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core/ai_engine.py

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+"""
+core/ai_engine.py
+All Groq API calls — prompts taken verbatim from the tested Colab notebook.
+Covers: task parsing, scheduling, journaling Q&A, context synthesis.
+"""
+
+import json
+import re
+import os
+from datetime import datetime, date
+from copy import deepcopy
+
+from groq import Groq
+
+GROQ_MODEL = "llama-3.3-70b-versatile"
+_client: Groq = None
+
+
+def init_groq(api_key: str = None):
+    global _client
+    key = api_key or os.environ.get("GROQ_API_KEY", "")
+    if not key:
+        raise ValueError(
+            "GROQ_API_KEY is not set. "
+            "Add it in HuggingFace Space → Settings → Repository secrets."
+        )
+    _client = Groq(api_key=key)
+
+
+def _groq() -> Groq:
+    if _client is None:
+        init_groq()
+    return _client
+
+
+# ── Shared util ───────────────────────────────────────────────────────────────
+
+def safe_json_parse(text: str):
+    """Parse JSON, stripping markdown fences if present. Returns None on failure."""
+    try:
+        return json.loads(text)
+    except json.JSONDecodeError:
+        cleaned = re.sub(r'^```(?:json)?\s*|\s*```$', '', text, flags=re.MULTILINE).strip()
+        try:
+            return json.loads(cleaned)
+        except json.JSONDecodeError:
+            m = re.search(r'\{[\s\S]*\}', cleaned)
+            if m:
+                try:
+                    return json.loads(m.group())
+                except Exception:
+                    pass
+    return None
+
+
+# ── Module 1: Task Capture ────────────────────────────────────────────────────
+
+TASK_CAPTURE_PROMPT = """You are a task classification assistant for a productivity app called The Second Brain.
+
+Your job is to take a user's raw task description and return a structured JSON object.
+
+Classify the task across these dimensions:
+
+1. title (string): A clean, concise, action-oriented task title. Fix grammar.
+
+2. life_area (string): Choose ONE from: Work, Health, Learning, Finance, Personal, Family, Other
+   - Work: job, meetings, deadlines, clients, projects
+   - Health: exercise, medical, diet, mental health
+   - Learning: courses, books, skills, studying
+   - Finance: bills, payments, budgeting, taxes, investments
+   - Personal: hobbies, errands, home maintenance
+   - Family: tasks involving family members
+   - Other: does not fit any category
+
+3. urgency (string): Choose ONE from:
+   - Habit: recurring or routine task
+   - Urgent: hard deadline or time pressure
+   - Not Urgent: no specific deadline
+
+4. importance (string): Choose ONE from:
+   - Move the Needle: very high impact
+   - Important: meaningful, should be done
+   - Not Important: low real impact
+
+5. state_of_mind (string): Choose ONE from:
+   - Quick: 5-10 mins, very low focus
+   - Easy: 10-20 mins, low focus
+   - Flow: deep concentration needed
+   - Personal: life admin, little goal impact
+
+6. time_estimate (integer): Realistic minutes to complete.
+
+7. clarifications_needed (array of strings):
+   If NOT confident about a dimension, add a short specific question.
+   If everything is clear, return []
+
+STRICT RULES:
+- Return ONLY valid JSON. No markdown, no explanation.
+- Never guess if uncertain — ask a clarification question instead.
+- Always return all 7 fields.
+
+Example: {"title": "Finish project proposal", "life_area": "Work", "urgency": "Urgent", "importance": "Move the Needle", "state_of_mind": "Flow", "time_estimate": 90, "clarifications_needed": []}"""
+
+
+def parse_task_with_groq(raw_text: str, user_context: dict = None,
+                         user_goals: list = None, life_areas: list = None) -> dict:
+    """Parse raw task text into structured dimensions using Groq."""
+    # Build context hint from AI memory + goals
+    context_hint = ""
+    if user_context and user_context.get("learned_patterns", {}).get("notes"):
+        notes = user_context["learned_patterns"]["notes"]
+        context_hint += f"\n\nUser context notes (use to inform classification): {'; '.join(notes[-3:])}"
+    if user_goals:
+        context_hint += f"\nUser goals: {'; '.join(user_goals[:5])}"
+    if life_areas:
+        context_hint += f"\nUser's life areas: {', '.join(life_areas)}"
+
+    response = _groq().chat.completions.create(
+        model=GROQ_MODEL,
+        messages=[
+            {"role": "system", "content": TASK_CAPTURE_PROMPT + context_hint},
+            {"role": "user",   "content": f"Parse this task: {raw_text}"}
+        ],
+        max_tokens=512,
+        temperature=0.1,
+    )
+
+    result = safe_json_parse(response.choices[0].message.content.strip())
+    if result is None:
+        result = {
+            "title": raw_text,
+            "life_area": None, "urgency": None, "importance": None,
+            "state_of_mind": None, "time_estimate": None,
+            "clarifications_needed": [
+                "Could you give more details about this task?",
+                "Which area of your life does this belong to?",
+                "Is this urgent or flexible?"
