import streamlit as st import pandas as pd import numpy as np import os import re import string from collections import Counter from datetime import datetime, timedelta from html import escape from database import engine, get_tweet_count, get_latest_crawl_time from timezone_utils import ( parse_dt_with_tz, parse_dt_with_source_tz, get_timezone_label, get_timezone_name, ) import plotly.graph_objects as go import joblib from page_modules.table_utils import render_standard_table model = joblib.load("model_naive_bayes.pkl") tfidf = joblib.load("tfidf_vectorizer.pkl") def parse_dt(series): return parse_dt_with_tz( series, st.session_state.get("user_timezone", "WIB (UTC+7)") ) def parse_crawled_dt(series): return parse_dt_with_source_tz( series, st.session_state.get("user_timezone", "WIB (UTC+7)"), os.getenv("APP_TIMEZONE", "Asia/Makassar") ) def format_dt(value): if value is None or pd.isna(value): return "Belum ada" try: tz_label = get_timezone_label( st.session_state.get("user_timezone", "WIB (UTC+7)") ) return f"{value.strftime('%d/%m/%Y %H:%M')} {tz_label}" except Exception: return "Belum ada" def format_now(): now = parse_dt(pd.Series([datetime.utcnow().isoformat()])).iloc[0] return format_dt(now) def user_today(): timezone_choice = st.session_state.get("user_timezone", "WIB (UTC+7)") return pd.Timestamp.now(tz=get_timezone_name(timezone_choice)).date() def _sync_dynamic_period(): mode = st.session_state.get("analysis_mode") today = user_today() configs = { "realtime": ( today - timedelta(days=6), today, "Tweet Terkini โ€” 7 Hari Terakhir", ), "30days": ( today - timedelta(days=29), today, "30 Hari Terakhir", ), "captured": ( today, today, "Tweet Hari Ini", ), } if mode not in configs: return start_day, end_day, mode_display = configs[mode] start_day = pd.Timestamp(start_day).date() end_day = pd.Timestamp(end_day).date() # Gunakan timestamp yang lebih inklusif untuk end_date dt_start = datetime.combine(start_day, datetime.min.time()) dt_end = datetime.combine( end_day + timedelta(days=1), # Termasuk seluruh hari end_day datetime.min.time() ) st.session_state.filter_start_date = dt_start st.session_state.filter_end_date = dt_end st.session_state.filter_label = ( f"{dt_start.strftime('%d/%m/%Y')} s/d {end_day.strftime('%d/%m/%Y')}" ) st.session_state.mode_display = mode_display st.session_state.filter_date_column = "created_at" NORMALISASI = { "gk": "tidak", "ga": "tidak", "gak": "tidak", "nggak": "tidak", "ngga": "tidak", "tdk": "tidak", "tak": "tidak", "yg": "yang", "dgn": "dengan", "utk": "untuk", "org": "orang", "krn": "karena", "dr": "dari", "tp": "tapi", "tpi": "tapi", "sm": "sama", "jd": "jadi", "sdh": "sudah", "blm": "belum", "emg": "memang", "emang": "memang", "gimana": "bagaimana", "gitu": "begitu", "gini": "begini", "bgt": "banget", "ongkir": "ongkos kirim", "freeongkir": "gratis ongkir", "free": "gratis", "ecommerce": "e commerce", } def _load_stopwords(): stopword_file = "indonesian-stopwords-complete.txt" base = set() try: with open(stopword_file, "r", encoding="utf-8") as f: base = set(f.read().splitlines()) for kata in ["tidak", "bukan", "jangan", "kurang", "lebih"]: