Update app.py

app.py

@@ -1,7 +1,7 @@
 """
 AI PLATFORMER + NEURAL CHATBOT (FROM SCRATCH)
-Custom PyTorch NLP model, AABB physics, rich graphics
-No external NLP libraries - everything built from zero
+Fixed jump physics, larger player, AABB collision, rich graphics
+Custom PyTorch NLP model - no external NLP libraries
 """
 
 import os, json, random, threading, logging, time, re, math
@@ -17,69 +17,66 @@ from flask import Flask, jsonify, request, render_template_string
 logging.basicConfig(level=logging.INFO, format='%(asctime)s [%(levelname)s] %(message)s')
 logger = logging.getLogger(__name__)
 
+
 @dataclass
 class Cfg:
     W: int = 80; H: int = 20; GROUND: int = 17; CHUNK: int = 30
     SAFE: int = 15; VIEW: int = 40
-    GRAV: float = 0.35; JUMP: float = -6.5; SPEED: float = 0.35
+    GRAV: float = 0.45        # Fixed: was 0.35
+    JUMP: float = -3.8        # Fixed: was -6.5 (~3.6 tiles high now)
+    SPEED: float = 0.35
     STATE: int = 40 * 40; ACTS: int = 4; MEM: int = 10000
     BATCH: int = 64; GAMMA: float = 0.99; LR: float = 5e-4
     EPS_DEC: float = 0.995; PORT: int = 7860
     MODEL: str = "dqn_model.pth"; CHAT: str = "chat_data.json"
-    # Chat NN config
     VOCAB_MAX: int = 2000; EMB_DIM: int = 64; HIDDEN: int = 128
     CHAT_LR: float = 1e-3; CHAT_EPOCHS: int = 80; SIM_THRESH: float = 0.5
 
 C = Cfg()
 
+
 # ============================================================================
 # CUSTOM TOKENIZER (From Scratch)
 # ============================================================================
 
 class Tokenizer:
-    """Simple character-ngram + word tokenizer built from zero."""
-    
     PAD = "<PAD>"; UNK = "<UNK>"
-    
+
     def __init__(self, max_vocab: int = 2000):
         self.max_vocab = max_vocab
         self.word2idx: Dict[str, int] = {self.PAD: 0, self.UNK: 1}
         self.idx2word: Dict[int, str] = {0: self.PAD, 1: self.UNK}
         self.frozen = False
-    
+
     def _tokenize(self, text: str) -> List[str]:
         text = text.lower().strip()
         text = re.sub(r'[^\w\sа-яё]', ' ', text)
         words = text.split()
-        # Add char bigrams for fuzzy matching
         tokens = []
         for w in words:
             tokens.append(w)
             if len(w) > 2:
                 tokens.extend([w[i:i+2] for i in range(len(w)-1)])
         return tokens
-    
+
     def build_vocab(self, texts: List[str]):
         counter = Counter()
         for t in texts:
             counter.update(self._tokenize(t))
-        
         most_common = counter.most_common(self.max_vocab - 2)
         for word, _ in most_common:
             idx = len(self.word2idx)
             self.word2idx[word] = idx
             self.idx2word[idx] = word
-        
         self.frozen = True
         logger.info(f"📝 Vocab built: {len(self.word2idx)} tokens")
-    
+
     def encode(self, text: str, max_len: int = 32) -> List[int]:
         tokens = self._tokenize(text)[:max_len]
         ids = [self.word2idx.get(t, 1) for t in tokens]
-        # Pad
         ids += [0] * (max_len - len(ids))
         return ids
-    
+
     @property
     def vocab_size(self) -> int:
         return len(self.word2idx)
@@ -90,8 +87,6 @@ class Tokenizer:
 # ============================================================================
 