+            ]
+        }
+    return result
+
+
+# ── Module 2: Scheduling ──────────────────────────────────────────────────────
+
+SCHEDULING_SYSTEM_PROMPT = """You are an intelligent daily scheduler for a productivity app called The Second Brain.
+
+You receive a USER CONTEXT (preferences + learned patterns), a TASK LIST, and a SCHEDULING PROMPT.
+Return a time-blocked schedule as a JSON object.
+
+SCHEDULING RULES:
+- Respect wake_time and sleep_time from context
+- Place Flow tasks during the user's peak focus time
+- If avg_task_overrun_pct > 0, add buffer proportionally to time estimates
+- If flow_batch_capable is true, group Flow tasks; otherwise space them out
+- Place Quick and Easy tasks around transitions and low-energy windows
+- Place Personal/Habit tasks at day boundaries (start or end of day)
+- Urgent tasks are scheduled before Not Urgent ones
+- Move the Needle tasks get the best time slots
+- Add 5-10 min breaks between tasks
+- Respect any fixed commitments mentioned in the scheduling prompt
+- Do NOT schedule past sleep_time
+- If tasks won't realistically fit, put them in deferred_tasks
+- If context is minimal (new user), use sensible defaults
+
+RETURN FORMAT (JSON only, no markdown):
+{
+  "schedule_date": "YYYY-MM-DD",
+  "scheduled_tasks": [
+    {
+      "task_id": "(id from input or index)",
+      "title": "...",
+      "life_area": "...",
+      "start_time": "HH:MM",
+      "end_time": "HH:MM",
+      "duration_minutes": 60,
+      "state_of_mind": "...",
+      "scheduling_reason": "1-sentence explanation"
+    }
+  ],
+  "deferred_tasks": [{"task_id": "...", "title": "...", "reason": "..."}],
+  "day_summary": "2-3 sentences on day structure and reasoning",
+  "warnings": ["any concerns e.g. day overloaded"]
+}"""
+
+
+def generate_schedule(context: dict, tasks: list, scheduling_prompt: str,
+                      goals: list = None, schedule_date: str = None) -> dict:
+    if not schedule_date:
+        schedule_date = str(date.today())
+
+    goals_section = ""
+    if goals:
+        goals_section = "\nUSER GOALS:\n" + "\n".join(f"- {g}" for g in goals)
+
+    user_message = f"""Schedule Date: {schedule_date}
+
+USER CONTEXT:
+{json.dumps(context, indent=2)}
+{goals_section}
+TASKS TO SCHEDULE ({len(tasks)} tasks):
+{json.dumps(tasks, indent=2)}
+
+USER SCHEDULING PROMPT:
+{scheduling_prompt}
+
+Generate the optimal schedule."""
+
+    response = _groq().chat.completions.create(
+        model=GROQ_MODEL,
+        messages=[
+            {"role": "system", "content": SCHEDULING_SYSTEM_PROMPT},
+            {"role": "user",   "content": user_message}
+        ],
+        max_tokens=2048,
+        temperature=0.2,
+    )
+
+    result = safe_json_parse(response.choices[0].message.content.strip())
+    if result is None:
+        result = {
+            "error": "Could not parse schedule response.",
+            "raw": response.choices[0].message.content
+        }
+
+    result["schedule_date"] = schedule_date
+    result["generated_at"] = datetime.now().isoformat()
+    return result
+
+
+# ── Module 3: Journaling ──────────────────────────────────────────────────────
+
+JOURNAL_QUESTION_PROMPT = """You are a reflective journaling coach for a productivity app called The Second Brain.
+
+You receive the user's context, today's schedule with completion status, and the conversation so far.
+Your job: decide what targeted question to ask NEXT.
+
+FOCUS AREAS (cover what's most relevant, don't ask all):
+- Tasks not completed — why? wrong time? too tired? overestimated?
+- Tasks that took much longer than estimated
+- Energy levels — when were they sharp vs drained?
+- Whether Flow tasks were placed well or hard to start
+- Whether the day felt balanced or overloaded
+- Patterns the user noticed about themselves
+
+RULES:
+- Ask ONE question at a time. Short and specific.
+- Build on previous answers — don't repeat covered ground.
+- After 4-6 good exchanges, signal completion.
+- Keep tone warm and efficient — 2-minute check-in, not therapy.