base.discard(kata) except FileNotFoundError: base = { "yang", "dan", "di", "ke", "dari", "ini", "itu", "dengan", "untuk", "pada", "adalah", "oleh", "ada", "ya", "akan", "atau", "juga" } base.update({ "rt", "amp", "https", "http", "co", "t", "wkwk", "wkwkwk", "haha", "hehe", "yg", "dgn", "utk", "dr", "krn", "tp", "jd", "sdh", "aja", "doang", "banget", "bgt", "nih", "sih", "dong", "deh", }) return base def _get_stemmer(): try: from Sastrawi.Stemmer.StemmerFactory import StemmerFactory return StemmerFactory().create_stemmer() except Exception: return None def preprocess(text, stopwords, stemmer): text = str(text).lower() text = re.sub(r"http\S+|www\S+|https\S+", "", text) text = re.sub(r"@\w+", "", text) text = re.sub(r"#", "", text) text = re.sub(r"\d+", "", text) text = text.translate(str.maketrans("", "", string.punctuation)) text = re.sub(r"[^a-zA-Z\s]", "", text) text = re.sub(r"\s+", " ", text).strip() text = " ".join( NORMALISASI.get(word, word) for word in text.split() ) text = " ".join( word for word in text.split() if word not in stopwords and len(word) > 2 ) if stemmer: text = stemmer.stem(text) return text def predict_batch(texts): vectors = tfidf.transform(texts) preds = model.predict(vectors) confidences = [] if hasattr(model, "predict_proba"): probs = model.predict_proba(vectors) confidences = probs.max(axis=1) else: confidences = np.ones(len(preds)) final = [] for pred, conf in zip(preds, confidences): p = str(pred).lower() if p == "positif": sentiment = "Positif" elif p == "negatif": sentiment = "Negatif" elif p == "netral": sentiment = "Netral" else: sentiment = str(pred).capitalize() final.append((sentiment, float(conf))) return final def _section_header(title, subtitle=""): """Render consistent section header card""" sub_html = ( f'
{subtitle}
' if subtitle else "" ) st.markdown(f"""
{title}
{sub_html}
""", unsafe_allow_html=True) def _render_sentiment_styles(): st.markdown(""" """, unsafe_allow_html=True) def _section_gap(size="md"): st.markdown( f'
', unsafe_allow_html=True ) def _info_chip(label, value, color="#3b6cf7"): st.markdown(f"""
{label}
{value}
""", unsafe_allow_html=True) def _pill(col, icon, bg, color, dark, label, val, sub): """Render metric pill card with consistent styling""" with col: fs = "1.25rem" if len(str(val)) <= 8 else "1rem" st.markdown(f"""
{icon}
{label}
{val}
{sub}
""", unsafe_allow_html=True) def _render_pie_chart(pos_n, neu_n, neg_n, total, filter_label): _section_header( "๐Ÿ”ต Sebaran Sentimen", f"Periode {filter_label} ยท Diupdate: {format_now()}" ) labels = [] values = [] colors = [] color_map = { "Positif": "#16a34a", "Netral": "#94a3b8", "Negatif": "#ef4444", } for label, val in [ ("Positif", pos_n), ("Netral", neu_n), ("Negatif", neg_n) ]: if val > 0: labels.append(label) values.append(val) colors.append(color_map[label]) dominant = max( [ ("Positif", pos_n), ("Netral", neu_n), ("Negatif", neg_n) ], key=lambda x: x[1] ) dom_pct = round( dominant[1] / total * 100 ) if total > 0 else 0 dom_emoji = { "Positif": "๐Ÿ˜Š", "Netral": "๐Ÿ˜", "Negatif": "๐Ÿ˜ž" }.get(dominant[0], "๐Ÿ“Š") fig = go.Figure(data=[ go.Pie( labels=labels, values=values, hole=0.62, marker=dict( colors=colors, line=dict(color="white", width=4) ), textinfo="label+percent", textfont=dict(size=12), hovertemplate="%{label}