 class ChatEncoder(nn.Module):
-    """Encodes text into semantic embedding vector."""
-    
     def __init__(self, vocab_size: int, emb_dim: int, hidden: int, out_dim: int):
         super().__init__()
         self.embedding = nn.Embedding(vocab_size, emb_dim, padding_idx=0)
@@ -102,14 +97,11 @@ class ChatEncoder(nn.Module):
         self.dropout = nn.Dropout(0.2)
         self.ln1 = nn.LayerNorm(hidden)
         self.ln2 = nn.LayerNorm(hidden)
-    
+
     def forward(self, x):
-        # x: (batch, seq_len)
-        emb = self.embedding(x)          # (batch, seq_len, emb_dim)
-        # Mean pooling over sequence (ignore padding)
-        mask = (x != 0).unsqueeze(-1).float()  # (batch, seq_len, 1)
-        pooled = (emb * mask).sum(dim=1) / mask.sum(dim=1).clamp(min=1)  # (batch, emb_dim)
-        
+        emb = self.embedding(x)
+        mask = (x != 0).unsqueeze(-1).float()
+        pooled = (emb * mask).sum(dim=1) / mask.sum(dim=1).clamp(min=1)
         h = self.ln1(self.fc1(pooled))
         h = self.relu(h)
         h = self.dropout(h)
@@ -117,16 +109,13 @@ class ChatEncoder(nn.Module):
         h = self.relu(h)
         h = self.dropout(h)
         out = self.fc3(h)
-        # L2 normalize for cosine similarity
         return nn.functional.normalize(out, p=2, dim=-1)
 
