Update app.py

app.py

@@ -1,769 +1,760 @@
-import streamlit as st
-import pandas as pd
-import numpy as np
-import joblib
-import os
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.calibration import CalibratedClassifierCV
-from sklearn.pipeline import Pipeline
-from sklearn.compose import ColumnTransformer
-from sklearn.preprocessing import StandardScaler, OneHotEncoder
-from sklearn.impute import SimpleImputer
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import (accuracy_score, precision_score, recall_score,
-                              f1_score, roc_auc_score, brier_score_loss,
-                              confusion_matrix, classification_report)
-import matplotlib.pyplot as plt
-import seaborn as sns
-import warnings
-warnings.filterwarnings('ignore')
-
-st.set_page_config(
-    page_title="💀 Ghosting Predictor",
-    page_icon="👻",
-    layout="wide",
-    initial_sidebar_state="collapsed"
-)
-
-st.markdown("""
-<style>
-@import url('https://fonts.googleapis.com/css2?family=Syne:wght@400;700;800&family=Inter:wght@400;500;600&display=swap');
-
-html, body, [class*="css"] { font-family: 'Inter', sans-serif; }
-h1, h2, h3 { font-family: 'Syne', sans-serif !important; }
-
-.main-title {
-    font-family: 'Syne', sans-serif; font-size: 3.2em; font-weight: 800;
-    background: linear-gradient(135deg, #ff6b6b, #ee5a24, #ff9f43);
-    -webkit-background-clip: text; -webkit-text-fill-color: transparent;
-    text-align: center; margin-bottom: 0; letter-spacing: -1px;
-}
-.sub-title { text-align: center; color: #636e72; font-size: 1em; margin-top: 4px; margin-bottom: 20px; }
-
-.verdict-card {
-    border-radius: 20px; padding: 28px 32px; text-align: center;
-    margin: 16px 0; position: relative; overflow: hidden;
-}
-.verdict-high  { background: linear-gradient(135deg, #00b894, #00cec9); color: white; }
-.verdict-mid   { background: linear-gradient(135deg, #fdcb6e, #e17055); color: white; }
-.verdict-low   { background: linear-gradient(135deg, #d63031, #6c5ce7); color: white; }
-.verdict-pct   { font-family: 'Syne', sans-serif; font-size: 4em; font-weight: 800; line-height: 1; }
-.verdict-label { font-size: 1.1em; font-weight: 600; margin-top: 6px; opacity: 0.92; }
-.verdict-quote { font-size: 0.95em; margin-top: 14px; font-style: italic; opacity: 0.88;
-                 border-top: 1px solid rgba(255,255,255,0.3); padding-top: 14px; }
-
-.diag-row { display: flex; gap: 12px; margin: 12px 0; flex-wrap: wrap; }
-.diag-card {
-    flex: 1; min-width: 140px; border-radius: 14px; padding: 16px 18px; text-align: center;
-    background: rgba(255,255,255,0.06); border: 1px solid rgba(255,255,255,0.1);
-}
-.diag-title { font-size: 0.72em; text-transform: uppercase; letter-spacing: 1px; color: #b2bec3; margin-bottom: 6px; }
-.diag-val   { font-family: 'Syne', sans-serif; font-size: 1.3em; font-weight: 700; }
-.val-high   { color: #00b894; }
-.val-mid    { color: #fdcb6e; }
-.val-low    { color: #ff7675; }
-
-.flag-item {
-    display: flex; align-items: flex-start; gap: 10px;
-    padding: 10px 14px; border-radius: 10px; margin: 6px 0;
-    background: rgba(214, 48, 49, 0.12); border-left: 3px solid #d63031; font-size: 0.92em;
-}
-.green-flag { background: rgba(0, 184, 148, 0.12); border-left: 3px solid #00b894; }
-
-.whatif-row {
-    display: flex; align-items: center; justify-content: space-between;
-    padding: 10px 16px; border-radius: 10px; margin: 6px 0;
-    background: rgba(255,255,255,0.05); border: 1px solid rgba(255,255,255,0.08); font-size: 0.9em;
-}
-.whatif-boost { color: #00b894; font-weight: 600; }
-.whatif-drop  { color: #ff7675; font-weight: 600; }
-
-.share-card {
-    background: linear-gradient(135deg, #1a1a2e, #16213e);
-    border-radius: 20px; padding: 28px; border: 1px solid rgba(255,255,255,0.1);
-    text-align: center; font-family: 'Syne', sans-serif;
-}
-.share-pct {
-    font-size: 3.5em; font-weight: 800;
-    background: linear-gradient(135deg, #ff6b6b, #ee5a24);
-    -webkit-background-clip: text; -webkit-text-fill-color: transparent;
-}
-.share-line { color: #dfe6e9; margin: 6px 0; font-size: 1em; }
-.share-tag  { color: #636e72; font-size: 0.78em; margin-top: 12px; }
-
-.m-card {
-    background: rgba(255,255,255,0.05); border-radius: 14px; padding: 18px; text-align: center;
-    border: 1px solid rgba(255,255,255,0.08); margin: 4px;
-}
-.m-num { font-family: 'Syne', sans-serif; font-size: 1.9em; font-weight: 800; color: #fdcb6e; }
-.m-lbl { font-size: 0.78em; color: #b2bec3; text-transform: uppercase; letter-spacing: 1px; }
-
-.stProgress > div > div { border-radius: 99px; }
-.block-container { padding-top: 1.5rem; }
-</style>
-""", unsafe_allow_html=True)
-
-# ── Feature config ─────────────────────────────────────────────────────────────
-NUM_FEATURES = [
-    'last_message_length', 'response_time_gap', 'conversation_length',
-    'reply_ratio', 'avg_response_time', 'emoji_count', 'question_asked',
-    'seen_ignored', 'past_ghosting_history', 'effort_score', 'delay',
-    'is_dry', 'is_long_gap', 'engagement_score', 'ghost_risk_combo',
-    'seen_delay', 'initiator_flag', 'inconsistency', 'decay_score', 'effort_mismatch'
-]
-CAT_FEATURES = ['initiator', 'message_tone', 'time_of_day', 'user_type']
-
-def make_preprocessor():
-    num_t = Pipeline([('imp', SimpleImputer(strategy='median')), ('sc', StandardScaler())])
-    cat_t = Pipeline([('imp', SimpleImputer(strategy='most_frequent')),
-                      ('ohe', OneHotEncoder(handle_unknown='ignore'))])
-    return ColumnTransformer([('num', num_t, NUM_FEATURES), ('cat', cat_t, CAT_FEATURES)], remainder='drop')
-
-# ── Load / train models ────────────────────────────────────────────────────────
-@st.cache_resource
-def load_models():
-    rp = 'rf_reply_model.pkl'; gp = 'rf_ghost_model.pkl'; mp = 'model_metrics.pkl'
-    if os.path.exists(rp) and os.path.exists(gp) and os.path.exists(mp):
-        try:
-            return joblib.load(rp), joblib.load(gp), joblib.load(mp)
-        except Exception as e:
-            st.warning(f"⚠️ Saved models failed ({str(e)[:60]}). Retraining...")
-
-    with st.spinner("🤖 Training models... (~30 sec)"):
-        try:
-            df = pd.read_csv('ghosting_dataset5.csv')
-        except FileNotFoundError:
-            st.error("❌ ghosting_dataset5.csv not found. Run gen_data5.py first.")
-            st.stop()
-
-        df['effort_score']     = df['last_message_length'] + (df['emoji_count'] * 2) + (df['question_asked'] * 5)
-        df['delay']            = df['response_time_gap'].apply(lambda x: 0 if x < 6 else 1 if x < 24 else 2)
-        df['is_dry']           = (df['message_tone'] == 'dry').astype(int)
-        df['is_long_gap']      = (df['response_time_gap'] > 24).astype(int)
-        df['engagement_score'] = df['reply_ratio'] * df['conversation_length']
-        df['ghost_risk_combo'] = ((df['response_time_gap'] > 24) & (df['reply_ratio'] < 0.4)).astype(int)
-        df['seen_delay']       = ((df['seen_ignored'] == 1) & (df['response_time_gap'] > 12)).astype(int)
-        df['initiator_flag']   = (df['initiator'] == 'me').astype(int)
-        df['inconsistency']    = (abs(df['response_time_gap'] - df['avg_response_time']) > 20).astype(int)
-        df['decay_score']      = (df['conversation_length'] / 200).clip(0, 1)
-        df['effort_mismatch']  = ((df['last_message_length'] > 20) & (df['reply_ratio'] < 0.3)).astype(int)
-
-        def _train(df, target):
-            X = df[NUM_FEATURES + CAT_FEATURES]; y = df[target]
-            # ── Proper 3-way split — no leakage ─────────────────────────────
-            X_tv, X_test, y_tv, y_test = train_test_split(X, y, test_size=0.15, random_state=42, stratify=y)
-            X_tr, X_val, y_tr, y_val   = train_test_split(X_tv, y_tv, test_size=0.15/0.85, random_state=42, stratify=y_tv)
-            mdl = Pipeline([('pre', make_preprocessor()),
-                            ('clf', RandomForestClassifier(n_estimators=400, max_depth=20,
-                                class_weight='balanced', random_state=42, n_jobs=-1))])
-            mdl.fit(X_tr, y_tr)
-            # Calibrate on val only; evaluate on test only
-            cal = CalibratedClassifierCV(mdl, method='sigmoid', cv=3)
-            cal.fit(X_val, y_val)
-            yp = cal.predict(X_test); yproba = cal.predict_proba(X_test)[:, 1]
-            return cal, {
-                'accuracy':  accuracy_score(y_test, yp),
-                'precision': precision_score(y_test, yp, zero_division=0),
-                'recall':    recall_score(y_test, yp, zero_division=0),
-                'f1_score':  f1_score(y_test, yp, zero_division=0),
-                'roc_auc':   roc_auc_score(y_test, yproba),
-                'brier':     brier_score_loss(y_test, yproba),
-                'confusion_matrix':      confusion_matrix(y_test, yp).tolist(),
-                'classification_report': classification_report(y_test, yp),
-                'train_size': len(X_tr), 'val_size': len(X_val), 'test_size': len(X_test),
-                'y_test': y_test.tolist(), 'y_pred_prob': yproba.tolist(),
-            }
-
-        rm, rmets = _train(df, 'reply')
-        gm, gmets = _train(df, 'ghosted')
-        joblib.dump(rm, rp); joblib.dump(gm, gp)
-        joblib.dump({'reply': rmets, 'ghosted': gmets}, mp)
-        return rm, gm, {'reply': rmets, 'ghosted': gmets}
-
-reply_model, ghost_model, all_metrics = load_models()
-
-# ── Feature builder ────────────────────────────────────────────────────────────
-def build_input(msg_len, tone, asked_q, resp_time, seen_ign, emoji,
-                conv_len=25, rr=None, avg_rt=None, past_ghost=0, user_type='casual'):
-    if rr is None:     rr     = 0.70 if asked_q else 0.50
-    if avg_rt is None: avg_rt = max(1.0, resp_time * 0.5)
-    tod = 'night' if resp_time > 20 else ('morning' if resp_time < 8 else 'day')
-    return pd.DataFrame({
-        'last_message_length':   [msg_len],
-        'response_time_gap':     [float(resp_time)],
-        'conversation_length':   [conv_len],
-        'reply_ratio':           [rr],
-        'avg_response_time':     [float(avg_rt)],
-        'emoji_count':           [emoji],
-        'question_asked':        [int(asked_q)],
-        'seen_ignored':          [seen_ign],
-        'past_ghosting_history': [past_ghost],
-        'effort_score':          [msg_len + (emoji * 2) + (5 if asked_q else 0)],
-        'delay':                 [0 if resp_time < 6 else (1 if resp_time < 24 else 2)],
-        'is_dry':                [int(tone == 'dry')],
-        'is_long_gap':           [int(resp_time > 24)],
-        'engagement_score':      [rr * conv_len],
-        'ghost_risk_combo':      [int(resp_time > 24 and rr < 0.4)],
-        'seen_delay':            [int(seen_ign == 1 and resp_time > 12)],
-        'initiator_flag':        [int(asked_q)],
-        'inconsistency':         [int(abs(resp_time - avg_rt) > 20)],
-        'decay_score':           [min(conv_len / 200, 1.0)],
-        'effort_mismatch':       [int(msg_len > 20 and rr < 0.3)],
-        'initiator':             ['me' if asked_q else 'them'],
-        'message_tone':          [tone],
-        'time_of_day':           [tod],
-        'user_type':             [user_type],
-    })
-
-def predict_both(row):
-    rp = reply_model.predict_proba(row)[0][1]
-    gp = ghost_model.predict_proba(row)[0][1]
-    return round(rp, 4), round(gp, 4)
-
-# ── Mode-adjusted probabilities ───────────────────────────────────────────────
-# The ML model gives one true probability. Each mode nudges it to tell a
-# coherent story consistent with that mode's personality:
-#   Savage:     slightly pessimistic — surfaces the worst-case reading
-#   Emotional:  softens ghost risk, because the point is empathy not alarm
-#   Delusional: bumps reply up, tanks ghost risk — the world is fine, always
-#   Normal:     raw model output, no adjustment
-#
-# Adjustments are additive deltas, clamped to [0.05, 0.95].
