Update app.py

app.py

@@ -149,4 +149,391 @@ SUGGESTIONS = {
         "Gratitude softens fear; it reminds you what’s still good.",
         "Send a short thank-you message to someone right now.",
         "Appreciate how far you’ve already come — quietly, just for you.",
-    ]
+    ],
+    "neutral": [
+        "Take one slow, deep breath. That’s a start.",
+        "Not every moment has to be meaningful — existing is enough.",
+        "Sometimes calm feels empty because we’re used to noise — rest in it.",
+        "Stretch for 30 seconds and notice your body waking up.",
+        "Drink water; your brain loves that.",
+        "Sit still for one minute — that’s all you need.",
+        "Neutral moments are where balance grows.",
+        "Doing nothing for a while is still doing something.",
+    ],
+}
+
+# --- Inspirational / comforting quotes & affirmations ---
+QUOTES = {
+    "sadness": [
+        "“Even the darkest night will end and the sun will rise.” – Victor Hugo",
+        "“You don’t have to feel better to start healing.”",
+        "“It’s okay to be lost for a while.”",
+        "“Tears are words the heart can’t express.” – Paulo Coelho",
+        "“You have survived every hard day so far.”",
+    ],
+    "fear": [
+        "“Feel the fear and do it anyway.” – Susan Jeffers",
+        "“Courage is not the absence of fear, but acting in spite of it.”",
+        "“You’ve faced hard things before — you can again.”",
+        "“This moment will not last forever.”",
+    ],
+    "joy": [
+        "“Happiness is not out there, it’s in you.”",
+        "“Let joy be your rebellion.”",
+        "“Enjoy the little things — one day you’ll realize they were the big things.”",
+        "“Joy shared is joy doubled.”",
+    ],
+    "anger": [
+        "“Speak when you are angry and you’ll make the best speech you’ll ever regret.” – Ambrose Bierce",
+        "“Peace begins with a pause.”",
+        "“Anger is energy — learn to guide it, not suppress it.”",
+    ],
+    "boredom": [
+        "“Boredom is the beginning of imagination.” – Jules Renard",
+        "“Curiosity is the cure for boredom.” – Dorothy Parker",
+        "“The small things done repeatedly change everything.”",
+    ],
+    "grief": [
+        "“Grief is love that has nowhere to go.”",
+        "“What we once enjoyed we can never lose; all that we love deeply becomes part of us.” – Helen Keller",
+        "“Love doesn’t end, it changes form.”",
+    ],
+    "love": [
+        "“Where there is love, there is life.” – Mahatma Gandhi",
+        "“You are loved just for being who you are.” – Ram Dass",
+        "“Love quietly transforms everything it touches.”",
+    ],
+    "nervousness": [
+        "“You don’t have to control your thoughts; just stop letting them control you.” – Dan Millman",
+        "“Breathe. You are doing enough.”",
+        "“This worry does not define you.”",
+    ],
+    "curiosity": [
+        "“Stay curious — it’s the mind’s way of loving life.”",
+        "“Wonder is wisdom’s beginning.” – Socrates",
+        "“Every question plants a seed.”",
+    ],
+    "gratitude": [
+        "“Gratitude turns what we have into enough.”",
+        "“The more grateful I am, the more beauty I see.” – Mary Davis",
+        "“Thankfulness unlocks joy.”",
+    ],
+    "neutral": [
+        "“Be present — even a calm moment can be a quiet victory.”",
+        "“Peace is not the absence of chaos, but the presence of inner calm.”",
+        "“Slow is smooth, smooth is peaceful.”",
+    ],
+}
+
+COLOR_MAP = {
+    "joy": "#FFF9C4", "love": "#F8BBD0", "gratitude": "#FFF176",
+    "sadness": "#BBDEFB", "grief": "#B3E5FC",
+    "fear": "#E1BEE7", "nervousness": "#E1BEE7",
+    "anger": "#FFCCBC", "boredom": "#E0E0E0",
+    "neutral": "#F5F5F5",
+}
+
+# Map GoEmotions label -> your UI buckets
+GOEMO_TO_APP = {
+    "admiration": "gratitude",