 
 class NeuralChat:
-    """Chat system with custom-trained neural network."""
-    
     SEQ_LEN = 32
     OUT_DIM = 64
-    
+
     def __init__(self):
         self.tokenizer = Tokenizer(C.VOCAB_MAX)
         self.data: Dict[str, str] = {}
@@ -134,11 +123,10 @@ class NeuralChat:
         self.q_embeddings: Optional[torch.Tensor] = None
         self.model: Optional[ChatEncoder] = None
         self.device = torch.device('cpu')
-        
         self._load_data()
         self._build_and_train()
         logger.info(f"�� Neural chat ready. {len(self.data)} entries.")
-    
+
     def _load_data(self):
         if os.path.exists(C.CHAT):
             try:
@@ -146,7 +134,6 @@ class NeuralChat:
                     self.data = json.load(f)
             except Exception as e:
                 logger.warning(f"⚠️ Chat load failed: {e}")
-        
         if not self.data:
             self.data = {
                 "как играть": "Стрелки ⬅️➡️ для движения, ⬆️/Пробел для прыжка",
@@ -162,181 +149,113 @@ class NeuralChat:
                 "кто тебя создал": "Я написан с нуля на PyTorch без внешних NLP библиотек",
             }
             self._save_data()
-    
+
     def _save_data(self):
         with open(C.CHAT, 'w', encoding='utf-8') as f:
             json.dump(self.data, f, ensure_ascii=False, indent=2)
-    
+
     def _build_and_train(self):
-        """Build vocab, create model, train on existing data."""
         self.questions = list(self.data.keys())
-        
         if not self.questions:
             return
-        
-        # Build vocabulary from all questions
         self.tokenizer.build_vocab(self.questions)
-        
-        # Create model
         self.model = ChatEncoder(
             vocab_size=self.tokenizer.vocab_size,
-            emb_dim=C.EMB_DIM,
-            hidden=C.HIDDEN,
-            out_dim=self.OUT_DIM
+            emb_dim=C.EMB_DIM, hidden=C.HIDDEN, out_dim=self.OUT_DIM
         ).to(self.device)
-        
-        # Train on question pairs (contrastive learning)
         self._train_model()
-        
-        # Pre-compute embeddings
         self._update_index()
-    
+
     def _train_model(self):
-        """Train encoder using contrastive loss on question pairs."""
         if len(self.questions) < 2:
-            # Not enough data to train meaningfully, use untrained model
-            logger.warning("⚠️ Too few entries to train, using untrained embeddings")
+            logger.warning("⚠️ Too few entries to train")
             return
-        
         optimizer = optim.Adam(self.model.parameters(), lr=C.CHAT_LR)
-        
-        # Encode all questions
         q_ids = torch.LongTensor([
             self.tokenizer.encode(q, self.SEQ_LEN) for q in self.questions
         ]).to(self.device)
-        
         n = len(self.questions)
         best_loss = float('inf')
-        
         logger.info(f"🧠 Training chat NN: {n} samples, {C.CHAT_EPOCHS} epochs...")
-        
         for epoch in range(C.CHAT_EPOCHS):
-            total_loss = 0.0
-            num_pairs = 0
-            
-            # For each question, positive = itself, negative = random other
-            indices = list(range(n))
-            random.shuffle(indices)
-            
+            total_loss = 0.0; num_pairs = 0
+            indices = list(range(n)); random.shuffle(indices)
             for i in indices:
-                anchor = q_ids[i:i+1]      # (1, seq)
-                positive = q_ids[i:i+1]     # same question
-                
-                # Pick negative (different question)
+                anchor = q_ids[i:i+1]
+                positive = q_ids[i:i+1]
                 neg_idx = random.choice([j for j in range(n) if j != i])
                 negative = q_ids[neg_idx:neg_idx+1]
-                
-                emb_a = self.model(anchor)     # (1, out_dim)
-                emb_p = self.model(positive)   # (1, out_dim)
-                emb_n = self.model(negative)   # (1, out_dim)
-                
-                # Cosine similarity
+                emb_a = self.model(anchor)
+                emb_p = self.model(positive)
+                emb_n = self.model(negative)
                 pos_sim = nn.functional.cosine_similarity(emb_a, emb_p)
                 neg_sim = nn.functional.cosine_similarity(emb_a, emb_n)
-                
-                # Contrastive loss: maximize pos_sim, minimize neg_sim
-                margin = 0.3