-# The base probability is always stored in session_state so switching modes
-# always starts from the same model output — no drift across mode switches.
-
-MODE_PROB_DELTA = {
-    #              reply_delta  ghost_delta
-    'normal':    (  0.00,        0.00),
-    'savage':    ( -0.07,       +0.10),   # pessimistic — "realistically, it's worse"
-    'emotional': ( +0.04,       -0.06),   # softer framing — ghost risk feels lower
-    'delusional':( +0.15,       -0.18),   # copium — everything looks fine
-}
-
-def apply_mode(base_rp, base_gp, mode):
-    rd, gd = MODE_PROB_DELTA[mode]
-    rp = max(0.05, min(0.95, base_rp + rd))
-    gp = max(0.05, min(0.95, base_gp + gd))
-    return round(rp, 4), round(gp, 4)
-
-# ── Mode-aware text ────────────────────────────────────────────────────────────
-# BUG FIX: All text that changes with mode must be derived AFTER reading mode
-# from session_state, and the cache key must include mode.
-
-MODE_QUOTES = {
-    'savage': {
-        'high':  ("Not bad. They might actually respond. Don't ruin it by double-texting.",
-                  "You're doing well. Shockingly."),
-        'mid':   ("50/50. A coin toss. Even randomness has standards.",
-                  "You're in the grey zone. Be honest — you already know."),
-        'low':   ("They saw it. Chose silence. That's your answer.",
-                  "This isn't a delay. This is an exit."),
-        'ghost_high': "They're already gone. The AI just confirmed what you felt.",
-        'ghost_mid':  "It could go either way. But look at that response time.",
-        'ghost_low':  "Slim chance. Still a chance. Do with that what you will.",
-    },
-    'emotional': {
-        'high':  ("There's still warmth here. Don't give up on this connection 💛",
-                  "The signs are good. You deserve someone who shows up."),
-        'mid':   ("It's uncertain, and that uncertainty is exhausting. You're not alone in this.",
-                  "You deserve clarity. This situation doesn't give you that yet."),
-        'low':   ("It's okay to feel this. Silence hurts. Your feelings are valid.",
-                  "Sometimes people fade. That's not a reflection of your worth."),
-        'ghost_high': "This is hard to hear, but you already sensed something was off.",
-        'ghost_mid':  "The uncertainty is real. You deserve better than wondering.",
-        'ghost_low':  "There's still a thread here. But protect your heart either way.",
-    },
-    'delusional': {
-        'high':  ("They're DEFINITELY writing a 3-paragraph reply right now 🔥",
-                  "They literally can't stop thinking about you. Facts."),
-        'mid':   ("They're just playing it cool. They're SO into you. Obviously.",
-                  "This is called mystery. They're keeping you guessing because you're special."),
-        'low':   ("They're probably just in a coma. Or lost their phone. In the ocean.",
-                  "WiFi issues. 100%. They'll reply any second now. Any. Second."),
-        'ghost_high': "Ghost risk?? No no no. They're just... composing the perfect reply.",
-        'ghost_mid':  "The model is clearly broken. You two have something special.",
-        'ghost_low':  "See?? Low ghost risk. They adore you. Manifesting the reply rn.",
-    },
-    'normal': {
-        'high':  ("Good signs based on your inputs. Message has solid energy.",
-                  "The indicators are positive here."),
-        'mid':   ("This one could genuinely go either way. Hard to call.",
-                  "Mixed signals in the data — reply is uncertain."),
-        'low':   ("The probability here is low based on current signals.",
-                  "Several risk factors are stacking up in this scenario."),
-        'ghost_high': "Multiple ghosting indicators are present.",
-        'ghost_mid':  "Some ghosting signals detected — not conclusive.",
-        'ghost_low':  "Low ghosting probability based on the inputs.",
-    }
-}
-
-# BUG FIX: Final Verdict text must also be mode-aware
-VERDICT_TEXT = {
-    'normal': {
-        'clear_ok':    ("You're overcomplicating this. They'll reply.", "#00b894"),
-        'mixed':       ("Mixed signals. Reply likely but something feels off.", "#fdcb6e"),
-        'one_sided':   ("This is one-sided. You're investing more than they are.", "#e17055"),
-        'move_on':     ("Move on. The data agrees with your gut.", "#d63031"),
-        'uncertain':   ("It's uncertain. Give it one more day before deciding.", "#636e72"),
-    },
-    'savage': {
-        'clear_ok':    ("They'll reply. Don't sabotage it now.", "#00b894"),
-        'mixed':       ("Reply likely. Ghost possible. Classic mixed energy situation.", "#fdcb6e"),
-        'one_sided':   ("You're the only one putting in effort here. Read that again.", "#e17055"),
-        'move_on':     ("It's over. Your gut knew. Now you have data too.", "#d63031"),
-        'uncertain':   ("Genuinely unclear. But your anxiety already picked a side.", "#636e72"),
-    },
-    'emotional': {
-        'clear_ok':    ("There's real connection here. Let it breathe.", "#00b894"),
-        'mixed':       ("Something good is here, but something's also holding back.", "#fdcb6e"),
-        'one_sided':   ("You deserve reciprocity. This doesn't look balanced right now.", "#e17055"),
-        'move_on':     ("It's okay to let go. That's not giving up, it's self-respect.", "#d63031"),
-        'uncertain':   ("Uncertainty is painful. Whatever happens, you'll be okay.", "#636e72"),
-    },
-    'delusional': {
-        'clear_ok':    ("Obviously they'll reply. You two are basically soulmates.", "#00b894"),
-        'mixed':       ("The universe is just building tension before the plot twist 🌟", "#fdcb6e"),
-        'one_sided':   ("You're the main character. They're just processing their feelings.", "#e17055"),
-        'move_on':     ("'Move on'?? The AI doesn't understand your unique connection.", "#d63031"),
-        'uncertain':   ("The model is just shy. It doesn't understand romance.", "#636e72"),
-    },
-}
-
-def get_verdict_key(reply_prob, ghost_prob):
-    if reply_prob > 0.65 and ghost_prob < 0.40:  return 'clear_ok'
-    if reply_prob > 0.65 and ghost_prob >= 0.40: return 'mixed'
-    if reply_prob > 0.40:                         return 'one_sided'
-    if ghost_prob > 0.65:                         return 'move_on'
-    return 'uncertain'
-
-def get_quotes(mode, reply_prob, ghost_prob):
-    rb = 'high' if reply_prob > 0.65 else ('mid' if reply_prob > 0.40 else 'low')
-    gb = 'ghost_high' if ghost_prob > 0.65 else ('ghost_mid' if ghost_prob > 0.40 else 'ghost_low')
-    q = MODE_QUOTES[mode][rb]
-    return (q[0], q[1]), MODE_QUOTES[mode][gb]
-
-def interest_label(reply_prob):
-    if reply_prob > 0.70: return "HIGH", "val-high"
-    if reply_prob > 0.45: return "MEDIUM", "val-mid"
-    return "LOW", "val-low"
-
-def effort_label(msg_len, asked_q, emoji):
-    score = (msg_len / 50) + (2 if asked_q else 0) + (emoji * 0.3)
-    if score > 4: return "HIGH", "val-high"
-    if score > 2: return "BALANCED", "val-mid"
-    return "ONE-SIDED", "val-low"
-
-def ghost_risk_label(ghost_prob):
-    if ghost_prob > 0.65: return "HIGH", "val-low"
-    if ghost_prob > 0.40: return "MEDIUM", "val-mid"
-    return "LOW", "val-high"
-
-# ── Header ─────────────────────────────────────���───────────────────────────────
-st.markdown("<div class='main-title'>💀 GHOSTING PREDICTOR</div>", unsafe_allow_html=True)
-st.markdown("<div class='sub-title'>AI-powered relationship reality check — be honest, it already knows</div>", unsafe_allow_html=True)
-
-# ── Personality mode selector ──────────────────────────────────────────────────
-st.markdown("#### Choose your vibe")
-if "personality_mode" not in st.session_state:
-    st.session_state["personality_mode"] = "normal"
-
-mode_cols = st.columns(4)
-modes = [("🧠 Normal", "normal"), ("💀 Savage", "savage"), ("😭 Emotional", "emotional"), ("🤡 Delusional", "delusional")]
-for i, (lbl, key) in enumerate(modes):
-    with mode_cols[i]:
-        if st.button(lbl, use_container_width=True,
-                     type="primary" if st.session_state["personality_mode"] == key else "secondary"):
-            st.session_state["personality_mode"] = key
-            st.rerun()
-
-# Read mode ONCE here — everything below uses this single variable
-mode = st.session_state["personality_mode"]
-
-mode_banner = {
-    "normal":     ("🧠 Normal Mode", "#636e72"),
-    "savage":     ("💀 Savage Mode — No feelings were harmed. They were obliterated.", "#d63031"),
-    "emotional":  ("😭 Emotional Mode — We see you. Your feelings are valid.", "#6c5ce7"),
-    "delusional": ("🤡 Delusional Mode — Stay hopeful! (The AI thinks you're cooked.)", "#e17055"),
-}
-banner_text, banner_color = mode_banner[mode]
-st.markdown(
-    f"<div style='text-align:center;background:{banner_color}22;border:1px solid {banner_color}55;"
-    f"border-radius:10px;padding:8px;font-size:0.88em;color:{banner_color};margin:8px 0 16px;'>"
-    f"{banner_text}</div>",
-    unsafe_allow_html=True
-)
-st.divider()
-
-# ── Tabs: 3 tabs only (message analyzer removed) ──────────────────────────────
-tab1, tab2, tab3 = st.tabs(["🔮 Predict", "📊 What-If", "🎓 Model Metrics"])
-
-# ══════════════════════════════════════════════════════════════════════════════
-# TAB 1 — MAIN PREDICTION
-# ══════════════════════════════════════════════════════════════════════════════
-with tab1:
-    col1, col2 = st.columns(2, gap="large")
-
-    with col1:
-        st.markdown("#### 📱 Your message")
-        message_length = st.slider("Message length (chars)", 1, 500, 80, 5)
-        message_tone   = st.selectbox("Tone", ['dry', 'neutral', 'enthusiastic'], index=1)
-        asked_question = st.toggle("Asked a question?", value=True)
-        emoji_count    = st.slider("Emojis used", 0, 10, 1)
-        past_ghost     = st.toggle("Have they ghosted you before?", value=False)
-
-    with col2:
-        st.markdown("#### ⏱️ Their behaviour")
-        response_time  = st.slider("Hours since you sent it", 0, 72, 4, 1)
-        seen_raw       = st.radio("Did they see it?", ["👁️ Yes, seen", "❓ Not seen yet"], horizontal=True)
-        seen_ignored   = 1 if "Yes" in seen_raw else 0
-        conv_len       = st.slider("How long has the convo been? (messages)", 1, 200, 20)
-        user_type_map  = {
-            "😊 Seems interested": "interested",
-            "💬 Normal/casual":    "casual",
-            "🌵 Very dry texter":  "dry_texter",
-            "👻 Known to ghost":   "ghoster",
-        }
-        user_type_label = st.selectbox("How would you describe them?", list(user_type_map.keys()))
-        user_type       = user_type_map[user_type_label]
-
-    st.divider()
-
-    # ── Predictions — cache key includes mode so text refreshes on mode change ──
-    # ikey tracks input changes only (not mode) — model is only re-run when
-    # inputs change. Base probabilities are stored raw (no mode applied).
