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+# 1. 使用官方 Python 3.9 镜像
+FROM python:3.9
+
+# 2. 设置工作目录
+WORKDIR /code
+
+# 3. 安装 OpenCV 需要的系统依赖 (必须要这一步，否则 cv2 会报错)
+RUN apt-get update && apt-get install -y libgl1-mesa-glx
+
+# 4. 复制依赖文件并安装 Python 库
+COPY ./requirements.txt /code/requirements.txt
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# 5. 复制所有代码 (main.py 和 static 文件夹) 到容器
+COPY . /code
+
+# 6. 【重要】解决 Hugging Face 的权限问题
+# HF 运行在非 root 用户下，我们需要把目录权限设为 777
+# 这样程序才能自动创建和写入 focus_guard.db 数据库文件
+RUN chmod 777 /code
+
+# 7. 启动命令
+# 注意：Hugging Face 强制要求端口为 7860
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

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+from fastapi import FastAPI, WebSocket, WebSocketDisconnect, HTTPException, Request
+from fastapi.staticfiles import StaticFiles
+from fastapi.responses import FileResponse
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from typing import Optional, List, Any
+import base64
+import cv2
+import numpy as np
+import aiosqlite
+import json
+from datetime import datetime, timedelta
+import math
+import os
+from pathlib import Path
+
+# Initialize FastAPI app
+app = FastAPI(title="Focus Guard API")
+
+# Add CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Global variables
+model = None
+db_path = "focus_guard.db"
+
+# ================ DATABASE MODELS ================
+
+async def init_database():
+    """Initialize SQLite database with required tables"""
+    async with aiosqlite.connect(db_path) as db:
+        # FocusSessions table
+        await db.execute("""
+            CREATE TABLE IF NOT EXISTS focus_sessions (
+                id INTEGER PRIMARY KEY AUTOINCREMENT,
+                start_time TIMESTAMP NOT NULL,
+                end_time TIMESTAMP,
+                duration_seconds INTEGER DEFAULT 0,
+                focus_score REAL DEFAULT 0.0,
+                total_frames INTEGER DEFAULT 0,
+                focused_frames INTEGER DEFAULT 0,
+                created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+            )
+        """)
+
+        # FocusEvents table
+        await db.execute("""
+            CREATE TABLE IF NOT EXISTS focus_events (
+                id INTEGER PRIMARY KEY AUTOINCREMENT,
+                session_id INTEGER NOT NULL,
+                timestamp TIMESTAMP NOT NULL,
+                is_focused BOOLEAN NOT NULL,
+                confidence REAL NOT NULL,
+                detection_data TEXT,
+                FOREIGN KEY (session_id) REFERENCES focus_sessions (id)
+            )
+        """)
+
+        # UserSettings table
+        await db.execute("""
+            CREATE TABLE IF NOT EXISTS user_settings (
+                id INTEGER PRIMARY KEY CHECK (id = 1),
+                sensitivity INTEGER DEFAULT 6,
+                notification_enabled BOOLEAN DEFAULT 1,
+                notification_threshold INTEGER DEFAULT 30,
+                frame_rate INTEGER DEFAULT 30,
+                model_name TEXT DEFAULT 'yolov8n.pt'
+            )
+        """)
+
+        # Insert default settings if not exists
+        await db.execute("""
+            INSERT OR IGNORE INTO user_settings (id, sensitivity, notification_enabled, notification_threshold, frame_rate, model_name)
+            VALUES (1, 6, 1, 30, 30, 'yolov8n.pt')
+        """)
+
+        await db.commit()
+
+# ================ PYDANTIC MODELS ================
+
+class SessionCreate(BaseModel):
+    pass
+
+class SessionEnd(BaseModel):
+    session_id: int
+
+class SettingsUpdate(BaseModel):
+    sensitivity: Optional[int] = None
+    notification_enabled: Optional[bool] = None
+    notification_threshold: Optional[int] = None
+    frame_rate: Optional[int] = None
+
+# ================ YOLO MODEL LOADING ================
+
+def load_yolo_model():
+    """Load YOLOv8 model with optimizations for CPU"""
+    global model
+    try:
+        # Fix PyTorch 2.6+ weights_only issue
+        os.environ['TORCH_LOAD_WEIGHTS_ONLY'] = '0'
+
+        import torch
+        if hasattr(torch.serialization, 'add_safe_globals'):
+            try:
+                from ultralytics.nn.tasks import DetectionModel
+                import torch.nn as nn
+                torch.serialization.add_safe_globals([
+                    DetectionModel,
+                    nn.modules.container.Sequential,
+                ])
+            except Exception as e:
+                print(f"  Safe globals setup: {e}")
+
+        from ultralytics import YOLO
+
+        model_path = "models/yolov8n.pt"
+
+        if not os.path.exists(model_path):
+            print(f"Model file {model_path} not found, downloading yolov8n.pt...")