+
+RETURN FORMAT (JSON only):
+{"question": "Your next question", "question_focus": "what aspect this targets", "session_complete": false}
+OR when done:
+{"question": null, "question_focus": null, "session_complete": true}"""
+
+
+SYNTHESIS_PROMPT = """You are a pattern recognition engine for a productivity app called The Second Brain.
+
+You have a completed journaling conversation. Extract learnings and return the UPDATED user context JSON.
+
+UPDATE these fields in learned_patterns based on conversation evidence:
+- productive_times: when user felt sharp/focused
+- low_energy_times: when they felt drained or skipped tasks
+- avg_task_overrun_pct: recalculate from actual vs estimated times mentioned
+- flow_batch_capable: update if user gave clear evidence
+- best_life_areas_morning: what they completed well before noon
+- common_skipped_task_types: patterns in what gets consistently skipped
+- notes: append 1-2 new insight notes (keep existing ones)
+
+ALWAYS UPDATE:
+- scheduling_feedback.total_days_scheduled: +1
+- scheduling_feedback.avg_completion_rate: rolling average
+- scheduling_feedback.last_7_day_completion_rates: append today, keep last 7
+- history_summary: append brief today summary, keep last 14
+- last_updated: now
+- version: +1
+
+RULES:
+- Return ONLY the complete updated context JSON. Nothing else.
+- Never remove existing patterns — only update or append.
+- Be conservative — only update if there is clear evidence in the conversation."""
+
+
+def build_opening_question(context: dict, tasks_today: list) -> dict:
+    """Generate the first journal question based on task completion at a glance."""
+    total     = len(tasks_today)
+    completed = sum(1 for t in tasks_today if t.get("completed", False))
+    incomplete = [t for t in tasks_today if not t.get("completed", False)]
+
+    if total == 0:
+        q = "It looks like you didn't have any tasks scheduled today — was that intentional or did things go sideways?"
+    elif completed == 0:
+        q = f"None of today's {total} tasks got marked complete — was the day unexpectedly derailed, or did the plan just not fit how your day went?"
+    elif completed == total:
+        q = f"You completed all {total} tasks today — great day! Did the schedule feel natural, or were you pushing through?"
+    elif len(incomplete) == 1:
+        q = f'You got almost everything done — the one task left was "{incomplete[0]["title"]}". What got in the way?'
+    else:
+        rate   = round(completed / total * 100)
+        titles = ", ".join(f'"{t["title"]}"' for t in incomplete[:2])
+        q = f"You completed {completed}/{total} tasks ({rate}%). Tasks like {titles} didn't get done — was that time, energy, or something else?"
+
+    return {"question": q, "question_focus": "completion_overview", "session_complete": False}
+
+
+def get_next_journal_question(context: dict, tasks_today: list,
+                              conversation_history: list) -> dict:
+    user_message = f"""USER CONTEXT:
+{json.dumps(context, indent=2)}
+
+TODAY'S SCHEDULE (with completion):
+{json.dumps(tasks_today, indent=2)}
+
+CONVERSATION SO FAR ({len(conversation_history)} exchanges):
+{json.dumps(conversation_history, indent=2)}
+
+What should I ask next? Return session_complete: true if enough has been covered."""
+
+    response = _groq().chat.completions.create(
+        model=GROQ_MODEL,
+        messages=[
+            {"role": "system", "content": JOURNAL_QUESTION_PROMPT},
+            {"role": "user",   "content": user_message}
+        ],
+        max_tokens=256,
+        temperature=0.3,
+    )
+
+    result = safe_json_parse(response.choices[0].message.content.strip())
+    if result is None:
+        result = {
+            "question": response.choices[0].message.content.strip(),
+            "question_focus": "general",
+            "session_complete": False
+        }
+    return result
+
+
+def synthesize_journal(context: dict, tasks_today: list,
+                       conversation_history: list) -> dict:
+    """Synthesize conversation into updated context. Fallback to manual stats update if LLM fails."""
+    total     = len(tasks_today)
+    completed = sum(1 for t in tasks_today if t.get("completed", False))
+    completion_rate = round(completed / total, 2) if total > 0 else 0.0
+
+    user_message = f"""USER CONTEXT (current):
+{json.dumps(context, indent=2)}
+
+TODAY'S SCHEDULE + COMPLETION:
+{json.dumps(tasks_today, indent=2)}
+
+Today's completion rate: {completion_rate} ({completed}/{total})
+
+FULL JOURNALING CONVERSATION:
+{json.dumps(conversation_history, indent=2)}
+
+Return the complete updated context JSON."""