-                loss = torch.relu(margin - pos_sim + neg_sim).mean()
-                
-                optimizer.zero_grad()
-                loss.backward()
-                optimizer.step()
-                
-                total_loss += loss.item()
-                num_pairs += 1
-            
+                loss = torch.relu(0.3 - pos_sim + neg_sim).mean()
+                optimizer.zero_grad(); loss.backward(); optimizer.step()
+                total_loss += loss.item(); num_pairs += 1
             avg_loss = total_loss / max(num_pairs, 1)
-            if avg_loss < best_loss:
-                best_loss = avg_loss
-            
+            if avg_loss < best_loss: best_loss = avg_loss
             if (epoch + 1) % 20 == 0:
                 logger.info(f"  Epoch {epoch+1}/{C.CHAT_EPOCHS}, loss={avg_loss:.4f}")
-        
         logger.info(f"✅ Chat training complete. Best loss: {best_loss:.4f}")
-    
+
     def _update_index(self):
-        """Pre-compute embeddings for all stored questions."""
         if not self.questions or self.model is None:
-            self.q_embeddings = None
-            return
-        
+            self.q_embeddings = None; return
         self.model.eval()
         with torch.no_grad():
             ids = torch.LongTensor([
                 self.tokenizer.encode(q, self.SEQ_LEN) for q in self.questions
             ]).to(self.device)
-            self.q_embeddings = self.model(ids)  # (n, out_dim)
+            self.q_embeddings = self.model(ids)
         self.model.train()
-    
+
     def ask(self, query: str) -> str:
-        """Semantic search using trained neural embeddings."""
         if not self.questions or self.q_embeddings is None:
             return "🤖 База пуста. Обучи меня: /data вопрос|ответ"
-        
         self.model.eval()
         with torch.no_grad():
             q_id = torch.LongTensor([self.tokenizer.encode(query, self.SEQ_LEN)]).to(self.device)
-            q_emb = self.model(q_id)  # (1, out_dim)
-            
-            # Cosine similarity against all stored questions
+            q_emb = self.model(q_id)
             sims = nn.functional.cosine_similarity(q_emb, self.q_embeddings)
             best_idx = torch.argmax(sims).item()
             best_score = sims[best_idx].item()
-        
         self.model.train()
-        
         if best_score >= C.SIM_THRESH:
-            conf = int(best_score * 100)
-            return f"{self.data[self.questions[best_idx]]} (🧠 {conf}%)"
-        
+            return f"{self.data[self.questions[best_idx]]} (🧠 {int(best_score*100)}%)"
         return f"🤖 Не знаю «{query}». Научи: /data {query}|ответ"
-    
+
     def teach(self, question: str, answer: str) -> str:
-        """Add knowledge and retrain incrementally."""
-        question = question.strip().lower()
-        answer = answer.strip()
-        
-        if not question or not answer:
-            return "❌ Формат: /data вопрос|ответ"
-        
+        question = question.strip().lower(); answer = answer.strip()
+        if not question or not answer: return "❌ Формат: /data вопрос|ответ"
         is_new = question not in self.data
-        self.data[question] = answer
-        self._save_data()
-        
-        # Rebuild everything
+        self.data[question] = answer; self._save_data()
         self.questions = list(self.data.keys())
         self.tokenizer = Tokenizer(C.VOCAB_MAX)
         self.tokenizer.build_vocab(self.questions)
-        
         self.model = ChatEncoder(
             vocab_size=self.tokenizer.vocab_size,
-            emb_dim=C.EMB_DIM,
-            hidden=C.HIDDEN,
-            out_dim=self.OUT_DIM
+            emb_dim=C.EMB_DIM, hidden=C.HIDDEN, out_dim=self.OUT_DIM
         ).to(self.device)
-        
-        self._train_model()
-        self._update_index()
-        
+        self._train_model(); self._update_index()
         action = "Добавлено" if is_new else "Обновлено"
         return f"✅ {action}: «{question}» → «{answer}». Модель переобучена."
-    
+
     @property
     def stats(self) -> dict:
         params = sum(p.numel() for p in self.model.parameters()) if self.model else 0
-        return {
-            'entries': len(self.data),
-            'vocab': self.tokenizer.vocab_size,
-            'params': params,
-            'emb_dim': self.OUT_DIM
-        }
+        return {'entries': len(self.data), 'vocab': self.tokenizer.vocab_size,
+                'params': params, 'emb_dim': self.OUT_DIM}
 