+            model_path = "yolov8n.pt"
+
+        model = YOLO(model_path)
+
+        try:
+            model.fuse()
+            print("[OK] Model layers fused for optimization")
+        except Exception as e:
+            print(f"  Model fusion skipped: {e}")
+
+        # Warm up
+        print("Warming up model...")
+        dummy_img = np.zeros((416, 416, 3), dtype=np.uint8)
+        model(dummy_img, imgsz=416, conf=0.4, iou=0.45, max_det=5, classes=[0], verbose=False)
+
+        print("[OK] YOLOv8 model loaded and warmed up successfully")
+        return True
+    except Exception as e:
+        print(f"[ERROR] Failed to load YOLOv8 model: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+# ================ FOCUS DETECTION ALGORITHM ================
+
+def is_user_focused(detections, frame_shape, sensitivity=6):
+    persons = [d for d in detections if d.get('class') == 0]
+
+    if not persons:
+        return False, 0.0, {'reason': 'no_person', 'count': 0}
+
+    best_person = max(persons, key=lambda x: x.get('confidence', 0))
+    bbox = best_person['bbox']
+    conf = best_person['confidence']
+
+    base_threshold = 0.8
+    sensitivity_adjustment = (sensitivity - 6) * 0.02
+    confidence_threshold = base_threshold + sensitivity_adjustment
+    confidence_threshold = max(0.5, min(0.95, confidence_threshold))
+
+    is_focused = conf >= confidence_threshold
+
+    h, w = frame_shape[0], frame_shape[1]
+    bbox_center_x = (bbox[0] + bbox[2]) / 2
+    bbox_center_y = (bbox[1] + bbox[3]) / 2
+
+    center_x_norm = bbox_center_x / w if w > 0 else 0.5
+    center_y_norm = bbox_center_y / h if h > 0 else 0.5
+
+    in_frame = (0.2 <= center_x_norm <= 0.8) and (0.15 <= center_y_norm <= 0.85)
+
+    position_factor = 1.0 if in_frame else 0.7
+    final_score = conf * position_factor
+
+    if len(persons) > 1:
+        final_score *= 0.9
+        reason = f"person_detected_multi_{len(persons)}"
+    else:
+        reason = "person_detected" if is_focused else "low_confidence"
+
+    metadata = {
+        'bbox': bbox,
+        'detection_confidence': round(conf, 3),
+        'confidence_threshold': round(confidence_threshold, 3),
+        'center_position': [round(center_x_norm, 3), round(center_y_norm, 3)],
+        'in_frame': in_frame,
+        'person_count': len(persons),
+        'reason': reason
+    }
+
+    return is_focused and in_frame, final_score, metadata
+
+def parse_yolo_results(results):
+    detections = []
+    if results and len(results) > 0:
+        result = results[0]
+        boxes = result.boxes
+        if boxes is not None and len(boxes) > 0:
+            for box in boxes:
+                xyxy = box.xyxy[0].cpu().numpy()
+                conf = float(box.conf[0].cpu().numpy())
+                cls = int(box.cls[0].cpu().numpy())
+                detection = {
+                    'bbox': [float(x) for x in xyxy],
+                    'confidence': conf,
+                    'class': cls,
+                    'class_name': result.names[cls] if hasattr(result, 'names') else str(cls)
+                }
+                detections.append(detection)
+    return detections
+
+# ================ DATABASE OPERATIONS ================
+
+async def create_session():
+    async with aiosqlite.connect(db_path) as db:
+        cursor = await db.execute(
+            "INSERT INTO focus_sessions (start_time) VALUES (?)",
+            (datetime.now().isoformat(),)
+        )
+        await db.commit()
+        return cursor.lastrowid
+
+async def end_session(session_id: int):
+    async with aiosqlite.connect(db_path) as db:
+        cursor = await db.execute(
+            "SELECT start_time, total_frames, focused_frames FROM focus_sessions WHERE id = ?",
+            (session_id,)
+        )
+        row = await cursor.fetchone()
+
+        if not row:
+            return None
+
+        start_time_str, total_frames, focused_frames = row
+        start_time = datetime.fromisoformat(start_time_str)
+        end_time = datetime.now()
+        duration = (end_time - start_time).total_seconds()
+        focus_score = focused_frames / total_frames if total_frames > 0 else 0.0
+
+        await db.execute("""
+            UPDATE focus_sessions
+            SET end_time = ?, duration_seconds = ?, focus_score = ?