+
+    response = _groq().chat.completions.create(
+        model=GROQ_MODEL,
+        messages=[
+            {"role": "system", "content": SYNTHESIS_PROMPT},
+            {"role": "user",   "content": user_message}
+        ],
+        max_tokens=2048,
+        temperature=0.1,
+    )
+
+    updated = safe_json_parse(response.choices[0].message.content.strip())
+
+    if updated is None:
+        # Fallback: update stats manually if synthesis fails
+        updated = deepcopy(context)
+        updated["last_updated"] = datetime.now().isoformat()
+        updated["version"]      = context.get("version", 1) + 1
+        sf    = updated.setdefault("scheduling_feedback", {})
+        sf["total_days_scheduled"] = sf.get("total_days_scheduled", 0) + 1
+        rates = sf.get("last_7_day_completion_rates", [])
+        rates.append(completion_rate)
+        sf["last_7_day_completion_rates"] = rates[-7:]
+        sf["avg_completion_rate"] = round(sum(rates) / len(rates), 2)
+
+    return updated
+
+
+# ── Context helpers ───────────────────────────────────────────────────────────
+
+def create_blank_context(user_id, preferences: dict = None) -> dict:
+    prefs = preferences or {}
+    return {
+        "user_id": user_id,
+        "created_at":   datetime.now().isoformat(),
+        "last_updated": datetime.now().isoformat(),
+        "version": 1,
+        "preferences": {
+            "wake_time":              prefs.get("wake_time", "08:00"),
+            "sleep_time":             prefs.get("sleep_time", "23:00"),
+            "focus_peak":             prefs.get("focus_peak", "Morning"),
+            "break_duration_minutes": 10,
+            "max_flow_block_minutes": 90,
+        },
+        "learned_patterns": {
+            "productive_times":          [],
+            "low_energy_times":          [],
+            "avg_task_overrun_pct":      0,
+            "flow_batch_capable":        None,
+            "best_life_areas_morning":   [],
+            "habit_completion_rate":     {},
+            "common_skipped_task_types": [],
+            "notes":                     []
+        },
+        "history_summary": [],
+        "scheduling_feedback": {
+            "total_days_scheduled":        0,
+            "avg_completion_rate":         0.0,
+            "last_7_day_completion_rates": []
+        }
+    }

core/database.py

@@ -0,0 +1,386 @@
+"""
+core/database.py
+SQLite persistence for Second Brain.
+Tables: users, life_areas, goals, tasks, user_context (AI memory)
+"""
+
+import sqlite3
+import json
+import bcrypt
+from datetime import datetime, date
+from typing import Optional
+
+DB_PATH = "second_brain.db"
+
+
+# ── Connection ────────────────────────────────────────────────────────────────
+
+def get_db() -> sqlite3.Connection:
+    conn = sqlite3.connect(DB_PATH)
+    conn.row_factory = sqlite3.Row
+    conn.execute("PRAGMA foreign_keys = ON")
+    return conn
+
+
+def init_db():
+    """Create all tables on first run."""
+    conn = get_db()
+    c = conn.cursor()
+
+    c.execute("""
+        CREATE TABLE IF NOT EXISTS users (
+            id            INTEGER PRIMARY KEY AUTOINCREMENT,
+            username      TEXT UNIQUE NOT NULL,
+            password_hash TEXT NOT NULL,
+            created_at    TEXT DEFAULT (datetime('now'))
+        )
+    """)
+
+    c.execute("""
+        CREATE TABLE IF NOT EXISTS life_areas (
+            id         INTEGER PRIMARY KEY AUTOINCREMENT,
+            user_id    INTEGER NOT NULL,
+            name       TEXT NOT NULL,
+            color      TEXT DEFAULT '#6366f1',
+            created_at TEXT DEFAULT (datetime('now')),
+            FOREIGN KEY (user_id) REFERENCES users(id)
+        )
+    """)
+
+    c.execute("""
+        CREATE TABLE IF NOT EXISTS user_goals (
+            id         INTEGER PRIMARY KEY AUTOINCREMENT,
+            user_id    INTEGER NOT NULL,
+            goal_text  TEXT NOT NULL,
+            created_at TEXT DEFAULT (datetime('now')),
+            FOREIGN KEY (user_id) REFERENCES users(id)
+        )
+    """)
+
+    c.execute("""
+        CREATE TABLE IF NOT EXISTS tasks (
+            id              INTEGER PRIMARY KEY AUTOINCREMENT,
+            user_id         INTEGER NOT NULL,
+            title           TEXT NOT NULL,
+            life_area       TEXT DEFAULT '',
+            urgency         TEXT DEFAULT 'Not Urgent',
+            importance      TEXT DEFAULT 'Important',
+            state_of_mind   TEXT DEFAULT 'Easy',
+            time_estimate   INTEGER DEFAULT 30,
+            scheduled_date  TEXT DEFAULT (date('now')),
+            is_completed    INTEGER DEFAULT 0,
+            actual_duration INTEGER,
+            is_habit        INTEGER DEFAULT 0,
+            habit_interval  TEXT DEFAULT '',
+            raw_input       TEXT DEFAULT '',
+            created_at      TEXT DEFAULT (datetime('now')),
+            FOREIGN KEY (user_id) REFERENCES users(id)
+        )
+    """)
+
+    # AI-learned context stored as a JSON blob per user
+    c.execute("""
+        CREATE TABLE IF NOT EXISTS user_context (
+            user_id    INTEGER PRIMARY KEY,
+            context    TEXT NOT NULL,
+            updated_at TEXT DEFAULT (datetime('now')),
+            FOREIGN KEY (user_id) REFERENCES users(id)
+        )
+    """)
+
+    conn.commit()
+    conn.close()
+
+
+# ── Auth ──────────────────────────────────────────────────────────────────────
+
+def register_user(username: str, password: str) -> tuple:
+    """Returns (user_id, message). user_id is None on failure."""