%{value:,} tweet โ€” %{percent}", direction="clockwise", sort=False, ) ]) fig.add_annotation( text=f"{dom_pct}%
{dom_emoji} {dominant[0]}", x=0.5, y=0.5, showarrow=False, align="center", font=dict(size=20, color="#0f172a") ) fig.update_layout( height=300, margin=dict(l=10, r=10, t=10, b=10), showlegend=True, legend=dict( orientation="h", y=-0.08, x=0.5, xanchor="center", font=dict(size=11) ), paper_bgcolor="rgba(0,0,0,0)", ) st.plotly_chart( fig, width="stretch", config={"displayModeBar": False} ) return dominant def _render_bar_chart(pos_n, neu_n, neg_n, filter_label, total): _section_header( "๐Ÿ“Š Perbandingan Jumlah Tweet per Sentimen", f"Periode {filter_label} ยท Diupdate: {format_now()}" ) categories = ["Positif ๐Ÿ˜Š", "Netral ๐Ÿ˜", "Negatif ๐Ÿ˜ž"] values = [pos_n, neu_n, neg_n] colors = ["#16a34a", "#94a3b8", "#ef4444"] # Hitung persentase percentages = [ (v / total * 100) if total > 0 else 0 for v in values ] fig = go.Figure() for cat, val, color, pct in zip(categories, values, colors, percentages): fig.add_trace( go.Bar( x=[cat], y=[val], marker=dict(color=color, opacity=0.88), text=[f"{val:,}"], textposition="outside", textfont=dict(size=14, color="#0f172a"), width=0.5, hovertemplate=f"{cat}
{val:,} tweet ({pct:.1f}%)", ) ) fig.update_layout( height=320, margin=dict(l=0, r=0, t=30, b=0), paper_bgcolor="rgba(0,0,0,0)", plot_bgcolor="rgba(0,0,0,0)", showlegend=False, xaxis=dict(showgrid=False), yaxis=dict( showgrid=True, gridcolor="rgba(226,232,240,0.8)", griddash="dot", ), ) st.plotly_chart( fig, width="stretch", config={"displayModeBar": False} ) def _render_trend_chart(fdf, filter_label, dt_start, dt_end): _section_header( "๐Ÿ“… Tren Sentimen dari Hari ke Hari", f"Periode {filter_label} ยท Diupdate: {format_now()}" ) df_tl = fdf.copy() df_tl["date"] = pd.to_datetime( df_tl["created_at"], errors="coerce" ).dt.date actual_tl = ( df_tl .groupby(["date", "sentiment"]) .size() .reset_index(name="count") ) date_range = pd.date_range( pd.Timestamp(dt_start).date(), pd.Timestamp(dt_end).date(), freq="D" ) base_dates = pd.DataFrame({"date": date_range.date}) fig = go.Figure() config = [ ("Positif", "#16a34a", "rgba(22,163,74,0.08)"), ("Netral", "#94a3b8", "rgba(148,163,184,0.06)"), ("Negatif", "#ef4444", "rgba(239,68,68,0.08)"), ] for sent, color, fill in config: data = base_dates.merge( actual_tl[actual_tl["sentiment"] == sent][["date", "count"]], on="date", how="left" ) data["count"] = data["count"].fillna(0).astype(int) fig.add_trace( go.Scatter( x=data["date"], y=data["count"], name=sent, mode="lines+markers", line=dict( color=color, width=2.5, shape="spline", smoothing=0.8 ), marker=dict( size=6, color="white", line=dict(color=color, width=2.5) ), fill="tozeroy", fillcolor=fill, hovertemplate=f"{sent}