+    "amusement": "joy",
+    "anger": "anger",
+    "annoyance": "anger",
+    "approval": "gratitude",
+    "caring": "love",
+    "confusion": "nervousness",
+    "curiosity": "curiosity",
+    "desire": "joy",
+    "disappointment": "sadness",
+    "disapproval": "anger",
+    "disgust": "anger",
+    "embarrassment": "nervousness",
+    "excitement": "joy",
+    "fear": "fear",
+    "gratitude": "gratitude",
+    "grief": "grief",
+    "joy": "joy",
+    "love": "love",
+    "nervousness": "nervousness",
+    "optimism": "joy",
+    "pride": "joy",
+    "realization": "neutral",
+    "relief": "gratitude",
+    "remorse": "grief",
+    "sadness": "sadness",
+    "surprise": "neutral",
+    "neutral": "neutral",
+}
+
+THRESHOLD = 0.30  # probability threshold for selecting labels
+
+# ---------------- SQLite helpers ----------------
+def get_conn():
+    return sqlite3.connect(DB_PATH, check_same_thread=False, timeout=10)
+
+def init_db():
+    conn = None
+    try:
+        conn = get_conn()
+        c = conn.cursor()
+        c.execute("""
+        CREATE TABLE IF NOT EXISTS sessions(
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            ts TEXT,
+            country TEXT,
+            user_text TEXT,
+            main_emotion TEXT
+        )
+        """)
+        conn.commit()
+    finally:
+        try:
+            if conn: conn.close()
+        except Exception:
+            pass
+
+def log_session(country, msg, emotion):
+    conn = None
+    try:
+        conn = get_conn()
+        c = conn.cursor()
+        c.execute(
+            "INSERT INTO sessions(ts, country, user_text, main_emotion) VALUES(?,?,?,?)",
+            (datetime.utcnow().isoformat(timespec="seconds"), country, msg[:500], emotion),
+        )
+        conn.commit()
+    finally:
+        try:
+            if conn: conn.close()
+        except Exception:
+            pass
+
+# ---------------- Train / Load model from DATASET ONLY ----------------
+def load_goemotions_dataset():
+    # "simplified" gives 'text' and 'labels' as list[int] indices
+    ds = load_dataset("google-research-datasets/go_emotions", "simplified")
+    label_names = ds["train"].features["labels"].feature.names
+    return ds, label_names
+
+def _prepare_xy(split):
+    # Each example has text and labels (list of ints)
+    X = split["text"]
+    y = split["labels"]  # list[list[int]]
+    return X, y
+
+def train_or_load_model():
+    # Try cache first
+    if os.path.isfile(MODEL_PATH):
+        print("[MM] Loading cached classifier...")
+        bundle = joblib.load(MODEL_PATH)
+        if bundle.get("version") == MODEL_VERSION:
+            return bundle["pipeline"], bundle["mlb"], bundle["label_names"]
+        else:
+            print("[MM] Cached model version mismatch; retraining...")
+
+    print("[MM] Loading GoEmotions dataset...")
+    ds, label_names = load_goemotions_dataset()
+
+    print("[MM] Preparing data...")
+    X_train, y_train_idx = _prepare_xy(ds["train"])
+    X_val, y_val_idx = _prepare_xy(ds["validation"])
+
+    # MultiLabelBinarizer to convert list[int] -> multi-hot
+    mlb = MultiLabelBinarizer(classes=list(range(len(label_names))))
+    Y_train = mlb.fit_transform(y_train_idx)
+    Y_val   = mlb.transform(y_val_idx)
+
+    # Build pipeline
+    clf = Pipeline(steps=[
+        ("tfidf", TfidfVectorizer(
+            lowercase=True,
+            ngram_range=(1,2),
+            min_df=2,
+            max_df=0.9,
+            strip_accents="unicode",
+        )),
+        ("ovr", OneVsRestClassifier(
+            LogisticRegression(
+                solver="saga",
+                max_iter=1000,
+                n_jobs=-1,
+                class_weight="balanced",
+            ),
+            n_jobs=-1
+        ))
+    ])
+
+    print("[MM] Training classifier (this happens once; cached afterward)...")