-    # Mode adjustment is applied on every render so switching mode instantly
-    # changes the displayed numbers without re-running the model.
-    ikey = (message_length, message_tone, asked_question, response_time,
-            seen_ignored, emoji_count, conv_len, user_type, int(past_ghost))
-
-    if st.session_state.get("ikey") != ikey:
-        try:
-            row = build_input(message_length, message_tone, asked_question,
-                              response_time, seen_ignored, emoji_count,
-                              conv_len=conv_len, past_ghost=int(past_ghost),
-                              user_type=user_type)
-            base_rp, base_gp = predict_both(row)
-        except Exception as e:
-            st.error(f"Prediction error: {e}")
-            base_rp, base_gp = 0.5, 0.4
-        st.session_state.update({"ikey": ikey, "base_rp": base_rp, "base_gp": base_gp})
-
-    # Apply mode delta on every render — no model re-run needed
-    reply_prob, ghost_prob = apply_mode(
-        st.session_state["base_rp"],
-        st.session_state["base_gp"],
-        mode
-    )
-
-    # Derive ALL mode-dependent text here, after reading mode from session_state
-    (main_q, sub_q), ghost_q = get_quotes(mode, reply_prob, ghost_prob)
-    verdict_key = get_verdict_key(reply_prob, ghost_prob)
-    verdict_text, verdict_color = VERDICT_TEXT[mode][verdict_key]
-
-    # ── Dual verdict cards ────────────────────────────────────────────────────
-    vc1, vc2 = st.columns(2)
-    with vc1:
-        vclass = "verdict-high" if reply_prob > 0.65 else ("verdict-mid" if reply_prob > 0.40 else "verdict-low")
-        vlabel = "They'll reply 🔥" if reply_prob > 0.65 else ("Could go either way 😬" if reply_prob > 0.40 else "They're ghosting you 💀")
-        st.markdown(f"""
-        <div class='verdict-card {vclass}'>
-            <div style='font-size:0.8em;font-weight:600;opacity:0.8;text-transform:uppercase;letter-spacing:1px;'>Reply probability</div>
-            <div class='verdict-pct'>{reply_prob*100:.0f}%</div>
-            <div class='verdict-label'>{vlabel}</div>
-            <div class='verdict-quote'>"{main_q}"</div>
-        </div>
-        """, unsafe_allow_html=True)
-
-    with vc2:
-        gclass = "verdict-low" if ghost_prob > 0.65 else ("verdict-mid" if ghost_prob > 0.40 else "verdict-high")
-        glabel = "High ghost risk 💀" if ghost_prob > 0.65 else ("Uncertain 😬" if ghost_prob > 0.40 else "Probably fine 🙂")
-        st.markdown(f"""
-        <div class='verdict-card {gclass}'>
-            <div style='font-size:0.8em;font-weight:600;opacity:0.8;text-transform:uppercase;letter-spacing:1px;'>Ghost probability</div>
-            <div class='verdict-pct'>{ghost_prob*100:.0f}%</div>
-            <div class='verdict-label'>{glabel}</div>
-            <div class='verdict-quote'>"{ghost_q}"</div>
-        </div>
-        """, unsafe_allow_html=True)
-
-    # ── Conversation Diagnosis ────────────────────────────────────────────────
-    # BUG FIX: Diagnosis values are derived from ML probabilities (correct),
-    # but the Read Status now also reflects mode tone
-    st.markdown("#### 🧠 Conversation Diagnosis")
-    int_lbl, int_cls = interest_label(reply_prob)
-    eff_lbl, eff_cls = effort_label(message_length, asked_question, emoji_count)
-    gr_lbl,  gr_cls  = ghost_risk_label(ghost_prob)
-
-    # Read status changes with mode (delusional gives an excuse, savage is blunt)
-    if seen_ignored and response_time > 6:
-        seen_txt = {
-            'normal':    "IGNORED 🚨",
-            'savage':    "SEEN. IGNORED. 💀",
-            'emotional': "SEEN, NO REPLY 💔",
-            'delusional':"SEEN (composing!!) ✍️",
-        }[mode]
-        seen_cls = "val-low"
-    elif seen_ignored:
-        seen_txt = "SEEN ✓"; seen_cls = "val-mid"
-    else:
-        seen_txt = "NOT SEEN"; seen_cls = "val-mid"
-
-    st.markdown(f"""
-    <div class='diag-row'>
-        <div class='diag-card'>
-            <div class='diag-title'>Interest Level</div>
-            <div class='diag-val {int_cls}'>{int_lbl}</div>
-        </div>
-        <div class='diag-card'>
-            <div class='diag-title'>Effort Balance</div>
-            <div class='diag-val {eff_cls}'>{eff_lbl}</div>
-        </div>
-        <div class='diag-card'>
-            <div class='diag-title'>Ghost Risk</div>
-            <div class='diag-val {gr_cls}'>{gr_lbl}</div>
-        </div>
-        <div class='diag-card'>
-            <div class='diag-title'>Read Status</div>
-            <div class='diag-val {seen_cls}'>{seen_txt}</div>
-        </div>
-    </div>
-    """, unsafe_allow_html=True)
-
-    # ── Signal Breakdown ──────────────────────────────────────────────────────
-    st.markdown("#### 🚩 Signal Breakdown")
-    fc1, fc2 = st.columns(2)
-
-    red_flags, green_flags = [], []
-    if seen_ignored and response_time > 6:   red_flags.append("They saw your message. They chose silence.")
-    if response_time > 48:                   red_flags.append(f"It's been {response_time}h. That's not busy, that's avoidance.")
-    elif response_time > 24:                 red_flags.append("Over 24 hours — the energy is cooling off.")
-    if message_tone == 'dry':                red_flags.append("Dry tone doesn't open doors.")
-    if not asked_question:                   red_flags.append("No question = no reason to reply.")
-    if message_length < 30:                  red_flags.append("Short message — looks like low effort.")
-    if user_type == 'ghoster':               red_flags.append("You described them as a known ghoster. That's data.")
-    if past_ghost:                           red_flags.append("They've ghosted you before. Pattern recognised.")
-    if emoji_count == 0 and message_tone == 'dry': red_flags.append("Zero warmth signals in this message.")
-
-    if asked_question:                       green_flags.append("Asked a question — gives them something to respond to.")
-    if message_length > 100:                 green_flags.append("Substantial message — shows you put in effort.")
-    if message_tone == 'enthusiastic':       green_flags.append("Enthusiastic tone — energy is contagious.")
-    if response_time < 12:                   green_flags.append("Sent recently — they still might be composing a reply.")
-    if emoji_count > 0:                      green_flags.append("Used emojis — lightens the vibe.")
-    if user_type == 'interested':            green_flags.append("You described them as interested — that matters.")
-    if not past_ghost:                       green_flags.append("No ghosting history — fresh start.")
-
-    with fc1:
-        st.markdown("**Red flags**")
-        for f in red_flags:
-            st.markdown(f"<div class='flag-item'>🚩 {f}</div>", unsafe_allow_html=True)
-        if not red_flags:
-            st.markdown("<div class='flag-item green-flag'>✅ No major red flags detected.</div>", unsafe_allow_html=True)
-
-    with fc2:
-        st.markdown("**Green flags**")
-        for g in green_flags:
-            st.markdown(f"<div class='flag-item green-flag'>✅ {g}</div>", unsafe_allow_html=True)
-        if not green_flags:
-            st.markdown("<div class='flag-item'>🚩 Hmm, not many positives here.</div>", unsafe_allow_html=True)
-
-    st.divider()
-
-    # ── Final Verdict — mode-aware ────────────────────────────────────────────
-    # BUG FIX: verdict_text and verdict_color now come from VERDICT_TEXT[mode]
-    st.markdown("#### 🎯 Final Verdict")
-    st.markdown(
-        f"<div style='background:{verdict_color}22;border-left:4px solid {verdict_color};"
-        f"border-radius:0 12px 12px 0;padding:16px 20px;margin:10px 0;"
-        f"font-family:Syne,sans-serif;font-size:1.1em;color:{verdict_color};font-weight:600;'>"
-        f"{verdict_text}</div>",
-        unsafe_allow_html=True
-    )
-    if sub_q:
-        st.markdown(
-            f"<div style='color:#b2bec3;font-style:italic;font-size:0.9em;margin-top:8px;'>💭 {sub_q}</div>",
-            unsafe_allow_html=True
-        )
-
-    st.divider()
-
-    # ── Shareable card ────────────────────────────────────────────────────────
-    st.markdown("#### 📸 Share Your Result")
-    share_label = "They'll probably reply 🔥" if reply_prob > 0.65 else ("It's a coin flip 😬" if reply_prob > 0.40 else "Ghosting incoming 💀")
-    ghost_label = "Ghost risk: HIGH 💀" if ghost_prob > 0.65 else ("Ghost risk: MEDIUM ⚠️" if ghost_prob > 0.40 else "Ghost risk: LOW ✅")
-
-    st.markdown(f"""
-    <div class='share-card'>
-        <div style='font-size:0.75em;letter-spacing:2px;color:#636e72;text-transform:uppercase;margin-bottom:8px;'>AI Reality Check</div>
-        <div class='share-pct'>{reply_prob*100:.0f}%</div>
-        <div class='share-line' style='font-size:1.2em;font-weight:700;'>{share_label}</div>
-        <div class='share-line' style='color:#b2bec3;'>{ghost_label}</div>
-        <div class='share-line' style='font-style:italic;color:#dfe6e9;margin-top:10px;font-size:0.95em;'>"{main_q}"</div>
-        <div class='share-tag'>#GhostingPredictor • ghostingpredictor.app</div>
-    </div>
-    """, unsafe_allow_html=True)
-
-    share_text = (f"💀 Ghosting Predictor says:\n"
-                  f"Reply chance: {reply_prob*100:.0f}% — {share_label}\n"
-                  f"{ghost_label}\n"
-                  f'"{main_q}"\n'
-                  f"#GhostingPredictor #AI #Dating")
-
-    # FIX: st.button causes a full page rerun which resets widget defaults →
-    # changes ikey → triggers fresh prediction with default inputs → wrong %.