 
 # ============================================================================
-# GAME ENGINE (AABB Physics + Rich Graphics)
+# GAME ENGINE (Fixed Physics + Larger Player)
 # ============================================================================
 
 class Engine:
+    PW = 0.8   # Player width — FIXED: was 0.6
+    PH = 0.95  # Player height — FIXED: was 0.9
+
     def __init__(self, seed=None):
         self.seed = seed or random.randint(0, 999999)
         self.reset()
@@ -344,44 +263,29 @@ class Engine:
     def reset(self):
         self.px, self.py = 5.0, float(C.GROUND)
         self.vx, self.vy = 0.0, 0.0
-        self.grounded = True
-        self.alive = True
-        self.score = 0
-        self.coins = 0
-        self.step_n = 0
+        self.grounded = True; self.alive = True
+        self.score = 0; self.coins = 0; self.step_n = 0
         self.chunks: Dict[int, dict] = {}
-        self.obs: List[dict] = []
-        self.enemies: List[dict] = []
-        self.coin_list: List[dict] = []
+        self.obs: List[dict] = []; self.enemies: List[dict] = []; self.coin_list: List[dict] = []
         self._load_chunks()
         return self.get_state()
 
     def _gen_chunk(self, cid: int) -> dict:
         rng = random.Random((cid * 1337 + self.seed) % 999999)
-        bx = cid * C.CHUNK
-        obs, ens, cns = [], [], []
-        diff = max(1.0, abs(cid) * 0.1)
-        safe = bx < C.SAFE
-
+        bx = cid * C.CHUNK; obs, ens, cns = [], [], []
+        diff = max(1.0, abs(cid) * 0.1); safe = bx < C.SAFE
         if not safe:
             for _ in range(rng.randint(3, 6) + int(diff)):
-                x = bx + rng.randint(5, 25)
-                h = rng.randint(1, 3 + int(diff * 0.5))
-                w = rng.randint(1, 3)
+                x = bx + rng.randint(5, 25); h = rng.randint(1, 3 + int(diff * 0.5)); w = rng.randint(1, 3)
                 obs.append({'x': x, 'y': C.GROUND - h, 'w': w, 'h': h, 'pit': False})
             for _ in range(rng.randint(1, 2)):
                 x = bx + rng.randint(10, 20)
                 obs.append({'x': x, 'y': C.GROUND + 1, 'w': rng.randint(2, 4), 'h': 1, 'pit': True})
             for _ in range(rng.randint(1, 2)):
                 x = bx + rng.randint(10, 20)
-                ens.append({
-                    'x': x, 'y': C.GROUND - 1,
-                    'type': rng.choice(['walker', 'jumper']),
-                    'dir': rng.choice([-1, 1]),
-                    'spd': 0.3 + rng.random() * 0.3,
-                    'rng': rng.randint(3, 8), 'ox': x
-                })
-
+                ens.append({'x': x, 'y': C.GROUND - 1, 'type': rng.choice(['walker', 'jumper']),
+                            'dir': rng.choice([-1, 1]), 'spd': 0.3 + rng.random() * 0.3,
+                            'rng': rng.randint(3, 8), 'ox': x})
         for _ in range(rng.randint(5, 10) + int(diff)):
             cns.append({'x': bx + rng.randint(2, 28), 'y': rng.randint(5, C.GROUND - 2), 'collected': False})
         return {'obs': obs, 'ens': ens, 'cns': cns}
@@ -389,8 +293,7 @@ class Engine:
     def _load_chunks(self):
         cc = int(self.px // C.CHUNK)
         for i in range(cc - 1, cc + 3):
-            if i not in self.chunks:
-                self.chunks[i] = self._gen_chunk(i)
+            if i not in self.chunks: self.chunks[i] = self._gen_chunk(i)
         vl, vr = self.px - C.W / 2, self.px + C.W / 2
         self.obs, self.enemies, self.coin_list = [], [], []
         for i in range(cc - 1, cc + 3):
@@ -403,10 +306,8 @@ class Engine:
         return ax < bx + bw and ax + aw > bx and ay < by + bh and ay + ah > by
 