+    username = username.strip().lower()
+    if not username or not password:
+        return None, "Username and password cannot be empty."
+    if len(password) < 6:
+        return None, "Password must be at least 6 characters."
+    conn = get_db()
+    try:
+        pw_hash = bcrypt.hashpw(password.encode(), bcrypt.gensalt()).decode()
+        conn.execute(
+            "INSERT INTO users (username, password_hash) VALUES (?, ?)",
+            (username, pw_hash)
+        )
+        conn.commit()
+        row = conn.execute("SELECT id FROM users WHERE username = ?", (username,)).fetchone()
+        return row["id"], "Account created!"
+    except sqlite3.IntegrityError:
+        return None, "Username already taken."
+    finally:
+        conn.close()
+
+
+def login_user(username: str, password: str) -> tuple:
+    """Returns (user_id, message). user_id is None on failure."""
+    username = username.strip().lower()
+    if not username or not password:
+        return None, "Please enter your credentials."
+    conn = get_db()
+    row = conn.execute(
+        "SELECT id, password_hash FROM users WHERE username = ?", (username,)
+    ).fetchone()
+    conn.close()
+    if not row:
+        return None, "Username not found."
+    if not bcrypt.checkpw(password.encode(), row["password_hash"].encode()):
+        return None, "Incorrect password."
+    return row["id"], f"Welcome back, {username}!"
+
+
+def get_username(user_id: int) -> str:
+    conn = get_db()
+    row = conn.execute("SELECT username FROM users WHERE id = ?", (user_id,)).fetchone()
+    conn.close()
+    return row["username"].capitalize() if row else "User"
+
+
+# ── Life Areas ────────────────────────────────────────────────────────────────
+
+DEFAULT_AREAS = [
+    ("Work",     "#4F8EF7"),
+    ("Health",   "#4CAF87"),
+    ("Finance",  "#F7A84F"),
+    ("Learning", "#A855F7"),
+    ("Personal", "#EC4899"),
+    ("Family",   "#F59E0B"),
+]
+
+
+def create_default_life_areas(user_id: int):
+    conn = get_db()
+    for name, color in DEFAULT_AREAS:
+        conn.execute(
+            "INSERT INTO life_areas (user_id, name, color) VALUES (?, ?, ?)",
+            (user_id, name, color)
+        )
+    conn.commit()
+    conn.close()
+
+
+def get_life_areas(user_id: int) -> list:
+    conn = get_db()
+    rows = conn.execute(
+        "SELECT id, name, color FROM life_areas WHERE user_id = ? ORDER BY id",
+        (user_id,)
+    ).fetchall()
+    conn.close()
+    return [dict(r) for r in rows]
+
+
+def get_life_area_names(user_id: int) -> list:
+    return [a["name"] for a in get_life_areas(user_id)]
+
+
+def add_life_area(user_id: int, name: str, color: str = "#6366f1") -> tuple:
+    name = name.strip()
+    if not name:
+        return False, "Name cannot be empty."
+    conn = get_db()
+    exists = conn.execute(
+        "SELECT id FROM life_areas WHERE user_id = ? AND LOWER(name) = LOWER(?)",
+        (user_id, name)
+    ).fetchone()
+    if exists:
+        conn.close()
+        return False, f'"{name}" already exists.'
+    conn.execute(
+        "INSERT INTO life_areas (user_id, name, color) VALUES (?, ?, ?)",
+        (user_id, name, color)
+    )
+    conn.commit()
+    conn.close()
+    return True, f'"{name}" added.'
+
+
+def delete_life_area(user_id: int, name: str) -> tuple:
+    conn = get_db()
+    conn.execute(
+        "DELETE FROM life_areas WHERE user_id = ? AND name = ?", (user_id, name)
+    )
+    conn.commit()
+    conn.close()
+    return True, f'"{name}" removed.'