-    # Solution: always render the share text in a st.text_area (read-only style).
-    # st.text_area does NOT trigger a rerun when the user clicks inside it to
-    # select/copy — it only reruns on actual value change, which can't happen
-    # because the value is set programmatically and the user just selects text.
-    st.markdown("<div style='font-size:0.82em;color:#b2bec3;margin-bottom:4px;'>📋 Click inside, Ctrl+A, Ctrl+C to copy:</div>", unsafe_allow_html=True)
-    st.text_area(
-        label="share_text_area",
-        value=share_text,
-        height=120,
-        label_visibility="collapsed",
-        key=f"share_ta_{hash(share_text)}",   # stable key tied to content, not mode
-    )
-    st.markdown("<div style='text-align:center;font-size:0.8em;color:#636e72;margin-top:4px;'>📸 Or screenshot the card above and post it</div>", unsafe_allow_html=True)
-
-    st.markdown("<div style='text-align:center;margin-top:12px;font-size:0.85em;color:#636e72;'>Drop your situation in the comments — I'll tell you what the model says 👇</div>", unsafe_allow_html=True)
-
-# ══════════════════════════════════════════════════════════════════════════════
-# TAB 2 — WHAT-IF SIMULATOR
-# ══════════════════════════════════════════════════════════════════════════════
-with tab2:
-    st.markdown("#### 📊 What-If Simulator")
-    st.markdown("<div style='color:#636e72;font-size:0.88em;'>See how your odds change if you tweak one thing. Experiment freely.</div>", unsafe_allow_html=True)
-
-    if "base_rp" not in st.session_state:
-        st.info("Go to the **Predict** tab first to set your base scenario.")
-    else:
-        base_rp, base_gp = apply_mode(
-            st.session_state["base_rp"],
-            st.session_state["base_gp"],
-            mode
-        )
-        ml, tone, aq, rt, si, ec, cl_val, ut, pg = st.session_state["ikey"]
-
-        rclr = "#00b894" if base_rp > 0.65 else ("#fdcb6e" if base_rp > 0.4 else "#ff7675")
-        gclr = "#ff7675" if base_gp > 0.65 else ("#fdcb6e" if base_gp > 0.4 else "#00b894")
-        st.markdown(f"""
-        <div style='background:rgba(255,255,255,0.05);border-radius:12px;padding:14px 18px;margin-bottom:16px;'>
-            <div style='font-size:0.8em;color:#b2bec3;text-transform:uppercase;letter-spacing:1px;'>Your current situation</div>
-            <div style='font-family:Syne,sans-serif;font-size:1.6em;font-weight:700;'>
-                Reply: <span style='color:{rclr}'>{base_rp*100:.0f}%</span>
-                &nbsp;&nbsp; Ghost: <span style='color:{gclr}'>{base_gp*100:.0f}%</span>
-            </div>
-        </div>
-        """, unsafe_allow_html=True)
-
-        scenarios = []
-        if not aq:
-            r = build_input(ml, tone, True, rt, si, ec, cl_val, user_type=ut)
-            nr, ng = predict_both(r)
-            scenarios.append(("❓ If you added a question", nr, ng))
-        if tone != 'enthusiastic':
-            r = build_input(ml, 'enthusiastic', aq, rt, si, ec, cl_val, user_type=ut)
-            nr, ng = predict_both(r)
-            scenarios.append(("😄 If your tone was enthusiastic", nr, ng))
-        if ml < 150:
-            r = build_input(150, tone, aq, rt, si, ec, cl_val, user_type=ut)
-            nr, ng = predict_both(r)
-            scenarios.append(("📝 If your message was longer (150 chars)", nr, ng))
-        if rt > 12:
-            r = build_input(ml, tone, aq, 2, si, ec, cl_val, user_type=ut)
-            nr, ng = predict_both(r)
-            scenarios.append(("⏱️ If you followed up now (2h gap)", nr, ng))
-        if ec == 0:
-            r = build_input(ml, tone, aq, rt, si, 3, cl_val, user_type=ut)
-            nr, ng = predict_both(r)
-            scenarios.append(("😂 If you added 3 emojis", nr, ng))
-        r = build_input(max(ml, 120), 'enthusiastic', True, min(rt, 4), si, max(ec, 2), cl_val, user_type=ut)
-        nr, ng = predict_both(r)
-        scenarios.append(("🚀 Best case (all fixes applied)", nr, ng))
-
-        st.markdown("**How your odds change:**")
-        for label, nr, ng in scenarios:
-            rdiff = (nr - base_rp) * 100
-            gdiff = (ng - base_gp) * 100
-            rc = "whatif-boost" if rdiff > 0 else "whatif-drop"
-            gc = "whatif-drop"  if gdiff > 0 else "whatif-boost"
-            rs = "+" if rdiff >= 0 else ""; gs = "+" if gdiff >= 0 else ""
-            st.markdown(f"""
-            <div class='whatif-row'>
-                <span>{label}</span>
-                <span>
-                    <span class='{rc}'>Reply: {rs}{rdiff:.0f}%</span>
-                    &nbsp;|&nbsp;
-                    <span class='{gc}'>Ghost: {gs}{gdiff:.0f}%</span>
-                </span>
-            </div>
-            """, unsafe_allow_html=True)
-
-        st.markdown("<div style='color:#636e72;font-size:0.8em;margin-top:12px;'>All scenarios keep the rest of your inputs unchanged.</div>", unsafe_allow_html=True)
-
-
-# ══════════════════════════════════════════════════════════════════════════════
-# TAB 3 — MODEL METRICS
-# ════════════════════════════════════════��═════════════════════════════════════
-with tab3:
-    st.markdown("#### 🎓 Model Performance")
-    st.markdown("<div style='color:#636e72;font-size:0.88em;'>Two calibrated Random Forest models — reply prediction and ghost prediction. Evaluated on a clean held-out test set.</div>", unsafe_allow_html=True)
-
-    for key, label in [('reply', '📩 Reply Model'), ('ghosted', '👻 Ghost Model')]:
-        m = all_metrics[key]
-        st.markdown(f"### {label}")
-        mc = st.columns(5)
-        mc[0].markdown(f"<div class='m-card'><div class='m-num'>{m['accuracy']*100:.1f}%</div><div class='m-lbl'>Accuracy</div></div>", unsafe_allow_html=True)
-        mc[1].markdown(f"<div class='m-card'><div class='m-num'>{m['precision']*100:.1f}%</div><div class='m-lbl'>Precision</div></div>", unsafe_allow_html=True)
-        mc[2].markdown(f"<div class='m-card'><div class='m-num'>{m['recall']*100:.1f}%</div><div class='m-lbl'>Recall</div></div>", unsafe_allow_html=True)
-        mc[3].markdown(f"<div class='m-card'><div class='m-num'>{m['f1_score']:.3f}</div><div class='m-lbl'>F1 Score</div></div>", unsafe_allow_html=True)
-        mc[4].markdown(f"<div class='m-card'><div class='m-num'>{m['roc_auc']:.3f}</div><div class='m-lbl'>ROC-AUC</div></div>", unsafe_allow_html=True)
-
-        with st.expander(f"Confusion matrix & report — {label}", expanded=False):
-            e1, e2 = st.columns(2)
-            with e1:
-                cm_arr = np.array(m['confusion_matrix'])
-                fig, ax = plt.subplots(figsize=(4, 3))
-                sns.heatmap(cm_arr, annot=True, fmt='d', cmap='Blues',
-                            xticklabels=['No', 'Yes'], yticklabels=['No', 'Yes'],
-                            ax=ax, cbar=False, annot_kws={'size': 13, 'weight': 'bold'})
-                ax.set_xlabel('Predicted', color='white'); ax.set_ylabel('Actual', color='white')
-                ax.set_title('Confusion Matrix', color='white', fontsize=11)
-                fig.patch.set_facecolor('#1a1a2e'); ax.set_facecolor('#1a1a2e')
-                ax.tick_params(colors='white')
-                plt.tight_layout(); st.pyplot(fig, use_container_width=True)
-            with e2:
-                try:
-                    from sklearn.metrics import roc_curve
-                    fpr, tpr, _ = roc_curve(np.array(m['y_test']), np.array(m['y_pred_prob']))
-                    fig2, ax2 = plt.subplots(figsize=(4, 3))
-                    ax2.plot(fpr, tpr, color='#ee5a24', lw=2, label=f"AUC={m['roc_auc']:.3f}")
-                    ax2.plot([0,1],[0,1],'--',color='gray',lw=1)
-                    ax2.fill_between(fpr, tpr, alpha=0.12, color='#ee5a24')
-                    ax2.set_xlabel('FPR', color='white'); ax2.set_ylabel('TPR', color='white')
-                    ax2.set_title('ROC Curve', color='white', fontsize=11)
-                    ax2.legend(fontsize=9); ax2.tick_params(colors='white')
-                    ax2.set_facecolor('#1a1a2e'); fig2.patch.set_facecolor('#1a1a2e')
-                    plt.tight_layout(); st.pyplot(fig2, use_container_width=True)
-                except: pass
-            st.code(m['classification_report'], language=None)
-        st.divider()
-
-    st.markdown(f"""
-    <div style='background:rgba(255,255,255,0.04);border-radius:12px;padding:16px 20px;font-size:0.85em;color:#636e72;'>
-    <b>Architecture:</b> Random Forest (400 trees, depth=20, class_weight=balanced) + Sigmoid calibration (cv=3)<br>
-    <b>Split:</b> 70% train / 15% calibration val / 15% test — no data leakage between steps<br>
-    <b>Dataset:</b> 10,000 synthetic samples — ghosting_dataset5.csv<br>
-    <b>Features:</b> 20 numerical + 4 categorical (including user_type persona)
-    </div>
-    """, unsafe_allow_html=True)
-
-# ── Footer ─────────────────────────────────────────────────────────────────────
-st.markdown("""
-<div style='text-align:center;color:#636e72;margin-top:40px;padding:20px;font-size:0.85em;'>
-    <div style='margin-bottom:4px;'>💭 <i>You already know the answer. The AI just confirmed it.</i></div>
-    <div>Powered by Random Forest ML · Not liable for heartbreak 💔</div>
-</div>
-""", unsafe_allow_html=True)
-
-
-# again the same problem, after implementing this reply, the probabilities are changing with the inputs, they are not static...
