| 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; } |
| |
| /* Mode buttons — uniform dark style */ |
| div[data-testid="stHorizontalBlock"] .stButton > button { |
| background: rgba(255,255,255,0.06) !important; |
| color: #b2bec3 !important; |
| border: 1px solid rgba(255,255,255,0.12) !important; |
| border-radius: 10px !important; |
| font-weight: 600 !important; |
| box-shadow: none !important; |
| transition: background 0.15s ease, border-color 0.15s ease, color 0.15s ease !important; |
| } |
| |
| /* Hover effect for all buttons */ |
| div[data-testid="stHorizontalBlock"] .stButton > button:hover { |
| background: rgba(255,255,255,0.12) !important; |
| border-color: rgba(255,255,255,0.28) !important; |
| color: #fff !important; |
| box-shadow: none !important; |
| } |
| |
| /* Active mode button — dynamic color based on mode */ |
| {''.join([f'div[data-testid="stHorizontalBlock"] .stButton > button[kind="primary"][data-baseweb="{key}"] {{\n background: {color} !important;\n border: 1px solid {color} !important;\n color: #fff !important;\n box-shadow: 0 0 12px {color} !important;\n}}\n' for key, color in zip(['normal', 'savage', 'emotional', 'delusional'], ['#c0392b', '#8e44ad', '#3498db', '#e67e22'])])} |
| </style> |
| """, unsafe_allow_html=True) |
|
|
| |
| 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') |
|
|
| |
| @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] |
| |
| 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) |
| |
| 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() |
|
|
| |
| 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_PROB_DELTA = { |
| |
| 'normal': ( 0.00, 0.00), |
| 'savage': ( -0.07, +0.10), |
| 'emotional': ( +0.04, -0.06), |
| 'delusional':( +0.15, -0.18), |
| } |
|
|
| 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_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.", |
| } |
| } |
|
|
| |
| 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" |
|
|
| |
| 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) |
|
|
| |
| 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"): |
| if st.session_state.get("last_clicked") == key: |
| |
| st.session_state["personality_mode"] = "savage" |
| else: |
| st.session_state["personality_mode"] = key |
| st.session_state["last_clicked"] = key |
| |
| |
| |
| |
|
|
| |
| 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() |
|
|
| |
| tab1, tab2, tab3 = st.tabs(["🔮 Predict", "📊 What-If", "🎓 Model Metrics"]) |
|
|
| |
| |
| |
| 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() |
|
|
| |
| |
| |
| |
| |
| ikey = (message_length, message_tone, asked_question, response_time, |
| seen_ignored, emoji_count, conv_len, user_type, int(past_ghost)) |
|
|
| |
| needs_predict = ( |
| st.session_state.get("ikey") != ikey |
| or "base_rp" not in st.session_state |
| or "base_gp" not in st.session_state |
| ) |
|
|
| if needs_predict: |
| 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}) |
|
|
| |
| reply_prob, ghost_prob = apply_mode( |
| st.session_state["base_rp"], |
| st.session_state["base_gp"], |
| mode |
| ) |
|
|
| |
| (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] |
|
|
| |
| 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) |
|
|
| |
| |
| |
| 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) |
|
|
| |
| 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) |
|
|
| |
| 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() |
|
|
| |
| |
| 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() |
|
|
| |
| 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") |
|
|
| |
| |
| |
| |
| |
| |
| 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)}", |
| ) |
| 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) |
|
|
| |
| |
| |
| 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> |
| 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> |
| | |
| <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) |
|
|
|
|
| |
| |
| |
| 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) |
|
|
| |
| 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) |