+            WHERE id = ?
+        """, (end_time.isoformat(), int(duration), focus_score, session_id))
+
+        await db.commit()
+
+        return {
+            'session_id': session_id,
+            'start_time': start_time_str,
+            'end_time': end_time.isoformat(),
+            'duration_seconds': int(duration),
+            'focus_score': round(focus_score, 3),
+            'total_frames': total_frames,
+            'focused_frames': focused_frames
+        }
+
+async def store_focus_event(session_id: int, is_focused: bool, confidence: float, metadata: dict):
+    async with aiosqlite.connect(db_path) as db:
+        await db.execute("""
+            INSERT INTO focus_events (session_id, timestamp, is_focused, confidence, detection_data)
+            VALUES (?, ?, ?, ?, ?)
+        """, (session_id, datetime.now().isoformat(), is_focused, confidence, json.dumps(metadata)))
+
+        await db.execute(f"""
+            UPDATE focus_sessions
+            SET total_frames = total_frames + 1,
+                focused_frames = focused_frames + {1 if is_focused else 0}
+            WHERE id = ?
+        """, (session_id,))
+        await db.commit()
+
+# ================ STARTUP/SHUTDOWN ================
+
+@app.on_event("startup")
+async def startup_event():
+    print(" Starting Focus Guard API...")
+    await init_database()
+    print("[OK] Database initialized")
+    load_yolo_model()
+
+@app.on_event("shutdown")
+async def shutdown_event():
+    print(" Shutting down Focus Guard API...")
+
+# ================ WEBSOCKET ================
+
+@app.websocket("/ws/video")
+async def websocket_endpoint(websocket: WebSocket):
+    await websocket.accept()
+    session_id = None
+    frame_count = 0
+    last_inference_time = 0
+    min_inference_interval = 0.1
+
+    try:
+        async with aiosqlite.connect(db_path) as db:
+            cursor = await db.execute("SELECT sensitivity FROM user_settings WHERE id = 1")
+            row = await cursor.fetchone()
+            sensitivity = row[0] if row else 6
+
+        while True:
+            data = await websocket.receive_json()
+
+            if data['type'] == 'frame':
+                from time import time
+                current_time = time()
+                if current_time - last_inference_time < min_inference_interval:
+                    await websocket.send_json({'type': 'ack', 'frame_count': frame_count})
+                    continue
+                last_inference_time = current_time
+
+                try:
+                    img_data = base64.b64decode(data['image'])
+                    nparr = np.frombuffer(img_data, np.uint8)
+                    frame = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+
+                    if frame is None: continue
+                    frame = cv2.resize(frame, (640, 480))
+
+                    if model is not None:
+                        results = model(frame, imgsz=416, conf=0.4, iou=0.45, max_det=5, classes=[0], verbose=False)
+                        detections = parse_yolo_results(results)
+                    else:
+                        detections = []
+
+                    is_focused, confidence, metadata = is_user_focused(detections, frame.shape, sensitivity)
+
+                    if session_id:
+                        await store_focus_event(session_id, is_focused, confidence, metadata)
+
+                    await websocket.send_json({
+                        'type': 'detection',
+                        'focused': is_focused,
+                        'confidence': round(confidence, 3),
+                        'detections': detections,
+                        'frame_count': frame_count
+                    })
+                    frame_count += 1
+                except Exception as e:
+                    print(f"Error processing frame: {e}")
+                    await websocket.send_json({'type': 'error', 'message': str(e)})
+
+            elif data['type'] == 'start_session':
+                session_id = await create_session()
+                await websocket.send_json({'type': 'session_started', 'session_id': session_id})
+
+            elif data['type'] == 'end_session':
+                if session_id:
+                    summary = await end_session(session_id)
+                    await websocket.send_json({'type': 'session_ended', 'summary': summary})
+                    session_id = None
+
+    except WebSocketDisconnect:
+        if session_id: await end_session(session_id)
+    except Exception as e:
+        if websocket.client_state.value == 1: await websocket.close()
+
+# ================ API ENDPOINTS ================
+
+@app.post("/api/sessions/start")
+async def api_start_session():
+    session_id = await create_session()
+    return {"session_id": session_id}
+
+@app.post("/api/sessions/end")
+async def api_end_session(data: SessionEnd):
+    summary = await end_session(data.session_id)
+    if not summary: raise HTTPException(status_code=404, detail="Session not found")
+    return summary
+
+@app.get("/api/sessions")
+async def get_sessions(filter: str = "all", limit: int = 50, offset: int = 0):
+    async with aiosqlite.connect(db_path) as db:
+        db.row_factory = aiosqlite.Row
+
+        # NEW: If importing/exporting all, remove limit if special flag or high limit
+        # For simplicity: if limit is -1, return all
+        limit_clause = "LIMIT ? OFFSET ?"