+
+
+# ── Goals ─────────────────────────────────────────────────────────────────────
+
+def save_goals(user_id: int, goals_text: str):
+    conn = get_db()
+    conn.execute("DELETE FROM user_goals WHERE user_id = ?", (user_id,))
+    for line in goals_text.strip().splitlines():
+        line = line.strip("•- ").strip()
+        if line:
+            conn.execute(
+                "INSERT INTO user_goals (user_id, goal_text) VALUES (?, ?)",
+                (user_id, line)
+            )
+    conn.commit()
+    conn.close()
+
+
+def get_goals(user_id: int) -> list:
+    conn = get_db()
+    rows = conn.execute(
+        "SELECT goal_text FROM user_goals WHERE user_id = ? ORDER BY id",
+        (user_id,)
+    ).fetchall()
+    conn.close()
+    return [r["goal_text"] for r in rows]
+
+
+# ── Tasks ─────────────────────────────────────────────────────────────────────
+
+def save_task(user_id: int, task: dict, scheduled_date: str = None) -> int:
+    conn = get_db()
+    cursor = conn.execute("""
+        INSERT INTO tasks
+            (user_id, title, life_area, urgency, importance, state_of_mind,
+             time_estimate, scheduled_date, raw_input, is_habit, habit_interval)
+        VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+    """, (
+        user_id,
+        task.get("title", "Untitled"),
+        task.get("life_area", ""),
+        task.get("urgency", "Not Urgent"),
+        task.get("importance", "Important"),
+        task.get("state_of_mind", "Easy"),
+        int(task.get("time_estimate") or 30),
+        scheduled_date or str(date.today()),
+        task.get("raw_input", ""),
+        1 if task.get("is_habit") else 0,
+        task.get("habit_interval", ""),
+    ))
+    task_id = cursor.lastrowid
+    conn.commit()
+    conn.close()
+    return task_id
+
+
+def get_tasks(user_id: int, filter_area: str = "All", only_today: bool = False,
+              include_completed: bool = True) -> list:
+    conn = get_db()
+    q = "SELECT * FROM tasks WHERE user_id = ?"
+    params = [user_id]
+    if filter_area and filter_area != "All":
+        q += " AND life_area = ?"
+        params.append(filter_area)
+    if only_today:
+        q += " AND scheduled_date = ?"
+        params.append(str(date.today()))
+    if not include_completed:
+        q += " AND is_completed = 0"
+    q += " ORDER BY is_completed ASC, created_at DESC"
+    rows = conn.execute(q, params).fetchall()
+    conn.close()
+    return [dict(r) for r in rows]
+
+
+def toggle_task_complete(task_id: int, user_id: int, actual_duration: int = None):
+    conn = get_db()
+    task = conn.execute(
+        "SELECT is_completed FROM tasks WHERE id = ? AND user_id = ?", (task_id, user_id)
+    ).fetchone()
+    if task:
+        new_status = 1 - task["is_completed"]
+        if actual_duration and new_status == 1:
+            conn.execute(
+                "UPDATE tasks SET is_completed = ?, actual_duration = ? WHERE id = ? AND user_id = ?",
+                (new_status, actual_duration, task_id, user_id)
+            )
+        else:
+            conn.execute(
+                "UPDATE tasks SET is_completed = ? WHERE id = ? AND user_id = ?",
+                (new_status, task_id, user_id)
+            )
+    conn.commit()
+    conn.close()
+
+
+def delete_task(task_id: int, user_id: int):
+    conn = get_db()
+    conn.execute("DELETE FROM tasks WHERE id = ? AND user_id = ?", (task_id, user_id))
+    conn.commit()
+    conn.close()
+
+
+def get_today_stats(user_id: int) -> dict:
+    tasks = get_tasks(user_id, only_today=True)
+    total = len(tasks)
+    done  = sum(1 for t in tasks if t["is_completed"])
+    return {"total": total, "done": done, "remaining": total - done}
+
+
+# ── Habit recurrence ──────────────────────────────────────────────────────────
+
+def spawn_due_habits(user_id: int):
+    """
+    Check all habit tasks. If a habit's scheduled_date < today and
+    it's not already scheduled for today, create a fresh copy for today.
+    Called on login / tab load.