%{{x}}: %{{y}} tweet", ) ) fig.update_layout( height=290, margin=dict(l=0, r=0, t=10, b=0), paper_bgcolor="rgba(0,0,0,0)", plot_bgcolor="rgba(0,0,0,0)", legend=dict( orientation="h", y=1.1, x=0, font=dict(size=11) ), xaxis=dict(showgrid=True, gridcolor="rgba(226,232,240,0.6)"), yaxis=dict(showgrid=True, gridcolor="rgba(226,232,240,0.6)", griddash="dot"), hovermode="x unified", ) st.plotly_chart( fig, width="stretch", config={"displayModeBar": False} ) def _render_wordcloud(fdf): _section_header( "โ˜๏ธ Kata-Kata Populer per Sentimen", "Word Cloud dari tweet yang sudah melalui preprocessing" ) try: from wordcloud import WordCloud import matplotlib.pyplot as plt stop_wc = { "ongkos", "kirim", "gratis", "komdigi", "ongkir" } wc1, wc2, wc3 = st.columns(3, gap="medium") cfg = [ (wc1, "Positif", "Greens", "๐Ÿ˜Š Positif", "#f0fdf4", "#14532d"), (wc2, "Netral", "Blues", "๐Ÿ˜ Netral", "#f8fafc", "#334155"), (wc3, "Negatif", "Reds", "๐Ÿ˜ž Negatif", "#fef2f2", "#7f1d1d"), ] for col, sent, cmap, title, bg, tc in cfg: with col: st.markdown(f"""
{title}
""", unsafe_allow_html=True) sub = fdf[ fdf["sentiment"] == sent ] words = [ w for w in " ".join( sub["clean_text"].fillna("") ).split() if len(w) > 2 and w not in stop_wc ] if words: wc = WordCloud( width=420, height=260, background_color="white", colormap=cmap, max_words=60, relative_scaling=0.5, collocations=False, ).generate(" ".join(words)) fig, ax = plt.subplots(figsize=(5, 3.1)) ax.imshow(wc, interpolation="bilinear") ax.axis("off") plt.tight_layout(pad=0) st.pyplot( fig, clear_figure=True ) else: st.info("Data kata tidak cukup") except ImportError: st.warning("Install wordcloud dan matplotlib terlebih dahulu.") def _render_tweet_table(fdf, filter_label): _section_header( "๐Ÿ“‹ Tabel Analisis Sentimen", f"Total {len(fdf):,} tweet ยท {filter_label}" ) with st.container(border=True, key="sentiment_table_controls"): tf1, tf2, tf3 = st.columns( [3, 1.6, 1.7], gap="medium", vertical_alignment="bottom" ) with tf1: search = st.text_input( "Search tweet", placeholder="Ketik kata kunci di isi tweet...", key="sentiment_table_search" ) with tf2: sf = st.selectbox( "Filter sentimen", ["Semua", "Positif ๐Ÿ˜Š", "Netral ๐Ÿ˜", "Negatif ๐Ÿ˜ž"], key="sentiment_table_filter" ) with tf3: sort_by = st.selectbox( "Urutkan", ["Terbaru dulu", "Terlama dulu", "Keyakinan tertinggi"], key="sentiment_table_sort" ) _section_gap("sm") tdf = fdf.copy() if "crawled_at" not in tdf.columns: tdf["crawled_at"] = pd.NaT if search: tdf = tdf[ tdf["text"].str.contains( search, case=False, na=False ) ] sf_map = { "Positif ๐Ÿ˜Š": "Positif", "Netral ๐Ÿ˜": "Netral", "Negatif ๐Ÿ˜ž": "Negatif", } if sf != "Semua": tdf = tdf[ tdf["sentiment"] == sf_map.get(sf, sf) ] if sort_by == "Terbaru dulu": tdf = tdf.sort_values( "created_at", ascending=False ) elif sort_by == "Terlama dulu": tdf = tdf.sort_values( "created_at", ascending=True ) else: tdf = tdf.sort_values( "confidence", ascending=False ) out = tdf[ [ "created_at", "crawled_at", "text", "clean_text", "sentiment", "confidence" ] ].copy() out["created_at"] = out["created_at"].apply(format_dt) out["crawled_at"] = out["crawled_at"].apply(format_dt) out["confidence"] = out["confidence"].apply( lambda x: f"{x:.0%}" ) out["sentiment"] = out["sentiment"].map({ "Positif": "๐Ÿ˜Š Positif", "Netral": "๐Ÿ˜ Netral", "Negatif": "๐Ÿ˜ž