+        params = []
+        
+        base_query = "SELECT * FROM focus_sessions"
+        where_clause = ""
+        
+        if filter == "today":
+            date_filter = datetime.now().replace(hour=0, minute=0, second=0, microsecond=0)
+            where_clause = " WHERE start_time >= ?"
+            params.append(date_filter.isoformat())
+        elif filter == "week":
+            date_filter = datetime.now() - timedelta(days=7)
+            where_clause = " WHERE start_time >= ?"
+            params.append(date_filter.isoformat())
+        elif filter == "month":
+            date_filter = datetime.now() - timedelta(days=30)
+            where_clause = " WHERE start_time >= ?"
+            params.append(date_filter.isoformat())
+        elif filter == "all":
+            # Just ensure we only get completed sessions or all sessions
+            where_clause = " WHERE end_time IS NOT NULL"
+
+        query = f"{base_query}{where_clause} ORDER BY start_time DESC"
+        
+        # Handle Limit for Exports
+        if limit == -1: 
+            # No limit clause for export
+            pass
+        else:
+            query += f" {limit_clause}"
+            params.extend([limit, offset])
+
+        cursor = await db.execute(query, tuple(params))
+        rows = await cursor.fetchall()
+        return [dict(row) for row in rows]
+
+# --- NEW: Import Endpoint ---
+@app.post("/api/import")
+async def import_sessions(sessions: List[dict]):
+    count = 0
+    try:
+        async with aiosqlite.connect(db_path) as db:
+            for session in sessions:
+                # Use .get() to handle potential missing fields from older versions or edits
+                await db.execute("""
+                    INSERT INTO focus_sessions (start_time, end_time, duration_seconds, focus_score, total_frames, focused_frames, created_at)
+                    VALUES (?, ?, ?, ?, ?, ?, ?)
