = st.selectbox(
"MS", labels_ms,
key=f"sel_ms_{st.session_state.ms_sel_ver}",
label_visibility="collapsed",
)
if chosen_ms != PLACEHOLDER_MS and chosen_ms != st.session_state.last_loaded_ms:
idx = [s[0] for s in SAMPLES_MS].index(chosen_ms)
set_review(SAMPLES_MS[idx][1], chosen_ms)
st.session_state.last_loaded_ms = chosen_ms
st.session_state.last_loaded_en = PLACEHOLDER_EN
st.session_state.en_sel_ver += 1
st.rerun()
if st.session_state.last_sample_label:
st.caption(f"Loaded: *{st.session_state.last_sample_label}*")
with col_input:
review_input = st.text_area(
"Course Review (English or Bahasa Melayu)",
value=st.session_state.review_text,
height=180,
placeholder="Paste a course review here…\nTampal ulasan kursus di sini…",
key=f"ta_{st.session_state.ta_key}",
)
analyse_clicked = st.button("Analyse →", type="primary")
if analyse_clicked and review_input.strip():
if not st.session_state.model_loaded:
st.error("Model is not loaded.")
else:
with st.spinner("Detecting language and running inference…"):
t0 = time.time()
lang = detect_language(review_input.strip())
if lang == "ms":
ensure_translators()
if st.session_state.trans_error:
st.warning("⚠️ Translation failed — see **Translation Error** in sidebar.")
translated_en = translate_ms_en(review_input.strip())
inference_text = translated_en
else:
translated_en = None
inference_text = review_input.strip()
raw, quads = run_inference(
inference_text,
st.session_state.tokenizer,
st.session_state.model,
)
elapsed = time.time() - t0
if lang == "ms":
st.markdown('