Negatif", }).fillna(out["sentiment"]) out.columns = [ "Tanggal Tweet", "Masuk Database", "Tweet Asli", "Tweet Bersih", "Sentimen", "Keyakinan" ] render_standard_table( out, height=400, min_width=1220, badge_columns=["Sentimen"], nowrap=["Tanggal Tweet", "Masuk Database", "Sentimen", "Keyakinan"], wide_columns=["Tweet Asli", "Tweet Bersih"], column_widths={ "Tanggal Tweet": "170px", "Masuk Database": "170px", "Tweet Asli": "360px", "Tweet Bersih": "360px", "Sentimen": "130px", "Keyakinan": "110px", }, ) st.caption( f"Menampilkan {len(tdf):,} tweet" ) return tdf def _render_policy_recommendations(dominant, pos_n, neu_n, neg_n, total, filter_label): dom_name = dominant[0] dom_pct = dominant[1] / total * 100 if total else 0 _section_header( "๐ŸŽฏ Rekomendasi Tindakan & Insight Kebijakan", f"Berdasarkan analisis {total:,} tweet ยท {filter_label}" ) c1, c2, c3, c4 = st.columns(4, gap="medium") _pill( c1, {"Positif": "๐Ÿ˜Š", "Netral": "๐Ÿ˜", "Negatif": "๐Ÿ˜ž"}.get(dom_name, "๐Ÿ“Š"), {"Positif": "#f0fdf4", "Netral": "#f8fafc", "Negatif": "#fef2f2"}.get(dom_name, "#eef2ff"), {"Positif": "#16a34a", "Netral": "#64748b", "Negatif": "#ef4444"}.get(dom_name, "#3b6cf7"), {"Positif": "#14532d", "Netral": "#334155", "Negatif": "#7f1d1d"}.get(dom_name, "#1e3a8a"), "Sentimen Dominan", f"{dom_pct:.1f}%", dom_name ) _pill( c2, "๐Ÿ˜Š", "#f0fdf4", "#16a34a", "#14532d", "Positif", f"{pos_n:,}", "Total tweet positif" ) _pill( c3, "๐Ÿ˜", "#f8fafc", "#64748b", "#334155", "Netral", f"{neu_n:,}", "Total tweet netral" ) _pill( c4, "๐Ÿ˜ž", "#fef2f2", "#ef4444", "#7f1d1d", "Negatif", f"{neg_n:,}", "Total tweet negatif" ) _section_gap("sm") neg_pct = neg_n / total * 100 if total else 0 pos_pct = pos_n / total * 100 if total else 0 neu_pct = neu_n / total * 100 if total else 0 rows = [] if neg_pct >= 40: rows.append(( "๐Ÿ”ด URGENT", "Tanggapi Keluhan Publik", "Sentimen negatif tinggi menunjukkan ketidakpuasan signifikan", "Buat klarifikasi resmi dan buka ruang dialog publik", "Humas / Tim Kebijakan" )) if neg_pct >= 20: rows.append(( "๐ŸŸ  TINGGI", "Tinjau Ulang Kebijakan", f"Sentimen negatif mencapai {neg_pct:.1f}%", "Evaluasi poin kebijakan yang paling banyak dikeluhkan", "Tim Kebijakan" )) if neu_pct >= 30: rows.append(( "๐ŸŸก SEDANG", "Tingkatkan Sosialisasi", f"Sentimen netral {neu_pct:.1f}% menunjukkan banyak publik belum berpihak", "Perbanyak konten edukatif dan FAQ resmi", "Tim Komunikasi" )) if pos_pct >= 40: rows.append(( "๐ŸŸข INFO", "Pertahankan Momentum Positif", f"Sentimen positif {pos_pct:.1f}%", "Perkuat narasi positif dan gunakan kanal resmi secara konsisten", "Tim Media Sosial" )) rows.append(( "๐Ÿ”ต RUTIN", "Pemantauan Berkelanjutan", "Opini publik dapat berubah sewaktu-waktu", "Pantau sentimen harian dan buat laporan berkala", "Tim Analis Data" )) df_rek = pd.DataFrame( rows, columns=[ "Prioritas", "Tindakan", "Dasar Analisis", "Rekomendasi Konkret", "Penanggung Jawab" ] ) priority_color = { "๐Ÿ”ด URGENT": ("#fef2f2", "#7f1d1d", "#ef4444"), "๐ŸŸ  TINGGI": ("#fff7ed", "#7c2d12", "#ea580c"), "๐ŸŸก SEDANG": ("#fefce8", "#713f12", "#ca8a04"), "๐ŸŸข INFO": ("#f0fdf4", "#14532d", "#16a34a"), "๐Ÿ”ต RUTIN": ("#eff6ff", "#1e3a8a", "#3b6cf7"), } for _, row in df_rek.iterrows(): bg, tc, bc = priority_color.get( row["Prioritas"], ("#f8fafc", "#0f172a", "#64748b") ) st.markdown(f"""