+                """, (
+                    session.get('start_time'),
+                    session.get('end_time'),
+                    session.get('duration_seconds', 0),
+                    session.get('focus_score', 0.0),
+                    session.get('total_frames', 0),
+                    session.get('focused_frames', 0),
+                    session.get('created_at', session.get('start_time'))
+                ))
+                count += 1
+            await db.commit()
+        return {"status": "success", "count": count}
+    except Exception as e:
+        print(f"Import Error: {e}")
+        return {"status": "error", "message": str(e)}
+
+# --- NEW: Clear History Endpoint ---
+@app.delete("/api/history")
+async def clear_history():
+    try:
+        async with aiosqlite.connect(db_path) as db:
+            # Delete events first (foreign key good practice)
+            await db.execute("DELETE FROM focus_events")
+            await db.execute("DELETE FROM focus_sessions")
+            await db.commit()
+        return {"status": "success", "message": "History cleared"}
+    except Exception as e:
+        return {"status": "error", "message": str(e)}
+
+@app.get("/api/sessions/{session_id}")
+async def get_session(session_id: int):
+    async with aiosqlite.connect(db_path) as db:
+        db.row_factory = aiosqlite.Row
+        cursor = await db.execute("SELECT * FROM focus_sessions WHERE id = ?", (session_id,))
+        row = await cursor.fetchone()
+        if not row: raise HTTPException(status_code=404, detail="Session not found")
+        session = dict(row)
+        cursor = await db.execute("SELECT * FROM focus_events WHERE session_id = ? ORDER BY timestamp", (session_id,))
+        events = [dict(r) for r in await cursor.fetchall()]
+        session['events'] = events
+        return session
+
+@app.get("/api/settings")
+async def get_settings():
+    async with aiosqlite.connect(db_path) as db:
+        db.row_factory = aiosqlite.Row
+        cursor = await db.execute("SELECT * FROM user_settings WHERE id = 1")
+        row = await cursor.fetchone()
+        if row: return dict(row)
+        else: return {'sensitivity': 6, 'notification_enabled': True, 'notification_threshold': 30, 'frame_rate': 30, 'model_name': 'yolov8n.pt'}
+
+@app.put("/api/settings")
+async def update_settings(settings: SettingsUpdate):
+    async with aiosqlite.connect(db_path) as db:
+        cursor = await db.execute("SELECT id FROM user_settings WHERE id = 1")
+        exists = await cursor.fetchone()
+        if not exists:
+            await db.execute("INSERT INTO user_settings (id, sensitivity) VALUES (1, 6)")
+            await db.commit()
+
+        updates = []
+        params = []
+        if settings.sensitivity is not None:
+            updates.append("sensitivity = ?")
+            params.append(max(1, min(10, settings.sensitivity)))
+        if settings.notification_enabled is not None:
+            updates.append("notification_enabled = ?")
+            params.append(settings.notification_enabled)
+        if settings.notification_threshold is not None:
+            updates.append("notification_threshold = ?")
+            params.append(max(5, min(300, settings.notification_threshold)))
+        if settings.frame_rate is not None:
+            updates.append("frame_rate = ?")
+            params.append(max(5, min(60, settings.frame_rate)))
+
+        if updates:
+            query = f"UPDATE user_settings SET {', '.join(updates)} WHERE id = 1"
+            await db.execute(query, params)
+            await db.commit()
+        return {"status": "success", "updated": len(updates) > 0}
+
+@app.get("/api/stats/summary")
+async def get_stats_summary():
+    async with aiosqlite.connect(db_path) as db:
+        cursor = await db.execute("SELECT COUNT(*) FROM focus_sessions WHERE end_time IS NOT NULL")
+        total_sessions = (await cursor.fetchone())[0]
+        cursor = await db.execute("SELECT SUM(duration_seconds) FROM focus_sessions WHERE end_time IS NOT NULL")
+        total_focus_time = (await cursor.fetchone())[0] or 0
+        cursor = await db.execute("SELECT AVG(focus_score) FROM focus_sessions WHERE end_time IS NOT NULL")
+        avg_focus_score = (await cursor.fetchone())[0] or 0.0
+        cursor = await db.execute("SELECT DISTINCT DATE(start_time) as session_date FROM focus_sessions WHERE end_time IS NOT NULL ORDER BY session_date DESC")
+        dates = [row[0] for row in await cursor.fetchall()]