+    clf.fit(X_train, Y_train)
+
+    # Quick validation metric (macro F1 over labels present in val)
+    Y_val_pred = clf.predict(X_val)
+    macro_f1 = f1_score(Y_val, Y_val_pred, average="macro", zero_division=0)
+    print(f"[MM] Validation macro F1: {macro_f1:.3f}")
+
+    # Cache model
+    joblib.dump({
+        "version": MODEL_VERSION,
+        "pipeline": clf,
+        "mlb": mlb,
+        "label_names": label_names
+    }, MODEL_PATH)
+    print(f"[MM] Saved classifier to {MODEL_PATH}")
+
+    return clf, mlb, label_names
+
+# Train/load at startup
+try:
+    CLASSIFIER, MLB, LABEL_NAMES = train_or_load_model()
+except Exception as e:
+    print(f"[WARN] Failed to train/load classifier: {e}")
+    CLASSIFIER, MLB, LABEL_NAMES = None, None, None
+
+# ---------------- Inference using ONLY the trained classifier ----------------
+def classify_text(text: str):
+    """
+    Returns list of (label_name, prob) for labels above THRESHOLD, sorted desc.
+    """
+    if not CLASSIFIER or not MLB or not LABEL_NAMES:
+        return []
+
+    # predict_proba returns array shape (1, n_labels)
+    try:
+        proba = CLASSIFIER.predict_proba([text])[0]
+    except AttributeError:
+        # If estimator doesn't support predict_proba (shouldn't happen with LR),
+        # fall back to decision_function -> sigmoid
+        from scipy.special import expit
+        scores = CLASSIFIER.decision_function([text])[0]
+        proba = expit(scores)
+
+    idxs = [i for i, p in enumerate(proba) if p >= THRESHOLD]
+    # Sort by probability desc
+    idxs.sort(key=lambda i: proba[i], reverse=True)
+    return [(LABEL_NAMES[i], float(proba[i])) for i in idxs]
+
+def detect_emotions(text: str):
+    chosen = classify_text(text)
+    if not chosen:
+        return "neutral"
+    # Map to app buckets and take the strongest
+    bucket = {}
+    for label, p in chosen:
+        app = GOEMO_TO_APP.get(label.lower(), "neutral")
+        bucket[app] = max(bucket.get(app, 0.0), p)
+    main = max(bucket, key=bucket.get) if bucket else "neutral"
+    return main
+
+# ---------------- Legacy-style reply composer (advice/quote/both) -----------
+def compose_support_legacy(main_emotion: str, is_first_msg: bool) -> str:
+    tip = random.choice(SUGGESTIONS.get(
+        main_emotion,
+        ["Take a slow breath. One small act of kindness can shift your day."]
+    ))
+    quote = random.choice(QUOTES.get(
+        main_emotion,
+        ["“No matter what you feel right now, this moment will pass.”"]
+    ))
+
+    # 0 = advice only, 1 = quote only, 2 = both
+    mode = random.choice([0, 1, 2])
+    if mode == 0:
+        reply = tip
+    elif mode == 1:
+        reply = f"✨ {quote}"
+    else:
+        reply = f"{tip}\n\n💬 {quote}"
+
+    if is_first_msg:
+        reply += "\n\n*Can you tell me a bit more about what’s behind that feeling?*"
+
+    return reply
+
+# ---------------- Chat logic ----------------
+def crisis_block(country):
+    msg = CRISIS_NUMBERS.get(country, CRISIS_NUMBERS["Other / Not listed"])
+    return (
+        "💛 I'm really sorry you're feeling like this. You matter.\n\n"
+        f"**If you might be in danger or thinking about harming yourself:**\n{msg}\n\n"
+        "Please reach out to someone now. You are not alone."