{row["Prioritas"]}
{row["Tindakan"]}
๐Ÿ“Œ {row["Dasar Analisis"]}
โœ… {row["Rekomendasi Konkret"]}
๐Ÿ‘ค {row["Penanggung Jawab"]}
""", unsafe_allow_html=True) return df_rek def show(): st.markdown("""
๐Ÿ“ˆ

Analisis Sentimen

""", unsafe_allow_html=True) _render_sentiment_styles() if "analysis_mode" not in st.session_state: st.warning("โš ๏ธ Silakan pilih mode tampilan di halaman Ambil Data Twitter terlebih dahulu.") return _sync_dynamic_period() start_date = st.session_state.get("filter_start_date") end_date = st.session_state.get("filter_end_date") filter_label = st.session_state.get("filter_label", "-") mode_display = st.session_state.get("mode_display", "-") if start_date is None or end_date is None: st.warning("โš ๏ธ Silakan buka halaman Ambil Data Twitter terlebih dahulu.") return mode_meta = { "realtime": ("#16a34a", "๐Ÿ“ก"), "30days": ("#3b6cf7", "๐Ÿ“…"), "captured": ("#0284c7", "๐Ÿ“†"), "custom": ("#d97706", "๐Ÿ”"), } mode_color, mode_icon = mode_meta.get( st.session_state.analysis_mode, ("#3b6cf7", "๐Ÿ“Š") ) st.markdown(f"""
{mode_icon}
{mode_display}
Periode: {filter_label}
""", unsafe_allow_html=True) try: df_all = pd.read_sql( "SELECT * FROM tweets ORDER BY created_at DESC", engine ) if df_all.empty: st.warning("โš ๏ธ Belum ada data tweet.") return df_all["created_at"] = parse_dt(df_all["created_at"]) if "crawled_at" in df_all.columns: df_all["crawled_at"] = parse_crawled_dt(df_all["crawled_at"]) except Exception as e: st.error(f"โŒ Gagal membaca database: {e}") return s_dt = pd.Timestamp(start_date) e_dt = pd.Timestamp(end_date) # Filter dengan lebih toleran - gunakan < untuk end_date karena sudah incremented df = df_all[ (df_all["created_at"] >= s_dt) & (df_all["created_at"] < e_dt) ].copy() if df.empty: st.warning( f"โš ๏ธ Tidak ada tweet dengan tanggal asli dalam periode {filter_label}." ) return total_tweets_in_db = get_tweet_count() latest_crawl_marker = get_latest_crawl_time() or "no-crawl" data_marker = (total_tweets_in_db, latest_crawl_marker) cache_key = ( f"sent_{st.session_state.analysis_mode}_" f"{start_date}_{end_date}_{total_tweets_in_db}_{latest_crawl_marker}" ) # Clear old cache entries untuk memastikan data selalu fresh for old_key in list(st.session_state.keys()): if old_key.startswith("sent_") and old_key != cache_key: del st.session_state[old_key] force_refresh = data_marker != st.session_state.get("_last_sentiment_data_marker") if cache_key not in st.session_state or force_refresh: if force_refresh: st.info("๐Ÿ”„ Menyegarkan prediksi sentimen dengan data terbaru...") with st.spinner("๐Ÿ” Preprocessing & prediksi sentimen seluruh tweet..."): stopwords = _load_stopwords() stemmer = _get_stemmer() df["clean_text"] = df["text"].apply( lambda t: preprocess(t, stopwords, stemmer) ) dfc = df[ df["clean_text"].str.strip().str.len() > 0 ].copy() results = predict_batch( dfc["clean_text"].tolist() ) if results: sentiments, confidences = zip(*results) else: sentiments, confidences = [], [] dfc["sentiment"] = list(sentiments) dfc["confidence"] = list(confidences) st.session_state[cache_key] = dfc st.session_state["_last_sentiment_data_marker"] = data_marker df_s = st.session_state[cache_key] _section_header( "๐ŸŽฏ Filter Kata Kunci", "Kosongkan untuk melihat semua tweet" ) with st.container(border=True, key="sentiment_keyword_panel"): kw = st.text_input( "Kata kunci", placeholder="Contoh: ongkir, kurir, komdigi...", key="sentiment_keyword_search" ) st.markdown(f"""
Mode {escape(str(mode_display))} Periode {escape(str(filter_label))}
""", unsafe_allow_html=True) _section_gap("sm") fdf = ( df_s[ df_s["text"].str.contains( kw, case=False, na=False ) ].copy() if kw else df_s.copy() ) if fdf.empty: st.warning("โš ๏ธ Tidak ada tweet yang cocok dengan kata kunci tersebut.") return sc = fdf["sentiment"].value_counts() total = len(fdf) pos_n = int(sc.get("Positif", 0)) neu_n = int(sc.get("Netral", 0)) neg_n = int(sc.get("Negatif", 0)) pos_p = pos_n / total * 100 neu_p = neu_n / total * 100 neg_p = neg_n / total * 100 _section_header( "๐Ÿ“Œ Ringkasan Sentimen", f"Berdasarkan tanggal asli tweet ยท {filter_label}" ) _section_gap("sm") c1, c2, c3, c4 = st.columns(4, gap="medium") _pill( c1, "๐Ÿ“Š", "#eef2ff", "#3b6cf7", "#1e3a8a", "Total Tweet Dianalisis", f"{total:,}", f"Periode {filter_label}" ) _pill( c2, "๐Ÿ˜Š", "#f0fdf4", "#16a34a", "#14532d", "Positif", f"{pos_n:,}", f"{pos_p:.1f}% dari total" ) _pill( c3, "๐Ÿ˜", "#f8fafc", "#64748b", "#334155", "Netral", f"{neu_n:,}", f"{neu_p:.1f}% dari total" ) _pill( c4, "๐Ÿ˜ž", "#fef2f2", "#ef4444", "#7f1d1d", "Negatif", f"{neg_n:,}", f"{neg_p:.1f}% dari total" ) _section_gap("lg") col_left, col_right = st.columns(2, gap="medium") with col_left: dominant = _render_pie_chart( pos_n, neu_n, neg_n, total, filter_label ) with col_right: _render_bar_chart( pos_n, neu_n, neg_n, filter_label, total ) _section_gap("lg") _render_trend_chart( fdf, filter_label, start_date, end_date ) _section_gap("lg") _render_wordcloud(fdf) _section_gap("lg") tdf = _render_tweet_table( fdf, filter_label ) _section_gap("lg") df_rek = _render_policy_recommendations( dominant, pos_n, neu_n, neg_n, total, filter_label ) _section_gap("lg") _section_header("๐Ÿ“ฅ Unduh Hasil Analisis") with st.container(border=True, key="sentiment_download_panel"): d1, d2, d3 = st.columns( 3, gap="medium", vertical_alignment="bottom" ) with d1: st.download_button( "๐Ÿ“ฅ Semua Hasil Prediksi", fdf.to_csv(index=False).encode("utf-8"), f"hasil_prediksi_{datetime.now().strftime('%Y%m%d_%H%M')}.csv", "text/csv", width="stretch" ) with d2: summary = pd.DataFrame({ "Sentimen": ["Positif", "Netral", "Negatif"], "Jumlah": [pos_n, neu_n, neg_n], "Persen": [ f"{pos_p:.2f}%", f"{neu_p:.2f}%", f"{neg_p:.2f}%" ], }) st.download_button( "๐Ÿ“ˆ Ringkasan Sentimen", summary.to_csv(index=False).encode("utf-8"), f"ringkasan_{datetime.now().strftime('%Y%m%d_%H%M')}.csv", "text/csv", width="stretch" ) with d3: st.download_button( "๐ŸŽฏ Rekomendasi Tindakan", df_rek.to_csv(index=False).encode("utf-8"), f"rekomendasi_{datetime.now().strftime('%Y%m%d_%H%M')}.csv", "text/csv", width="stretch" )