+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+import os
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.calibration import CalibratedClassifierCV
+from sklearn.pipeline import Pipeline
+from sklearn.compose import ColumnTransformer
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.impute import SimpleImputer
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import (accuracy_score, precision_score, recall_score,
+                              f1_score, roc_auc_score, brier_score_loss,
+                              confusion_matrix, classification_report)
+import matplotlib.pyplot as plt
+import seaborn as sns
+import warnings
+warnings.filterwarnings('ignore')
+
+st.set_page_config(
+    page_title="💀 Ghosting Predictor",
+    page_icon="👻",
+    layout="wide",
+    initial_sidebar_state="collapsed"
+)
+
+st.markdown("""
+<style>
+@import url('https://fonts.googleapis.com/css2?family=Syne:wght@400;700;800&family=Inter:wght@400;500;600&display=swap');
+
+html, body, [class*="css"] { font-family: 'Inter', sans-serif; }
+h1, h2, h3 { font-family: 'Syne', sans-serif !important; }
+
+.main-title {
+    font-family: 'Syne', sans-serif; font-size: 3.0em; font-weight: 700;
+    color: #ff6b6b; /* Set text color to red */
+    text-align: center; margin-bottom: 20px; letter-spacing: 0.5px;
+}
+.sub-title { text-align: center; color: #b2bec3; font-size: 1.2em; margin-top: 10px; margin-bottom: 20px; }
+
+.verdict-card {
+    border-radius: 20px; padding: 30px; text-align: center;
+    margin: 20px auto; position: relative; overflow: hidden;
+    max-width: 700px;
+}
+.verdict-high  { background: linear-gradient(135deg, #00b894, #00cec9); color: white; }
+.verdict-mid   { background: linear-gradient(135deg, #fdcb6e, #e17055); color: white; }
+.verdict-low   { background: linear-gradient(135deg, #d63031, #6c5ce7); color: white; }
+.verdict-pct   { font-family: 'Syne', sans-serif; font-size: 3em; font-weight: 800; line-height: 1.2; }
+.verdict-label { font-size: 1.2em; font-weight: 600; margin-top: 10px; opacity: 0.9; }
+.verdict-quote { font-size: 1em; margin-top: 16px; font-style: italic; opacity: 0.85;
+                 border-top: 1px solid rgba(255,255,255,0.2); padding-top: 16px; }
+
+.diag-row { display: flex; gap: 20px; margin: 20px auto; flex-wrap: wrap; justify-content: center; }
+.diag-card {
+    flex: 1; min-width: 180px; max-width: 250px; border-radius: 15px; padding: 20px;
+    text-align: center; background: rgba(255,255,255,0.08); border: 1px solid rgba(255,255,255,0.1);
+}
+.diag-title { font-size: 0.9em; text-transform: uppercase; letter-spacing: 1px; color: #b2bec3; margin-bottom: 8px; }
+.diag-val   { font-family: 'Syne', sans-serif; font-size: 1.5em; font-weight: 700; white-space: nowrap; overflow: hidden; text-overflow: ellipsis; }
+.val-high   { color: #00b894; }
+.val-mid    { color: #fdcb6e; }
+.val-low    { color: #ff7675; }
+
+.flag-item {
+    display: flex; align-items: flex-start; gap: 12px;
+    padding: 12px 16px; border-radius: 12px; margin: 8px auto;
+    background: rgba(214, 48, 49, 0.15); border-left: 4px solid #d63031; font-size: 1em;
+    max-width: 700px;
+}
+.green-flag { background: rgba(0, 184, 148, 0.15); border-left: 4px solid #00b894; }
+
+.share-card {
+    background: linear-gradient(135deg, #1a1a2e, #16213e);
+    border-radius: 20px; padding: 30px; border: 1px solid rgba(255,255,255,0.1);
+    text-align: center; font-family: 'Syne', sans-serif;
+    max-width: 700px; margin: 30px auto;
+}
+.share-pct {
+    font-size: 3em; font-weight: 800;
+    background: linear-gradient(135deg, #ff6b6b, #ee5a24);
+    -webkit-background-clip: text; -webkit-text-fill-color: transparent;
+}
+.share-line { color: #dfe6e9; margin: 8px 0; font-size: 0.9em; }
+.share-tag  { color: #636e72; font-size: 0.85em; margin-top: 12px; }
+
+.m-card {
+    background: rgba(255,255,255,0.08); border-radius: 15px; padding: 20px; text-align: center;
+    border: 1px solid rgba(255,255,255,0.1); margin: 6px auto; max-width: 250px;
+}
+.m-num { font-family: 'Syne', sans-serif; font-size: 1.8em; font-weight: 800; color: #fdcb6e; }
+.m-lbl { font-size: 0.85em; color: #b2bec3; text-transform: uppercase; letter-spacing: 0.8px; }
+
+.stProgress > div > div { border-radius: 99px; }
+.block-container { padding-top: 3.5rem; max-width: 1300px; margin: auto; }
+</style>
+""", unsafe_allow_html=True)
+
+# ── Feature config ─────────────────────────────────────────────────────────────
+NUM_FEATURES = [
+    'last_message_length', 'response_time_gap', 'conversation_length',
+    'reply_ratio', 'avg_response_time', 'emoji_count', 'question_asked',
+    'seen_ignored', 'past_ghosting_history', 'effort_score', 'delay',
+    'is_dry', 'is_long_gap', 'engagement_score', 'ghost_risk_combo',
+    'seen_delay', 'initiator_flag', 'inconsistency', 'decay_score', 'effort_mismatch'
+]
+CAT_FEATURES = ['initiator', 'message_tone', 'time_of_day', 'user_type']
+
+def make_preprocessor():
+    num_t = Pipeline([('imp', SimpleImputer(strategy='median')), ('sc', StandardScaler())])
+    cat_t = Pipeline([('imp', SimpleImputer(strategy='most_frequent')),
+                      ('ohe', OneHotEncoder(handle_unknown='ignore'))])
+    return ColumnTransformer([('num', num_t, NUM_FEATURES), ('cat', cat_t, CAT_FEATURES)], remainder='drop')
+
+# ── Load / train models ────────────────────────────────────────────────────────
+@st.cache_resource
+def load_models():
+    rp = 'rf_reply_model.pkl'; gp = 'rf_ghost_model.pkl'; mp = 'model_metrics.pkl'
+    if os.path.exists(rp) and os.path.exists(gp) and os.path.exists(mp):
+        try:
+            return joblib.load(rp), joblib.load(gp), joblib.load(mp)
+        except Exception as e:
+            st.warning(f"⚠️ Saved models failed ({str(e)[:60]}). Retraining...")
+
+    with st.spinner("🤖 Training models... (~30 sec)"):
+        try:
+            df = pd.read_csv('ghosting_dataset5.csv')
+        except FileNotFoundError:
+            st.error("❌ ghosting_dataset5.csv not found. Run gen_data5.py first.")
+            st.stop()
+
+        df['effort_score']     = df['last_message_length'] + (df['emoji_count'] * 2) + (df['question_asked'] * 5)
+        df['delay']            = df['response_time_gap'].apply(lambda x: 0 if x < 6 else 1 if x < 24 else 2)
+        df['is_dry']           = (df['message_tone'] == 'dry').astype(int)
+        df['is_long_gap']      = (df['response_time_gap'] > 24).astype(int)
+        df['engagement_score'] = df['reply_ratio'] * df['conversation_length']
+        df['ghost_risk_combo'] = ((df['response_time_gap'] > 24) & (df['reply_ratio'] < 0.4)).astype(int)
+        df['seen_delay']       = ((df['seen_ignored'] == 1) & (df['response_time_gap'] > 12)).astype(int)
+        df['initiator_flag']   = (df['initiator'] == 'me').astype(int)
+        df['inconsistency']    = (abs(df['response_time_gap'] - df['avg_response_time']) > 20).astype(int)
+        df['decay_score']      = (df['conversation_length'] / 200).clip(0, 1)
+        df['effort_mismatch']  = ((df['last_message_length'] > 20) & (df['reply_ratio'] < 0.3)).astype(int)
+
+        def _train(df, target):
+            X = df[NUM_FEATURES + CAT_FEATURES]; y = df[target]
+            # ── Proper 3-way split ➺ no leakage ─────────────────────────────
+            X_tv, X_test, y_tv, y_test = train_test_split(X, y, test_size=0.15, random_state=42, stratify=y)
+            X_tr, X_val, y_tr, y_val   = train_test_split(X_tv, y_tv, test_size=0.15/0.85, random_state=42, stratify=y_tv)
+            mdl = Pipeline([('pre', make_preprocessor()),
+                            ('clf', RandomForestClassifier(n_estimators=400, max_depth=20,
+                                class_weight='balanced', random_state=42, n_jobs=-1))])
+            mdl.fit(X_tr, y_tr)
+            # Calibrate on val only; evaluate on test only
+            cal = CalibratedClassifierCV(mdl, method='sigmoid', cv=3)
+            cal.fit(X_val, y_val)
+            yp = cal.predict(X_test); yproba = cal.predict_proba(X_test)[:, 1]
+            return cal, {
+                'accuracy':  accuracy_score(y_test, yp),
+                'precision': precision_score(y_test, yp, zero_division=0),
+                'recall':    recall_score(y_test, yp, zero_division=0),
+                'f1_score':  f1_score(y_test, yp, zero_division=0),
+                'roc_auc':   roc_auc_score(y_test, yproba),
+                'brier':     brier_score_loss(y_test, yproba),
+                'confusion_matrix':      confusion_matrix(y_test, yp).tolist(),
+                'classification_report': classification_report(y_test, yp),
+                'train_size': len(X_tr), 'val_size': len(X_val), 'test_size': len(X_test),
+                'y_test': y_test.tolist(), 'y_pred_prob': yproba.tolist(),
+            }
+
+        rm, rmets = _train(df, 'reply')
+        gm, gmets = _train(df, 'ghosted')
+        joblib.dump(rm, rp); joblib.dump(gm, gp)
+        joblib.dump({'reply': rmets, 'ghosted': gmets}, mp)
+        return rm, gm, {'reply': rmets, 'ghosted': gmets}
+
+reply_model, ghost_model, all_metrics = load_models()
+
+# ── Feature builder ────────────────────────────────────────────────────────────
+def build_input(msg_len, tone, asked_q, resp_time, seen_ign, emoji,
+                conv_len=25, rr=None, avg_rt=None, past_ghost=0, user_type='casual'):
+    if rr is None:     rr     = 0.70 if asked_q else 0.50
+    if avg_rt is None: avg_rt = max(1.0, resp_time * 0.5)
+    tod = 'night' if resp_time > 20 else ('morning' if resp_time < 8 else 'day')
+    return pd.DataFrame({
+        'last_message_length':   [msg_len],
+        'response_time_gap':     [float(resp_time)],
+        'conversation_length':   [conv_len],
+        'reply_ratio':           [rr],
+        'avg_response_time':     [float(avg_rt)],
+        'emoji_count':           [emoji],
+        'question_asked':        [int(asked_q)],
+        'seen_ignored':          [seen_ign],
+        'past_ghosting_history': [past_ghost],
+        'effort_score':          [msg_len + (emoji * 2) + (5 if asked_q else 0)],
+        'delay':                 [0 if resp_time < 6 else (1 if resp_time < 24 else 2)],
+        'is_dry':                [int(tone == 'dry')],
+        'is_long_gap':           [int(resp_time > 24)],
+        'engagement_score':      [rr * conv_len],
+        'ghost_risk_combo':      [int(resp_time > 24 and rr < 0.4)],
+        'seen_delay':            [int(seen_ign == 1 and resp_time > 12)],
+        'initiator_flag':        [int(asked_q)],
+        'inconsistency':         [int(abs(resp_time - avg_rt) > 20)],
+        'decay_score':           [min(conv_len / 200, 1.0)],
+        'effort_mismatch':       [int(msg_len > 20 and rr < 0.3)],
+        'initiator':             ['me' if asked_q else 'them'],
+        'message_tone':          [tone],
+        'time_of_day':           [tod],
+        'user_type':             [user_type],
+    })
+
+def predict_both(row):
+    rp = reply_model.predict_proba(row)[0][1]
+    gp = ghost_model.predict_proba(row)[0][1]
+    return round(rp, 4), round(gp, 4)
+
+# ── Mode-adjusted probabilities ───────────────────────────────────────────────
+# The ML model gives one true probability. Each mode nudges it to tell a
+# coherent story consistent with that mode's personality:
+#   Savage:     slightly pessimistic ➺ surfaces the worst-case reading
+#   Emotional:  softens ghost risk, because the point is empathy not alarm
+#   Delusional: bumps reply up, tanks ghost risk ➺ the world is fine, always
+#   Normal:     raw model output, no adjustment
+#
+# Adjustments are additive deltas, clamped to [0.05, 0.95].