     def get_state(self):
-        s = np.zeros((C.VIEW, C.VIEW), dtype=np.float32)
-        h = C.VIEW // 2
-        px, py = int(round(self.px)), int(round(self.py))
-        s[h, h] = 1.0
+        s = np.zeros((C.VIEW, C.VIEW), dtype=np.float32); h = C.VIEW // 2
+        px, py = int(round(self.px)), int(round(self.py)); s[h, h] = 1.0
         for o in self.obs:
             dx, dy = int(round(o['x'])) - px, int(round(o['y'])) - py
             v = -1.0 if o.get('pit') else 0.8
@@ -424,47 +325,52 @@ class Engine:
 
     def step(self, action: int):
         sound = None
-        PW, PH = 0.6, 0.9
+        # Input
         self.vx = 0.0
         if action == 1: self.vx = -C.SPEED
         elif action == 2: self.vx = C.SPEED
         if action == 3 and self.grounded:
             self.vy = C.JUMP; self.grounded = False; sound = 'jump'
 
-        # X axis
+        # X axis movement + collision
         self.px += self.vx
         for o in self.obs:
             if o.get('pit'): continue
-            if self._aabb(self.px, self.py, PW, PH, o['x'], o['y'], o['w'], o['h']):
-                if self.vx > 0: self.px = o['x'] - PW
+            if self._aabb(self.px, self.py, self.PW, self.PH, o['x'], o['y'], o['w'], o['h']):
+                if self.vx > 0: self.px = o['x'] - self.PW
                 elif self.vx < 0: self.px = o['x'] + o['w']
                 self.vx = 0
 
-        # Y axis
+        # Y axis movement + collision
         self.vy += C.GRAV; self.py += self.vy; self.grounded = False
         if self.py >= C.GROUND:
             self.py = C.GROUND; self.vy = 0.0; self.grounded = True
         for o in self.obs:
             if o.get('pit'): continue
-            if self._aabb(self.px, self.py, PW, PH, o['x'], o['y'], o['w'], o['h']):
-                if self.vy > 0: self.py = o['y'] - PH; self.vy = 0.0; self.grounded = True
-                elif self.vy < 0: self.py = o['y'] + o['h']; self.vy = 0.0
+            if self._aabb(self.px, self.py, self.PW, self.PH, o['x'], o['y'], o['w'], o['h']):
+                if self.vy > 0:
+                    self.py = o['y'] - self.PH; self.vy = 0.0; self.grounded = True
+                elif self.vy < 0:
+                    self.py = o['y'] + o['h']; self.vy = 0.0
 
+        # Death checks
         if self.py > C.H + 2:
             self.alive = False; return self.get_state(), -50.0, True, 'die'
         for o in self.obs:
-            if o.get('pit') and o['x'] <= self.px + PW / 2 <= o['x'] + o['w'] and self.py >= C.GROUND:
+            if o.get('pit') and o['x'] <= self.px + self.PW / 2 <= o['x'] + o['w'] and self.py >= C.GROUND:
                 self.alive = False; return self.get_state(), -50.0, True, 'die'
         for e in self.enemies:
-            if self._aabb(self.px, self.py, PW, PH, e['x'] - 0.3, e['y'] - 0.3, 0.6, 0.6):
+            if self._aabb(self.px, self.py, self.PW, self.PH, e['x'] - 0.3, e['y'] - 0.3, 0.6, 0.6):
                 self.alive = False; return self.get_state(), -50.0, True, 'die'
 