+    )
+
+def chat_step(message, history, country, save_session):
+    if CRISIS_RE.search(message):
+        return crisis_block(country), "#FFD6E7"
+
+    if CLOSING_RE.search(message):
+        return ("Thank you 💛 Take care of yourself. Small steps matter. 🌿", "#FFFFFF")
+
+    recent = " ".join(message.split()[-100:])
+    main = detect_emotions(recent)
+    color = COLOR_MAP.get(main, "#FFFFFF")
+
+    if save_session:
+        log_session(country, message, main)
+
+    reply = compose_support_legacy(main, is_first_msg=not bool(history))
+    return reply, color
+
+# ---------------- Gradio UI ----------------
+init_db()
+
+custom_css = """
+:root, body, .gradio-container { transition: background-color 0.8s ease !important; }
+.typing { font-style: italic; opacity: 0.8; animation: blink 1s infinite; }
+@keyframes blink { 50% {opacity: 0.4;} }
+"""
+
+with gr.Blocks(css=custom_css, title="🪞 MoodMirror+ (Dataset-only Edition)") as demo:
+    style_injector = gr.HTML("")
+    gr.Markdown(
+        "### 🪞 MoodMirror+ — Emotional Support & Inspiration 🌸\n"
+        "Powered only by the **GoEmotions dataset** (trained locally on startup).\n\n"
+        "_Not medical advice. If you feel unsafe, please reach out for help immediately._"
+    )
+
+    with gr.Row():
+        country = gr.Dropdown(choices=list(CRISIS_NUMBERS.keys()), value="Other / Not listed", label="Country")
+        save_ok = gr.Checkbox(value=False, label="Save anonymized session (no personal data)")
+
+    chat = gr.Chatbot(height=360)
+    msg = gr.Textbox(placeholder="Type how you feel...", label="Your message")
+    send = gr.Button("Send")
+    typing = gr.Markdown("", elem_classes="typing")
+
+    # Optional: dataset preview (for transparency)
+    with gr.Accordion("🔎 Preview GoEmotions samples", open=False):
+        with gr.Row():
+            n_examples = gr.Slider(1, 10, value=5, step=1, label="Number of examples")
+            split = gr.Dropdown(["train", "validation", "test"], value="train", label="Split")
+            refresh = gr.Button("Show samples")
+        table = gr.Dataframe(headers=["text", "labels"], row_count=5, wrap=True)
+
+        def refresh_samples(n, split_name):
+            try:
+                ds = load_dataset("google-research-datasets/go_emotions", "simplified")
+                names = ds["train"].features["labels"].feature.names
+                rows = ds[split_name].shuffle(seed=42).select(range(min(int(n), len(ds[split_name]))))
+                return [[t, ", ".join([names[i] for i in labs])] for t, labs in zip(rows["text"], rows["labels"])]
+            except Exception as e:
+                return [[f"Dataset load error: {e}", ""]]
+
+        refresh.click(refresh_samples, inputs=[n_examples, split], outputs=[table])
+
+    def respond(user_msg, chat_hist, country_choice, save_flag):
+        if not user_msg or not user_msg.strip():
+            yield chat_hist + [[user_msg, "Please share a short sentence about how you feel 🙂"]], "", "", ""
+            return
+        yield chat_hist, "💭 MoodMirror is thinking...", "", ""
+        reply, color = chat_step(user_msg, chat_hist, country_choice, bool(save_flag))
+        style_tag = f"<style>:root,body,.gradio-container{{background:{color}!important;}}</style>"
+        yield chat_hist + [[user_msg, reply]], "", style_tag, ""
+
+    send.click(respond, inputs=[msg, chat, country, save_ok],
+               outputs=[chat, typing, style_injector, msg], queue=True)
+    msg.submit(respond, inputs=[msg, chat, country, save_ok],
+               outputs=[chat, typing, style_injector, msg], queue=True)
+
+if __name__ == "__main__":
+    demo.queue()
+    demo.launch()