+
+        streak_days = 0
+        if dates:
+            current_date = datetime.now().date()
+            for i, date_str in enumerate(dates):
+                session_date = datetime.fromisoformat(date_str).date()
+                expected_date = current_date - timedelta(days=i)
+                if session_date == expected_date: streak_days += 1
+                else: break
+        return {
+            'total_sessions': total_sessions,
+            'total_focus_time': int(total_focus_time),
+            'avg_focus_score': round(avg_focus_score, 3),
+            'streak_days': streak_days
+        }
+
+@app.get("/health")
+async def health_check():
+    return {"status": "healthy", "model_loaded": model is not None, "database": os.path.exists(db_path)}
+
+# ================ STATIC FILES (SPA SUPPORT) ================
+
+# 1. Mount the assets folder (JS/CSS built by Vite/React)
+if os.path.exists("static/assets"):
+    app.mount("/assets", StaticFiles(directory="static/assets"), name="assets")
+
+# 2. Catch-all route for SPA (React Router)
+# This ensures that if you refresh /customise, it serves index.html instead of 404
+@app.get("/{full_path:path}")
+async def serve_react_app(full_path: str, request: Request):
+    # Skip API and WS routes
+    if full_path.startswith("api") or full_path.startswith("ws"):
+        raise HTTPException(status_code=404, detail="Not Found")
+    
+    # Serve index.html for any other route
+    if os.path.exists("static/index.html"):
+        return FileResponse("static/index.html")
+    else:
+        return {"message": "React app not found. Please run 'npm run build' and copy dist to static."}

requirements.txt

@@ -0,0 +1,9 @@
+fastapi
+uvicorn
+websockets
+numpy
+opencv-python-headless
+aiosqlite
+ultralytics
+python-multipart
+jinja2

static/assets/index-DjIeJJNR.css

@@ -0,0 +1 @@
+:root{font-family:system-ui,Avenir,Helvetica,Arial,sans-serif;line-height:1.5;font-weight:400;color-scheme:light dark;color:#ffffffde;background-color:#242424;font-synthesis:none;text-rendering:optimizeLegibility;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale}a{font-weight:500;color:#646cff;text-decoration:inherit}a:hover{color:#535bf2}body{margin:0;display:flex;place-items:center;min-width:320px;min-height:100vh}h1{font-size:3.2em;line-height:1.1}button{border-radius:8px;border:1px solid transparent;padding:.6em 1.2em;font-size:1em;font-weight:500;font-family:inherit;background-color:#1a1a1a;cursor:pointer;transition:border-color .25s}button:hover{border-color:#646cff}button:focus,button:focus-visible{outline:4px auto 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video{width:100%;height:100%;object-fit:cover}#timeline-area{width:60%;height:80px;position:relative;display:flex;flex-direction:column;justify-content:flex-end}.timeline-label{position:absolute;top:0;left:0;color:#888;font-size:14px}#timeline-line{width:100%;height:2px;background-color:#87ceeb}#control-panel{display:flex;gap:20px;width:60%;justify-content:space-between}.action-btn{flex:1;padding:12px 0;border:none;border-radius:12px;font-size:16px;font-family:Nunito,sans-serif;font-weight:700;cursor:pointer;color:#fff;transition:opacity .2s}.action-btn:hover{opacity:.9}.action-btn.green{background-color:#28a745}.action-btn.yellow{background-color:#ffce0b}.action-btn.blue{background-color:#326ed6}.action-btn.red{background-color:#dc3545}#frame-control{display:flex;align-items:center;gap:15px;color:#333;font-weight:700}#frame-slider{width:200px;cursor:pointer}#frame-input{width:50px;padding:5px;border:1px solid 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auto;width:80%;justify-content:center}.filter-btn{padding:10px 20px;border:2px solid #007BFF;background:#fff;color:#007bff;border-radius:8px;cursor:pointer;font-family:Nunito,sans-serif;font-weight:600;transition:all .2s}.filter-btn:hover{background:#e7f3ff}.filter-btn.active{background:#007bff;color:#fff}.chart-container{width:80%;background:#fff;padding:30px;border-radius:12px;margin:20px auto;box-shadow:0 2px 10px #0000001a}#focus-chart{display:block;margin:0 auto;max-width:100%}.sessions-list{width:80%;margin:20px auto}.sessions-list h2{color:#333;margin-bottom:15px}#sessions-table{width:100%;background:#fff;border-collapse:collapse;border-radius:12px;overflow:hidden;box-shadow:0 2px 10px #0000001a}#sessions-table th{background:#007bff;color:#fff;padding:15px;text-align:left;font-weight:600}#sessions-table td{padding:12px 15px;border-bottom:1px solid #eee}#sessions-table