+        # Coins
         got = 0
         for c in self.coin_list:
-            if not c['collected'] and self._aabb(self.px, self.py, PW, PH, c['x'] - 0.3, c['y'] - 0.3, 0.6, 0.6):
+            if not c['collected'] and self._aabb(self.px, self.py, self.PW, self.PH, c['x'] - 0.3, c['y'] - 0.3, 0.6, 0.6):
                 c['collected'] = True; got += 1
         if got: self.coins += got; self.score += got * 10; sound = 'coin'
 
+        # Update enemies
         t = time.time()
         for e in self.enemies:
             if e['type'] == 'walker':
@@ -568,7 +474,7 @@ is_training = False
 
 
 # ============================================================================
-# HTML (Rich Graphics)
+# HTML (Rich Graphics + Larger Player Rendering)
 # ============================================================================
 
 HTML = """
@@ -665,57 +571,173 @@ let pA=0;
 function draw(ctx,d,show){
     const W=ctx.canvas.width,H=ctx.canvas.height,cW=W/80,cH=H/20;
     ctx.clearRect(0,0,W,H);
+
+    // Sky
     const sg=ctx.createLinearGradient(0,0,0,H);
     sg.addColorStop(0,'#0f0c29');sg.addColorStop(0.5,'#302b63');sg.addColorStop(1,'#24243e');
     ctx.fillStyle=sg;ctx.fillRect(0,0,W,H);
+
+    // Stars
     ctx.fillStyle='rgba(255,255,255,0.3)';
     for(let i=0;i<30;i++){const sx=(i*137+d.player[0]*0.1)%W,sy=(i*97)%((d.ground-2)*cH);ctx.fillRect(sx,sy,2,2)}
+
     const cam=Math.max(0,d.player[0]-40);
     function toS(wx,wy){return[(wx-cam)*cW,wy*cH]}
+
+    // Ground
     const gy=d.ground*cH;
     const gg=ctx.createLinearGradient(0,gy,0,H);
     gg.addColorStop(0,'#4a7c59');gg.addColorStop(0.15,'#3d6b4e');gg.addColorStop(0.5,'#5c4033');gg.addColorStop(1,'#3e2723');
     ctx.fillStyle=gg;ctx.fillRect(0,gy,W,H-gy);
     ctx.fillStyle='#6abf69';ctx.fillRect(0,gy,W,cH*0.3);
     ctx.fillStyle='#81c784';for(let gx=0;gx<W;gx+=8)ctx.fillRect(gx,gy-cH*0.1,4,cH*0.15);
+
+    // Obstacles
     for(const o of d.obstacles){
         const[x,y]=toS(o.x,o.y);
-        if(o.pit){const pg=ctx.createLinearGradient(0,y-cH,0,y+cH);pg.addColorStop(0,'#1a1a2e');pg.addColorStop(1,'#000');ctx.fillStyle=pg;ctx.fillRect(x,y-cH,o.w*cW,cH*2);ctx.fillStyle='#ff4444';ctx.fillRect(x,y-cH*0.5,o.w*cW,2)}
-        else{const bg=ctx.createLinearGradient(x,y,x,y+o.h*cH);bg.addColorStop(0,'#8d6e63');bg.addColorStop(1,'#6d4c41');ctx.fillStyle=bg;ctx.fillRect(x,y,o.w*cW,o.h*cH);ctx.strokeStyle='rgba(0,0,0,0.3)';ctx.lineWidth=1;for(let by=0;by<o.h;by++){const yy=y+by*cH;ctx.beginPath();ctx.moveTo(x,yy);ctx.lineTo(x+o.w*cW,yy);ctx.stroke();const off=(by%2)*cW*0.5;for(let bx=off;bx<o.w*cW;bx+=cW){ctx.beginPath();ctx.moveTo(x+bx,yy);ctx.lineTo(x+bx,yy+cH);ctx.stroke()}}ctx.fillStyle='rgba(255,255,255,0.15)';ctx.fillRect(x,y,o.w*cW,cH*0.15);ctx.fillStyle='rgba(0,0,0,0.3)';ctx.fillRect(x+o.w*cW,y,3,o.h*cH)}
+        if(o.pit){
+            const pg=ctx.createLinearGradient(0,y-cH,0,y+cH);
+            pg.addColorStop(0,'#1a1a2e');pg.addColorStop(1,'#000');
+            ctx.fillStyle=pg;ctx.fillRect(x,y-cH,o.w*cW,cH*2);
+            ctx.fillStyle='#ff4444';ctx.fillRect(x,y-cH*0.5,o.w*cW,2);
+        }else{
+            const bg=ctx.createLinearGradient(x,y,x,y+o.h*cH);
+            bg.addColorStop(0,'#8d6e63');bg.addColorStop(1,'#6d4c41');
+            ctx.fillStyle=bg;ctx.fillRect(x,y,o.w*cW,o.h*cH);
+            ctx.strokeStyle='rgba(0,0,0,0.3)';ctx.lineWidth=1;
+            for(let by=0;by<o.h;by++){
+                const yy=y+by*cH;ctx.beginPath();ctx.moveTo(x,yy);ctx.lineTo(x+o.w*cW,yy);ctx.stroke();
+                const off=(by%2)*cW*0.5;
+                for(let bx=off;bx<o.w*cW;bx+=cW){ctx.beginPath();ctx.moveTo(x+bx,yy);ctx.lineTo(x+bx,yy+cH);ctx.stroke()}
+            }
+            ctx.fillStyle='rgba(255,255,255,0.15)';ctx.fillRect(x,y,o.w*cW,cH*0.15);
+            ctx.fillStyle='rgba(0,0,0,0.3)';ctx.fillRect(x+o.w*cW,y,3,o.h*cH);
+        }
     }
+
+    // Enemies
     const t=Date.now()/200;