+# The base probability is always stored in session_state so switching modes
+# always starts from the same model output ➺ no drift across mode switches.
+
+MODE_PROB_DELTA = {
+    #              reply_delta  ghost_delta
+    'normal':    (  0.00,        0.00),
+    'savage':    ( -0.07,       +0.10),   # pessimistic ➺ "realistically, it's worse"
+    'emotional': ( +0.04,       -0.06),   # softer framing ➺ ghost risk feels lower
+    'delusional':( +0.15,       -0.18),   # copium ➺ everything looks fine
+}
+
+def apply_mode(base_rp, base_gp, mode):
+    rd, gd = MODE_PROB_DELTA[mode]
+    rp = max(0.05, min(0.95, base_rp + rd))
+    gp = max(0.05, min(0.95, base_gp + gd))
+    return round(rp, 4), round(gp, 4)
+
+# ── Mode-aware text ────────────────────────────────────────────────────────────
+# BUG FIX: All text that changes with mode must be derived AFTER reading mode
+# from session_state, and the cache key must include mode.
+
+MODE_QUOTES = {
+    'savage': {
+        'high':  ("Not bad. They might actually respond. Don't ruin it by double-texting.",
+                  "You're doing well. Shockingly."),
+        'mid':   ("50/50. A coin toss. Even randomness has standards.",
+                  "You're in the grey zone. Be honest ➺ you already know."),
+        'low':   ("They saw it. Chose silence. That's your answer.",
+                  "This isn't a delay. This is an exit."),
+        'ghost_high': "They're already gone. The AI just confirmed what you felt.",
+        'ghost_mid':  "It could go either way. But look at that response time.",
+        'ghost_low':  "Slim chance. Still a chance. Do with that what you will.",
+    },
+    'emotional': {
+        'high':  ("There's still warmth here. Don't give up on this connection 💛",
+                  "The signs are good. You deserve someone who shows up."),
+        'mid':   ("It's uncertain, and that uncertainty is exhausting. You're not alone in this.",
+                  "You deserve clarity. This situation doesn't give you that yet."),
+        'low':   ("It's okay to feel this. Silence hurts. Your feelings are valid.",
+                  "Sometimes people fade. That's not a reflection of your worth."),
+        'ghost_high': "This is hard to hear, but you already sensed something was off.",
+        'ghost_mid':  "The uncertainty is real. You deserve better than wondering.",
+        'ghost_low':  "There's still a thread here. But protect your heart either way.",
+    },
+    'delusional': {
+        'high':  ("They're DEFINITELY writing a 3-paragraph reply right now 🔥",
+                  "They literally can't stop thinking about you. Facts."),
+        'mid':   ("They're just playing it cool. They're SO into you. Obviously.",
+                  "This is called mystery. They're keeping you guessing because you're special."),
+        'low':   ("They're probably just in a coma. Or lost their phone. In the ocean.",
+                  "WiFi issues. 100%. They'll reply any second now. Any. Second."),
+        'ghost_high': "Ghost risk?? No no no. They're just... composing the perfect reply.",
+        'ghost_mid':  "The model is clearly broken. You two have something special.",
+        'ghost_low':  "See?? Low ghost risk. They adore you. Manifesting the reply rn.",
+    },
+    'normal': {
+        'high':  ("Good signs based on your inputs. Message has solid energy.",
+                  "The indicators are positive here."),
+        'mid':   ("This one could genuinely go either way. Hard to call.",
+                  "Mixed signals in the data ➺ reply is uncertain."),
+        'low':   ("The probability here is low based on current signals.",
+                  "Several risk factors are stacking up in this scenario."),
+        'ghost_high': "Multiple ghosting indicators are present.",
+        'ghost_mid':  "Some ghosting signals detected ➺ not conclusive.",
+        'ghost_low':  "Low ghosting probability based on the inputs.",
+    }
+}
+
+# BUG FIX: Final Verdict text must also be mode-aware
+VERDICT_TEXT = {
+    'normal': {
+        'clear_ok':    ("You're overcomplicating this. They'll reply.", "#00b894"),
+        'mixed':       ("Mixed signals. Reply likely but something feels off.", "#fdcb6e"),
+        'one_sided':   ("This is one-sided. You're investing more than they are.", "#e17055"),
+        'move_on':     ("Move on. The data agrees with your gut.", "#d63031"),
+        'uncertain':   ("It's uncertain. Give it one more day before deciding.", "#636e72"),
+    },
+    'savage': {
+        'clear_ok':    ("They'll reply. Don't sabotage it now.", "#00b894"),
+        'mixed':       ("Reply likely. Ghost possible. Classic mixed energy situation.", "#fdcb6e"),
+        'one_sided':   ("You're the only one putting in effort here. Read that again.", "#e17055"),
+        'move_on':     ("It's over. Your gut knew. Now you have data too.", "#d63031"),
+        'uncertain':   ("Genuinely unclear. But your anxiety already picked a side.", "#636e72"),
+    },
+    'emotional': {
+        'clear_ok':    ("There's real connection here. Let it breathe.", "#00b894"),
+        'mixed':       ("Something good is here, but something's also holding back.", "#fdcb6e"),
+        'one_sided':   ("You deserve reciprocity. This doesn't look balanced right now.", "#e17055"),
+        'move_on':     ("It's okay to let go. That's not giving up, it's self-respect.", "#d63031"),
+        'uncertain':   ("Uncertainty is painful. Whatever happens, you'll be okay.", "#636e72"),
+    },
+    'delusional': {
+        'clear_ok':    ("Obviously they'll reply. You two are basically soulmates.", "#00b894"),
+        'mixed':       ("The universe is just building tension before the plot twist 🌟", "#fdcb6e"),
+        'one_sided':   ("You're the main character. They're just processing their feelings.", "#e17055"),
+        'move_on':     ("'Move on'?? The AI doesn't understand your unique connection.", "#d63031"),
+        'uncertain':   ("The model is just shy. It doesn't understand romance.", "#636e72"),
+    },
+}
+
+def get_verdict_key(reply_prob, ghost_prob):
+    if reply_prob > 0.65 and ghost_prob < 0.40:  return 'clear_ok'
+    if reply_prob > 0.65 and ghost_prob >= 0.40: return 'mixed'
+    if reply_prob > 0.40:                         return 'one_sided'
+    if ghost_prob > 0.65:                         return 'move_on'
+    return 'uncertain'
+
+def get_quotes(mode, reply_prob, ghost_prob):
+    rb = 'high' if reply_prob > 0.65 else ('mid' if reply_prob > 0.40 else 'low')
+    gb = 'ghost_high' if ghost_prob > 0.65 else ('ghost_mid' if ghost_prob > 0.40 else 'ghost_low')
+    q = MODE_QUOTES[mode][rb]
+    return (q[0], q[1]), MODE_QUOTES[mode][gb]
+
+def interest_label(reply_prob):
+    if reply_prob > 0.70: return "HIGH", "val-high"
+    if reply_prob > 0.45: return "MEDIUM", "val-mid"
+    return "LOW", "val-low"
+
+def effort_label(msg_len, asked_q, emoji):
+    score = (msg_len / 50) + (2 if asked_q else 0) + (emoji * 0.3)
+    if score > 4: return "HIGH", "val-high"
+    if score > 2: return "BALANCED", "val-mid"
+    return "ONE-SIDED", "val-low"
+
+def ghost_risk_label(ghost_prob):
+    if ghost_prob > 0.65: return "HIGH", "val-low"
+    if ghost_prob > 0.40: return "MEDIUM", "val-mid"
+    return "LOW", "val-high"
+
+# ── Header ─────────────────────────────────────────────────────────────────────
+st.markdown("<div class='main-title'>💀 GHOSTING PREDICTOR</div>", unsafe_allow_html=True)
+st.markdown("<div class='sub-title'>AI-powered relationship reality check ➺ be honest, it already knows</div>", unsafe_allow_html=True)
+
+# ── Personality mode selector ──────────────────────────────────────────────────
+st.markdown("#### Choose your vibe")
+if "personality_mode" not in st.session_state:
+    st.session_state["personality_mode"] = "normal"
+
+mode_cols = st.columns(4)
+modes = [("🧠 Normal", "normal"), ("💀 Savage", "savage"), ("😭 Emotional", "emotional"), ("🤡 Delusional", "delusional")]
+for i, (lbl, key) in enumerate(modes):
+    with mode_cols[i]:
+        if st.button(lbl, use_container_width=True,
+                     type="primary" if st.session_state["personality_mode"] == key else "secondary"):
+            st.session_state["personality_mode"] = key
+            st.rerun()
+
+# Read mode ONCE here ➺ everything below uses this single variable
+mode = st.session_state["personality_mode"]
+
+mode_banner = {
+    "normal":     ("🧠 Normal Mode", "#636e72"),
+    "savage":     ("💀 Savage Mode ➺ No feelings were harmed. They were obliterated.", "#d63031"),
+    "emotional":  ("😭 Emotional Mode ➺ We see you. Your feelings are valid.", "#6c5ce7"),
+    "delusional": ("🤡 Delusional Mode ➺ Stay hopeful! (AI thinks you're cooked.)", "#e17055"),
+}
+banner_text, banner_color = mode_banner[mode]
+st.markdown(
+    f"<div style='text-align:center;background:{banner_color}22;border:1px solid {banner_color}55;"
+    f"border-radius:10px;padding:8px;font-size:1.5em;color:{banner_color};margin:8px 0 16px;'>"
+    f"{banner_text}</div>",
+    unsafe_allow_html=True
+)
+st.divider()
+
+# ── Tabs: 3 tabs only (message analyzer removed) ──────────────────────────────
+tab1, tab2, tab3 = st.tabs(["🔮 Predict", "📊 What-If", "🎓 Model Metrics"])
+
+# ══════════════════════════════════════════════════════════════════════════════
+# TAB 1 ➺ MAIN PREDICTION
+# ══════════════════════════════════════════════════════════════════════════════
+with tab1:
+    col1, col2 = st.columns(2, gap="large")
+
+    with col1:
+        st.markdown("#### 📱 Your message")
+        message_length = st.slider("Message length (chars)", 1, 500, 80, 5)
+        message_tone   = st.selectbox("Tone", ['dry', 'neutral', 'enthusiastic'], index=1)
+        asked_question = st.toggle("Asked a question?", value=True)
+        emoji_count    = st.slider("Emojis used", 0, 10, 1)
+        past_ghost     = st.toggle("Have they ghosted you before?", value=False)
+
+    with col2:
+        st.markdown("#### ⏱️ Their behaviour")
+        response_time  = st.slider("Hours since you sent it", 0, 72, 4, 1)
+        seen_raw       = st.radio("Did they see it?", ["👁️ Yes, seen", "❓ Not seen yet"], horizontal=True)
+        seen_ignored   = 1 if "Yes" in seen_raw else 0
+        conv_len       = st.slider("How long has the convo been? (messages)", 1, 200, 20)
+        user_type_map  = {
+            "😊 Seems interested": "interested",
+            "💬 Normal/casual":    "casual",
+            "🌵 Very dry texter":  "dry_texter",
+            "👻 Known to ghost":   "ghoster",
+        }
+        user_type_label = st.selectbox("How would you describe them?", list(user_type_map.keys()))
+        user_type       = user_type_map[user_type_label]
+
+    st.divider()
+
+    # ── Predictions ➺ cache key includes mode so text refreshes on mode change ──
+    # ikey tracks input changes only (not mode) ➺ model is only re-run when
+    # inputs change. Base probabilities are stored raw (no mode applied).