+    """
+    today = str(date.today())
+    conn = get_db()
+    habits = conn.execute(
+        "SELECT * FROM tasks WHERE user_id = ? AND is_habit = 1",
+        (user_id,)
+    ).fetchall()
+
+    for h in habits:
+        # Check if already exists today
+        existing = conn.execute(
+            "SELECT id FROM tasks WHERE user_id = ? AND title = ? AND is_habit = 1 AND scheduled_date = ?",
+            (user_id, h["title"], today)
+        ).fetchone()
+        if existing:
+            continue
+        # Only spawn if original was scheduled before today
+        if h["scheduled_date"] and h["scheduled_date"] >= today:
+            continue
+        conn.execute("""
+            INSERT INTO tasks (user_id, title, life_area, urgency, importance,
+                               state_of_mind, time_estimate, scheduled_date, is_habit,
+                               habit_interval, raw_input)
+            VALUES (?, ?, ?, ?, ?, ?, ?, ?, 1, ?, ?)
+        """, (
+            user_id, h["title"], h["life_area"], "Habit",
+            h["importance"], h["state_of_mind"], h["time_estimate"],
+            today, h["habit_interval"], h["raw_input"]
+        ))
+    conn.commit()
+    conn.close()
+
+
+# ── AI Context ────────────────────────────────────────────────────────────────
+
+def load_user_context(user_id: int) -> Optional[dict]:
+    conn = get_db()
+    row = conn.execute(
+        "SELECT context FROM user_context WHERE user_id = ?", (user_id,)
+    ).fetchone()
+    conn.close()
+    if row:
+        try:
+            return json.loads(row["context"])
+        except Exception:
+            return None
+    return None
+
+
+def save_user_context(user_id: int, context: dict):
+    conn = get_db()
+    conn.execute("""
+        INSERT INTO user_context (user_id, context, updated_at)
+        VALUES (?, ?, datetime('now'))
+        ON CONFLICT(user_id) DO UPDATE SET
+            context    = excluded.context,
+            updated_at = excluded.updated_at
+    """, (user_id, json.dumps(context)))
+    conn.commit()
+    conn.close()

core/styles.py

@@ -0,0 +1,190 @@
+"""
+core/styles.py
+All CSS for the Second Brain Gradio app.
+"""
+
+CSS = """
+@import url('https://fonts.googleapis.com/css2?family=Sora:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap');
+
+/* ── Reset & base ──────────────────────────────────────── */
+*, *::before, *::after { box-sizing: border-box; }
+
+body, .gradio-container {
+    font-family: 'Sora', system-ui, sans-serif !important;
+    background: #060a12 !important;
+    color: #cbd5e1 !important;
+}
+.gradio-container { max-width: 1200px !important; margin: 0 auto !important; }
+.main { padding: 16px !important; }
+
+/* ── Auth card ─────────────────────────────────────────── */
+#auth-card {
+    max-width: 420px;
+    margin: 48px auto 0;
+    background: #0d1117;
+    border: 1px solid #1e293b;
+    border-radius: 20px;
+    padding: 40px 36px 32px;
+    box-shadow: 0 0 80px rgba(99,102,241,.1);
+}
+#brand-logo {
+    font-size: 28px; font-weight: 700; text-align: center;
+    background: linear-gradient(135deg, #a78bfa 0%, #60a5fa 100%);
+    -webkit-background-clip: text; -webkit-text-fill-color: transparent;
+    margin-bottom: 4px;
+}
+#brand-sub { text-align: center; color: #334155; font-size: 13px; margin-bottom: 28px; }
+
+/* ── Top header ────────────────────────────────────────── */
+#top-header {
+    background: #0d1117;
+    border: 1px solid #1e293b;
+    border-radius: 14px;
+    padding: 14px 22px;
+    display: flex;
+    align-items: center;
+    justify-content: space-between;
+    margin-bottom: 18px;
+}
+#top-header-brand {
+    font-size: 18px; font-weight: 700;
+    background: linear-gradient(135deg, #a78bfa, #60a5fa);
+    -webkit-background-clip: text; -webkit-text-fill-color: transparent;
+}
+#top-header-user { font-size: 12px; color: #334155; }
+
+/* ── Stat cards ────────────────────────────────────────── */
+.stat-card {
+    background: #0d1117;
+    border: 1px solid #1e293b;
+    border-radius: 14px;
+    padding: 18px 12px;
+    text-align: center;
+    min-height: 90px;
+}
+.stat-num   { font-size: 36px; font-weight: 700; line-height: 1; }
+.stat-label { font-size: 10px; color: #334155; margin-top: 6px;
+              text-transform: uppercase; letter-spacing: .8px; }
+
+/* ── Panels ────────────────────────────────────────────── */
+.panel {
+    background: #0d1117;
+    border: 1px solid #1e293b;
+    border-radius: 14px;
+    padding: 22px 20px;
+    margin-top: 14px;
+}
+.sec-label {