tr:last-child td{border-bottom:none}#sessions-table tbody tr:hover{background:#f8f9fa}.btn-view{padding:6px 12px;background:#007bff;color:#fff;border:none;border-radius:5px;cursor:pointer;font-family:Nunito,sans-serif;transition:background .2s}.btn-view:hover{background:#0056b3}.settings-container{width:60%;max-width:800px;margin:20px auto}.setting-group{background:#fff;padding:30px;border-radius:12px;margin-bottom:20px;box-shadow:0 2px 10px #0000001a}.setting-group h2{margin-top:0;color:#333;font-size:20px;margin-bottom:20px;border-bottom:2px solid #007BFF;padding-bottom:10px}.setting-item{margin-bottom:25px}.setting-item:last-child{margin-bottom:0}.setting-item label{display:block;margin-bottom:8px;color:#333;font-weight:600}.slider-group{display:flex;align-items:center;gap:15px}.slider-group input[type=range]{flex:1}.slider-group span{min-width:40px;text-align:center;font-weight:700;color:#007bff;font-size:18px}.setting-description{font-size:14px;color:#666;margin-top:5px;font-style:italic}input[type=checkbox]{margin-right:10px;cursor:pointer}input[type=number]{width:100px;padding:8px;border:1px solid #ccc;border-radius:5px;font-family:Nunito,sans-serif}.setting-group .action-btn{display:inline-block;width:48%;margin:15px 1%;text-align:center;box-sizing:border-box}#save-settings{display:block;margin:20px auto}.help-container{width:70%;max-width:900px;margin:20px auto}.help-section{background:#fff;padding:30px;border-radius:12px;margin-bottom:20px;box-shadow:0 2px 10px #0000001a}.help-section h2{color:#007bff;margin-top:0;margin-bottom:15px}.help-section ol,.help-section ul{line-height:1.8;color:#333}.help-section p{line-height:1.6;color:#333}details{margin:15px 0;cursor:pointer;padding:10px;background:#f8f9fa;border-radius:5px}summary{font-weight:700;padding:5px;color:#007bff}details[open] summary{margin-bottom:10px;border-bottom:1px solid #ddd;padding-bottom:10px}details p{margin:10px 0 0}.modal-overlay{position:fixed;top:0;left:0;width:100%;height:100%;background:#000000b3;display:flex;align-items:center;justify-content:center;z-index:2000}.modal-content{background:#fff;padding:40px;border-radius:16px;box-shadow:0 10px 40px #0000004d;max-width:500px;width:90%}.modal-content h2{margin-top:0;color:#333;text-align:center;margin-bottom:30px}.summary-stats{margin-bottom:30px}.summary-item{display:flex;justify-content:space-between;padding:15px 0;border-bottom:1px solid #eee}.summary-item:last-child{border-bottom:none}.summary-label{font-weight:600;color:#666}.summary-value{font-weight:700;color:#007bff;font-size:18px}.modal-content .btn-main{display:block;margin:0 auto;padding:12px 40px}.timeline-block{transition:opacity .2s;border-radius:2px}.timeline-block:hover{opacity:.7}@media(max-width:1200px){.stats-grid,.badges-grid{grid-template-columns:repeat(2,1fr)}}@media(max-width:768px){.stats-grid,.badges-grid{grid-template-columns:1fr;width:90%}.settings-container,.help-container,.chart-container,.sessions-list,.records-controls{width:90%}#control-panel{width:90%;flex-wrap:wrap}#display-area,#timeline-area{width:90%}#frame-control{width:90%;flex-direction:column}}.session-result-overlay{position:absolute;top:0;left:0;width:100%;height:100%;background-color:#000000d9;display:flex;flex-direction:column;justify-content:center;align-items:center;color:#fff;z-index:10;animation:fadeIn .5s ease;-webkit-backdrop-filter:blur(5px);backdrop-filter:blur(5px)}.session-result-overlay h3{font-size:32px;margin-bottom:30px;color:#4cd137;text-transform:uppercase;letter-spacing:2px}.session-result-overlay .result-item{display:flex;justify-content:space-between;width:200px;margin-bottom:15px;font-size:20px;border-bottom:1px solid rgba(255,255,255,.2);padding-bottom:5px}.session-result-overlay .label{color:#ccc;font-weight:400}.session-result-overlay .value{color:#fff;font-weight:700;font-family:Courier New,monospace}@keyframes fadeIn{0%{opacity:0;transform:scale(.95)}to{opacity:1;transform:scale(1)}}

static/index.html

@@ -0,0 +1,15 @@
+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <link rel="icon" type="image/svg+xml" href="/vite.svg" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>my-ai-app</title>
+    <link href="https://fonts.googleapis.com/css2?family=Nunito:wght@400;700&display=swap" rel="stylesheet">
+    <script type="module" crossorigin src="/assets/index-DERnMkkp.js"></script>
+    <link rel="stylesheet" crossorigin href="/assets/index-DjIeJJNR.css">
+  </head>
+  <body>
+    <div id="root"></div>
+  </body>
+</html>