+    # Mode adjustment is applied on every render so switching mode instantly
+    # changes the displayed numbers without re-running the model.
+    ikey = (message_length, message_tone, asked_question, response_time,
+            seen_ignored, emoji_count, conv_len, user_type, int(past_ghost))
+
+    if st.session_state.get("ikey") != ikey:
+        try:
+            row = build_input(message_length, message_tone, asked_question,
+                              response_time, seen_ignored, emoji_count,
+                              conv_len=conv_len, past_ghost=int(past_ghost),
+                              user_type=user_type)
+            base_rp, base_gp = predict_both(row)
+        except Exception as e:
+            st.error(f"Prediction error: {e}")
+            base_rp, base_gp = 0.5, 0.4
+        st.session_state.update({"ikey": ikey, "base_rp": base_rp, "base_gp": base_gp})
+
+    # Apply mode delta on every render ➺ no model re-run needed
+    reply_prob, ghost_prob = apply_mode(
+        st.session_state["base_rp"],
+        st.session_state["base_gp"],
+        mode
+    )
+
+    # Derive ALL mode-dependent text here, after reading mode from session_state
+    (main_q, sub_q), ghost_q = get_quotes(mode, reply_prob, ghost_prob)
+    verdict_key = get_verdict_key(reply_prob, ghost_prob)
+    verdict_text, verdict_color = VERDICT_TEXT[mode][verdict_key]
+
+    # ── Dual verdict cards ────────────────────────────────────────────────────
+    vc1, vc2 = st.columns(2)
+    with vc1:
+        vclass = "verdict-high" if reply_prob > 0.65 else ("verdict-mid" if reply_prob > 0.40 else "verdict-low")
+        vlabel = "They'll reply 🔥" if reply_prob > 0.65 else ("Could go either way 😬" if reply_prob > 0.40 else "They're ghosting you 💀")
+        st.markdown(f"""
+        <div class='verdict-card {vclass}'>
+            <div style='font-size:0.8em;font-weight:600;opacity:0.8;text-transform:uppercase;letter-spacing:1px;'>Reply probability</div>
+            <div class='verdict-pct'>{reply_prob*100:.0f}%</div>
+            <div class='verdict-label'>{vlabel}</div>
+            <div class='verdict-quote'>"{main_q}"</div>
+        </div>
+        """, unsafe_allow_html=True)
+
+    with vc2:
+        gclass = "verdict-low" if ghost_prob > 0.65 else ("verdict-mid" if ghost_prob > 0.40 else "verdict-high")
+        glabel = "High ghost risk 💀" if ghost_prob > 0.65 else ("Uncertain 😬" if ghost_prob > 0.40 else "Probably fine 🙂")
+        st.markdown(f"""
+        <div class='verdict-card {gclass}'>
+            <div style='font-size:0.8em;font-weight:600;opacity:0.8;text-transform:uppercase;letter-spacing:1px;'>Ghost probability</div>
+            <div class='verdict-pct'>{ghost_prob*100:.0f}%</div>
+            <div class='verdict-label'>{glabel}</div>
+            <div class='verdict-quote'>"{ghost_q}"</div>
+        </div>
+        """, unsafe_allow_html=True)
+
+    # ── Conversation Diagnosis ────────────────────────────────────────────────
+    # BUG FIX: Diagnosis values are derived from ML probabilities (correct),
+    # but the Read Status now also reflects mode tone
+    st.markdown("#### 🧠 Conversation Diagnosis")
+    int_lbl, int_cls = interest_label(reply_prob)
+    eff_lbl, eff_cls = effort_label(message_length, asked_question, emoji_count)
+    gr_lbl,  gr_cls  = ghost_risk_label(ghost_prob)
+
+    # Read status changes with mode (delusional gives an excuse, savage is blunt)
+    if seen_ignored and response_time > 6:
+        seen_txt = {
+            'normal':    "IGNORED 🚨",
+            'savage':    "SEEN. IGNORED. 💀",
+            'emotional': "SEEN, NO REPLY 💔",
+            'delusional':"SEEN (composing!!) ✍️",
+        }[mode]
+        seen_cls = "val-low"
+    elif seen_ignored:
+        seen_txt = "SEEN ✓"; seen_cls = "val-mid"
+    else:
+        seen_txt = "NOT SEEN"; seen_cls = "val-mid"
+
+    st.markdown(f"""
+    <div class='diag-row'>
+        <div class='diag-card'>
+            <div class='diag-title'>Interest Level</div>
+            <div class='diag-val {int_cls}'>{int_lbl}</div>
+        </div>
+        <div class='diag-card'>
+            <div class='diag-title'>Effort Balance</div>
+            <div class='diag-val {eff_cls}'>{eff_lbl}</div>
+        </div>
+        <div class='diag-card'>
+            <div class='diag-title'>Ghost Risk</div>
+            <div class='diag-val {gr_cls}'>{gr_lbl}</div>
+        </div>
+        <div class='diag-card'>
+            <div class='diag-title'>Read Status</div>
+            <div class='diag-val {seen_cls}'>{seen_txt}</div>
+        </div>
+    </div>
+    """, unsafe_allow_html=True)
+
+    # ── Signal Breakdown ──────────────────────────────────────────────────────
+    st.markdown("#### 🚩 Signal Breakdown")
+    fc1, fc2 = st.columns(2)
+
+    red_flags, green_flags = [], []
+    if seen_ignored and response_time > 6:   red_flags.append("They saw your message. They chose silence.")
+    if response_time > 48:                   red_flags.append(f"It's been {response_time}h. That's not busy, that's avoidance.")
+    elif response_time > 24:                 red_flags.append("Over 24 hours ➺ the energy is cooling off.")
+    if message_tone == 'dry':                red_flags.append("Dry tone doesn't open doors.")
+    if not asked_question:                   red_flags.append("No question = no reason to reply.")
+    if message_length < 30:                  red_flags.append("Short message ➺ looks like low effort.")
+    if user_type == 'ghoster':               red_flags.append("You described them as a known ghoster. That's data.")
+    if past_ghost:                           red_flags.append("They've ghosted you before. Pattern recognised.")
+    if emoji_count == 0 and message_tone == 'dry': red_flags.append("Zero warmth signals in this message.")
+
+    if asked_question:                       green_flags.append("Asked a question ➺ gives them something to respond to.")
+    if message_length > 100:                 green_flags.append("Substantial message ➺ shows you put in effort.")
+    if message_tone == 'enthusiastic':       green_flags.append("Enthusiastic tone ➺ energy is contagious.")
+    if response_time < 12:                   green_flags.append("Sent recently ➺ they still might be composing a reply.")
+    if emoji_count > 0:                      green_flags.append("Used emojis ➺ lightens the vibe.")
+    if user_type == 'interested':            green_flags.append("You described them as interested ➺ that matters.")
+    if not past_ghost:                       green_flags.append("No ghosting history ➺ fresh start.")
+
+    with fc1:
+        st.markdown("**Red flags**")
+        for f in red_flags:
+            st.markdown(f"<div class='flag-item'>🚩 {f}</div>", unsafe_allow_html=True)
+        if not red_flags:
+            st.markdown("<div class='flag-item green-flag'>✅ No major red flags detected.</div>", unsafe_allow_html=True)
+
+    with fc2:
+        st.markdown("**Green flags**")
+        for g in green_flags:
+            st.markdown(f"<div class='flag-item green-flag'>✅ {g}</div>", unsafe_allow_html=True)
+        if not green_flags:
+            st.markdown("<div class='flag-item'>🚩 Hmm, not many positives here.</div>", unsafe_allow_html=True)
+
+    st.divider()
+
+    # ── Final Verdict ➺ mode-aware ────────────────────────────────────────────
+    # BUG FIX: verdict_text and verdict_color now come from VERDICT_TEXT[mode]
+    st.markdown("#### 🎯 Final Verdict")
+    st.markdown(
+        f"<div style='background:{verdict_color}22;border-left:4px solid {verdict_color};"
+        f"border-radius:0 12px 12px 0;padding:16px 20px;margin:10px 0;"
+        f"font-family:Syne,sans-serif;font-size:1.1em;color:{verdict_color};font-weight:600;'>"
+        f"{verdict_text}</div>",
+        unsafe_allow_html=True
+    )
+    if sub_q:
+        st.markdown(
+            f"<div style='color:#b2bec3;font-style:italic;font-size:0.9em;margin-top:8px;'>💭 {sub_q}</div>",
+            unsafe_allow_html=True
+        )
+
+    st.divider()
+
+    # ── Shareable card ────────────────────────────────────────────────────────
+    st.markdown("#### 📸 Share Your Result")
+    share_label = "They'll probably reply 🔥" if reply_prob > 0.65 else ("It's a coin flip 😬" if reply_prob > 0.40 else "Ghosting incoming 💀")
+    ghost_label = "Ghost risk: HIGH 💀" if ghost_prob > 0.65 else ("Ghost risk: MEDIUM ⚠️" if ghost_prob > 0.40 else "Ghost risk: LOW ✅")
+
+    st.markdown(f"""
+    <div class='share-card'>
+        <div style='font-size:1.75em;letter-spacing:2px;color:#636e72;text-transform:uppercase;margin-bottom:8px;'>AI Reality Check</div>
+        <div class='share-pct'>{reply_prob*100:.0f}%</div>
+        <div class='share-line' style='font-size:1.2em;font-weight:700;'>{share_label}</div>
+        <div class='share-line' style='color:#b2bec3;'>{ghost_label}</div>
+        <div class='share-line' style='font-style:italic;color:#dfe6e9;margin-top:10px;font-size:1.95em;'>"{main_q}"</div>
+        <div class='share-tag'>#GhostingPredictor • ghostingpredictor.app</div>
+    </div>
+    """, unsafe_allow_html=True)
+
+    share_text = (f"💀 Ghosting Predictor says:\n"
+                  f"Reply chance: {reply_prob*100:.0f}% ➺ {share_label}\n"
+                  f"{ghost_label}\n"
+                  f'"{main_q}"\n'
+                  f"#GhostingPredictor #AI #Dating")
+
+    # FIX: st.button causes a full page rerun which resets widget defaults →
+    # changes ikey → triggers fresh prediction with default inputs → wrong %.