+    font-size: 10px; text-transform: uppercase; letter-spacing: 1.2px;
+    color: #334155; font-weight: 600; margin-bottom: 16px;
+}
+
+/* ── Buttons ───────────────────────────────────────────── */
+.btn-primary button {
+    background: linear-gradient(135deg,#6366f1,#8b5cf6) !important;
+    color: #fff !important; font-weight: 600 !important;
+    border: none !important; border-radius: 10px !important;
+    font-family: 'Sora', sans-serif !important;
+    transition: opacity .15s !important;
+}
+.btn-primary button:hover { opacity: .85 !important; }
+
+.btn-accent button {
+    background: linear-gradient(135deg,#0ea5e9,#6366f1) !important;
+    color: #fff !important; font-weight: 600 !important;
+    border: none !important; border-radius: 10px !important;
+    font-family: 'Sora', sans-serif !important;
+}
+.btn-secondary button {
+    background: #161b27 !important; color: #64748b !important;
+    border: 1px solid #1e293b !important; border-radius: 10px !important;
+    font-family: 'Sora', sans-serif !important;
+}
+.btn-success button {
+    background: #052e16 !important; color: #4ade80 !important;
+    border: 1px solid #14532d !important; border-radius: 10px !important;
+    font-family: 'Sora', sans-serif !important;
+}
+.btn-danger button {
+    background: #1c0606 !important; color: #f87171 !important;
+    border: 1px solid #450a0a !important; border-radius: 10px !important;
+    font-family: 'Sora', sans-serif !important;
+}
+
+/* ── Inputs ────────────────────────────────────────────── */
+input, textarea, select {
+    background: #060a12 !important;
+    border: 1px solid #1e293b !important;
+    border-radius: 10px !important;
+    color: #e2e8f0 !important;
+    font-family: 'Sora', sans-serif !important;
+}
+input:focus, textarea:focus {
+    border-color: #6366f1 !important;
+    box-shadow: 0 0 0 2px rgba(99,102,241,.18) !important;
+}
+label { color: #475569 !important; font-size: 12px !important; font-weight: 500 !important; }
+
+/* ── Tabs ──────────────────────────────────────────────── */
+.tab-nav button {
+    color: #334155 !important; font-family: 'Sora', sans-serif !important;
+    font-weight: 500 !important; background: transparent !important;
+    border: none !important; border-bottom: 2px solid transparent !important;
+    padding: 10px 18px !important; font-size: 13px !important;
+}
+.tab-nav button.selected {
+    color: #a78bfa !important;
+    border-bottom: 2px solid #a78bfa !important;
+}
+
+/* ── Dataframe ─────────────────────────────────────────── */
+.dataframe thead th {
+    background: #0d1117 !important; color: #334155 !important;
+    font-size: 10px !important; text-transform: uppercase !important;
+    letter-spacing: .6px !important; padding: 10px 12px !important;
+    font-family: 'Sora', sans-serif !important; border: none !important;
+}
+.dataframe tbody td {
+    background: #060a12 !important; color: #94a3b8 !important;
+    font-family: 'JetBrains Mono', monospace !important;
+    font-size: 12px !important; padding: 9px 12px !important;
+    border-bottom: 1px solid #0d1117 !important;
+}
+.dataframe tbody tr:hover td { background: #0d1117 !important; }
+
+/* ── Chatbot ───────────────────────────────────────────── */
+.chatbot {
+    background: #060a12 !important;
+    border: 1px solid #1e293b !important;
+    border-radius: 14px !important;
+}
+
+/* ── Schedule cards ────────────────────────────────────── */
+.sched-card {
+    background: #0d1117;
+    border: 1px solid #1e293b;
+    border-left: 3px solid #6366f1;
+    border-radius: 10px;
+    padding: 12px 16px;
+    margin-bottom: 8px;
+}
+.sched-time  { font-size: 11px; font-family: 'JetBrains Mono',monospace; color: #6366f1; font-weight: 600; }
+.sched-title { font-size: 14px; font-weight: 600; color: #f1f5f9; margin: 2px 0 4px; }
+.sched-meta  { font-size: 11px; color: #334155; }
+.sched-why   { font-size: 11px; color: #475569; font-style: italic; margin-top: 3px; }
+
+/* ── Life area chips ───────────────────────────────────── */
+.chip {
+    display: inline-block; padding: 4px 12px; border-radius: 20px;
+    font-size: 12px; font-weight: 600; margin: 3px;
+}
+
+/* ── Status messages ───────────────────────────────────── */
+.ok  { color: #4ade80; font-size: 13px; }
+.err { color: #f87171; font-size: 13px; }
+.inf { color: #60a5fa; font-size: 13px; }
+
+/* ── Misc ──────────────────────────────────────────────── */
+hr { border-color: #1e293b !important; margin: 16px 0 !important; }
+.hidden { display: none !important; }
+"""