+    # Solution: always render the share text in a st.text_area (read-only style).
+    # st.text_area does NOT trigger a rerun when the user clicks inside it to
+    # select/copy ➺ it only reruns on actual value change, which can't happen
+    # because the value is set programmatically and the user just selects text.
+    st.markdown("<div style='font-size:0.82em;color:#b2bec3;margin-bottom:4px;'>📋 Click inside, Ctrl+A, Ctrl+C to copy:</div>", unsafe_allow_html=True)
+    st.text_area(
+        label="share_text_area",
+        value=share_text,
+        height=120,
+        label_visibility="collapsed",
+        key=f"share_ta_{hash(share_text)}",   # stable key tied to content, not mode
+    )
+    st.markdown("<div style='text-align:center;font-size:1.8em;color:#636e72;margin-top:4px;'>📸 Or screenshot the card above and post it</div>", unsafe_allow_html=True)
+
+    st.markdown("<div style='text-align:center;margin-top:12px;font-size:0.85em;color:#636e72;'>Drop your situation in the comments ➺ I'll tell you what the model says 👇</div>", unsafe_allow_html=True)
+
+# ══════════════════════════════════════════════════════════════════════════════
+# TAB 2 ➺ WHAT-IF SIMULATOR
+# ══════════════════════════════════════════════════════════════════════════════
+with tab2:
+    st.markdown("#### 📊 What-If Simulator")
+    st.markdown("<div style='color:#636e72;font-size:1.5em;'>See how your odds change if you tweak one thing. Experiment freely.</div>", unsafe_allow_html=True)
+
+    if "base_rp" not in st.session_state:
+        st.info("Go to the **Predict** tab first to set your base scenario.")
+    else:
+        base_rp, base_gp = apply_mode(
+            st.session_state["base_rp"],
+            st.session_state["base_gp"],
+            mode
+        )
+        ml, tone, aq, rt, si, ec, cl_val, ut, pg = st.session_state["ikey"]
+
+        rclr = "#00b894" if base_rp > 0.65 else ("#fdcb6e" if base_rp > 0.4 else "#ff7675")
+        gclr = "#ff7675" if base_gp > 0.65 else ("#fdcb6e" if base_gp > 0.4 else "#00b894")
+        st.markdown(f"""
+        <div style='background:rgba(255,255,255,0.05);border-radius:12px;padding:14px 18px;margin-bottom:16px;'>
+            <div style='font-size:0.8em;color:#b2bec3;text-transform:uppercase;letter-spacing:1px;'>Your current situation</div>
+            <div style='font-family:Syne,sans-serif;font-size:1.6em;font-weight:700;'>
+                Reply: <span style='color:{rclr}'>{base_rp*100:.0f}%</span>
+                &nbsp;&nbsp; Ghost: <span style='color:{gclr}'>{base_gp*100:.0f}%</span>
+            </div>
+        </div>
+        """, unsafe_allow_html=True)
+
+        scenarios = []
+        if not aq:
+            r = build_input(ml, tone, True, rt, si, ec, cl_val, user_type=ut)
+            nr, ng = predict_both(r)
+            scenarios.append(("❓ If you added a question", nr, ng))
+        if tone != 'enthusiastic':
+            r = build_input(ml, 'enthusiastic', aq, rt, si, ec, cl_val, user_type=ut)
+            nr, ng = predict_both(r)
+            scenarios.append(("😄 If your tone was enthusiastic", nr, ng))
+        if ml < 150:
+            r = build_input(150, tone, aq, rt, si, ec, cl_val, user_type=ut)
+            nr, ng = predict_both(r)
+            scenarios.append(("📝 If your message was longer (150 chars)", nr, ng))
+        if rt > 12:
+            r = build_input(ml, tone, aq, 2, si, ec, cl_val, user_type=ut)
+            nr, ng = predict_both(r)
+            scenarios.append(("⏱️ If you followed up now (2h gap)", nr, ng))
+        if ec == 0:
+            r = build_input(ml, tone, aq, rt, si, 3, cl_val, user_type=ut)
+            nr, ng = predict_both(r)
+            scenarios.append(("😂 If you added 3 emojis", nr, ng))
+        r = build_input(max(ml, 120), 'enthusiastic', True, min(rt, 4), si, max(ec, 2), cl_val, user_type=ut)
+        nr, ng = predict_both(r)
+        scenarios.append(("🚀 Best case (all fixes applied)", nr, ng))
+
+        st.markdown("**How your odds change:**")
+        for label, nr, ng in scenarios:
+            rdiff = (nr - base_rp) * 100
+            gdiff = (ng - base_gp) * 100
+            rc = "whatif-boost" if rdiff > 0 else "whatif-drop"
+            gc = "whatif-drop"  if gdiff > 0 else "whatif-boost"
+            rs = "+" if rdiff >= 0 else ""; gs = "+" if gdiff >= 0 else ""
+            st.markdown(f"""
+            <div class='whatif-row'>
+                <span>{label}</span>
+                <span>
+                    <span class='{rc}'>Reply: {rs}{rdiff:.0f}%</span>
+                    &nbsp;|&nbsp;
+                    <span class='{gc}'>Ghost: {gs}{gdiff:.0f}%</span>
+                </span>
+            </div>
+            """, unsafe_allow_html=True)
+
+        st.markdown("<div style='color:#636e72;font-size:1.5em;margin-top:12px;'>All scenarios keep the rest of your inputs unchanged.</div>", unsafe_allow_html=True)
+
+
+# ══════════════════════════════════════════════════════════════════════════════
+# TAB 3 ➺ MODEL METRICS
+# ══════════════════════════════════════════════════════════════════════════════
+with tab3:
+    st.markdown("#### 🎓 Model Performance")
+    st.markdown("<div style='color:#636e72;font-size:1.5em;'>Two calibrated Random Forest models ➺ reply prediction and ghost prediction. Evaluated on a clean held-out test set.</div>", unsafe_allow_html=True)
+
+    for key, label in [('reply', '📩 Reply Model'), ('ghosted', '👻 Ghost Model')]:
+        m = all_metrics[key]
+        st.markdown(f"### {label}")
+        mc = st.columns(5)
+        mc[0].markdown(f"<div class='m-card'><div class='m-num'>{m['accuracy']*100:.1f}%</div><div class='m-lbl'>Accuracy</div></div>", unsafe_allow_html=True)
+        mc[1].markdown(f"<div class='m-card'><div class='m-num'>{m['precision']*100:.1f}%</div><div class='m-lbl'>Precision</div></div>", unsafe_allow_html=True)
+        mc[2].markdown(f"<div class='m-card'><div class='m-num'>{m['recall']*100:.1f}%</div><div class='m-lbl'>Recall</div></div>", unsafe_allow_html=True)
+        mc[3].markdown(f"<div class='m-card'><div class='m-num'>{m['f1_score']:.3f}</div><div class='m-lbl'>F1 Score</div></div>", unsafe_allow_html=True)
+        mc[4].markdown(f"<div class='m-card'><div class='m-num'>{m['roc_auc']:.3f}</div><div class='m-lbl'>ROC-AUC</div></div>", unsafe_allow_html=True)
+
+        with st.expander(f"Confusion matrix & report ➺ {label}", expanded=False):
+            e1, e2 = st.columns(2)
+            with e1:
+                cm_arr = np.array(m['confusion_matrix'])
+                fig, ax = plt.subplots(figsize=(4, 3))
+                sns.heatmap(cm_arr, annot=True, fmt='d', cmap='Blues',
+                            xticklabels=['No', 'Yes'], yticklabels=['No', 'Yes'],
+                            ax=ax, cbar=False, annot_kws={'size': 13, 'weight': 'bold'})
+                ax.set_xlabel('Predicted', color='white'); ax.set_ylabel('Actual', color='white')
+                ax.set_title('Confusion Matrix', color='white', fontsize=11)
+                fig.patch.set_facecolor('#1a1a2e'); ax.set_facecolor('#1a1a2e')
+                ax.tick_params(colors='white')
+                plt.tight_layout(); st.pyplot(fig, use_container_width=True)
+            with e2:
+                try:
+                    from sklearn.metrics import roc_curve
+                    fpr, tpr, _ = roc_curve(np.array(m['y_test']), np.array(m['y_pred_prob']))
+                    fig2, ax2 = plt.subplots(figsize=(4, 3))
+                    ax2.plot(fpr, tpr, color='#ee5a24', lw=2, label=f"AUC={m['roc_auc']:.3f}")
+                    ax2.plot([0,1],[0,1],'--',color='gray',lw=1)
+                    ax2.fill_between(fpr, tpr, alpha=0.12, color='#ee5a24')
+                    ax2.set_xlabel('FPR', color='white'); ax2.set_ylabel('TPR', color='white')
+                    ax2.set_title('ROC Curve', color='white', fontsize=11)
+                    ax2.legend(fontsize=9); ax2.tick_params(colors='white')
+                    ax2.set_facecolor('#1a1a2e'); fig2.patch.set_facecolor('#1a1a2e')
+                    plt.tight_layout(); st.pyplot(fig2, use_container_width=True)
+                except: pass
+            st.code(m['classification_report'], language=None)
+        st.divider()
+
+    st.markdown(f"""
+    <div style='background:rgba(255,255,255,0.04);border-radius:12px;padding:16px 20px;font-size:1.0em;color:#636e72;'>
+    <b>Architecture:</b> Random Forest (400 trees, depth=20, class_weight=balanced) + Sigmoid calibration (cv=3)<br>
+    <b>Split:</b> 70% train / 15% calibration val / 15% test ➺ no data leakage between steps<br>
+    <b>Dataset:</b> 10,000 synthetic samples ➺ ghosting_dataset5.csv<br>
+    <b>Features:</b> 20 numerical + 4 categorical (including user_type persona)
+    </div>
+    """, unsafe_allow_html=True)
+
+# ── Footer ─────────────────────────────────────────────────────────────────────
+st.markdown("""
+<div style='text-align:center;color:#636e72;margin-top:40px;padding:20px;font-size:1.85em;'>
+    <div style='margin-bottom:4px;'>💭 <i>You already know the answer. The AI just confirmed it.</i></div>
+    <div>Powered by Random Forest ML · Not liable for heartbreak 💔</div>
+</div>
+""", unsafe_allow_html=True)