-    for(const e of d.entities){const[x,y]=toS(e.x,e.y);const bounce=Math.sin(t+e.x)*2;ctx.save();ctx.translate(x+cW/2,y+cH/2+bounce);const eg=ctx.createRadialGradient(0,0,2,0,0,cH/2);eg.addColorStop(0,'#ff6b6b');eg.addColorStop(1,'#c0392b');ctx.fillStyle=eg;ctx.beginPath();ctx.arc(0,0,cH/2.5,0,Math.PI*2);ctx.fill();ctx.fillStyle='#fff';ctx.beginPath();ctx.arc(-4,-3,3,0,Math.PI*2);ctx.arc(4,-3,3,0,Math.PI*2);ctx.fill();ctx.fillStyle='#000';const ex=e.dir*2;ctx.beginPath();ctx.arc(-4+ex,-3,1.5,0,Math.PI*2);ctx.arc(4+ex,-3,1.5,0,Math.PI*2);ctx.fill();ctx.shadowColor='#ff6b6b';ctx.shadowBlur=10;ctx.strokeStyle='#ff6b6b';ctx.lineWidth=1;ctx.beginPath();ctx.arc(0,0,cH/2.2,0,Math.PI*2);ctx.stroke();ctx.restore()}
-    for(const c of d.coins){const[x,y]=toS(c.x,c.y);const pulse=1+Math.sin(t*2+c.x)*0.15;ctx.save();ctx.translate(x+cW/2,y+cH/2);ctx.scale(pulse,pulse);const cg=ctx.createRadialGradient(-2,-2,1,0,0,cH/3);cg.addColorStop(0,'#fff9c4');cg.addColorStop(0.5,'#ffd700');cg.addColorStop(1,'#f9a825');ctx.fillStyle=cg;ctx.beginPath();ctx.arc(0,0,cH/3,0,Math.PI*2);ctx.fill();ctx.shadowColor='#ffd700';ctx.shadowBlur=12;ctx.strokeStyle='#ffeb3b';ctx.lineWidth=1.5;ctx.beginPath();ctx.arc(0,0,cH/3,0,Math.PI*2);ctx.stroke();ctx.fillStyle='rgba(255,255,255,0.8)';ctx.beginPath();ctx.arc(-3,-3,2,0,Math.PI*2);ctx.fill();ctx.restore()}
-    if(show&&d.alive){const[px,py]=toS(d.player[0],d.player[1]);ctx.save();ctx.shadowColor='#00ff88';ctx.shadowBlur=20;const pg=ctx.createLinearGradient(px,py,px+cW,py+cH);pg.addColorStop(0,'#00ff88');pg.addColorStop(1,'#00b894');ctx.fillStyle=pg;ctx.fillRect(px+2,py+2,cW-4,cH-4);ctx.shadowBlur=0;ctx.fillStyle='#fff';ctx.fillRect(px+cW*0.2,py+cH*0.25,cW*0.2,cH*0.2);ctx.fillRect(px+cW*0.6,py+cH*0.25,cW*0.2,cH*0.2);ctx.fillStyle='#0d1117';ctx.fillRect(px+cW*0.25,py+cH*0.3,cW*0.1,cH*0.1);ctx.fillRect(px+cW*0.65,py+cH*0.3,cW*0.1,cH*0.1);ctx.restore()}
+    for(const e of d.entities){
+        const[x,y]=toS(e.x,e.y);const bounce=Math.sin(t+e.x)*2;
+        ctx.save();ctx.translate(x+cW/2,y+cH/2+bounce);
+        const eg=ctx.createRadialGradient(0,0,2,0,0,cH/2);
+        eg.addColorStop(0,'#ff6b6b');eg.addColorStop(1,'#c0392b');
+        ctx.fillStyle=eg;ctx.beginPath();ctx.arc(0,0,cH/2.5,0,Math.PI*2);ctx.fill();
+        ctx.fillStyle='#fff';ctx.beginPath();ctx.arc(-4,-3,3,0,Math.PI*2);ctx.arc(4,-3,3,0,Math.PI*2);ctx.fill();
+        ctx.fillStyle='#000';const ex=e.dir*2;
+        ctx.beginPath();ctx.arc(-4+ex,-3,1.5,0,Math.PI*2);ctx.arc(4+ex,-3,1.5,0,Math.PI*2);ctx.fill();
+        ctx.shadowColor='#ff6b6b';ctx.shadowBlur=10;
+        ctx.strokeStyle='#ff6b6b';ctx.lineWidth=1;ctx.beginPath();ctx.arc(0,0,cH/2.2,0,Math.PI*2);ctx.stroke();
+        ctx.restore();
+    }
+
+    // Coins
+    for(const c of d.coins){
+        const[x,y]=toS(c.x,c.y);const pulse=1+Math.sin(t*2+c.x)*0.15;
+        ctx.save();ctx.translate(x+cW/2,y+cH/2);ctx.scale(pulse,pulse);
+        const cg=ctx.createRadialGradient(-2,-2,1,0,0,cH/3);
+        cg.addColorStop(0,'#fff9c4');cg.addColorStop(0.5,'#ffd700');cg.addColorStop(1,'#f9a825');
+        ctx.fillStyle=cg;ctx.beginPath();ctx.arc(0,0,cH/3,0,Math.PI*2);ctx.fill();
+        ctx.shadowColor='#ffd700';ctx.shadowBlur=12;
+        ctx.strokeStyle='#ffeb3b';ctx.lineWidth=1.5;ctx.beginPath();ctx.arc(0,0,cH/3,0,Math.PI*2);ctx.stroke();
+        ctx.fillStyle='rgba(255,255,255,0.8)';ctx.beginPath();ctx.arc(-3,-3,2,0,Math.PI*2);ctx.fill();
+        ctx.restore();
+    }
+
+    // PLAYER — FIXED: larger, more visible
+    if(show&&d.alive){
+        const[px,py]=toS(d.player[0],d.player[1]);
+        const pw=cW*0.85, ph=cH*0.95;
+        const ox=px+(cW-pw)/2, oy=py+(cH-ph)/2;
+        ctx.save();
+        ctx.shadowColor='#00ff88';ctx.shadowBlur=25;
+        const pg=ctx.createLinearGradient(ox,oy,ox+pw,oy+ph);
+        pg.addColorStop(0,'#00ff88');pg.addColorStop(0.5,'#00e676');pg.addColorStop(1,'#00b894');
+        ctx.fillStyle=pg;ctx.fillRect(ox,oy,pw,ph);
+        ctx.shadowBlur=0;
+        ctx.strokeStyle='#b9f6ca';ctx.lineWidth=2;ctx.strokeRect(ox,oy,pw,ph);
+        // Eyes
+        ctx.fillStyle='#fff';
+        ctx.fillRect(ox+pw*0.15,oy+ph*0.2,pw*0.25,ph*0.22);
+        ctx.fillRect(ox+pw*0.6,oy+ph*0.2,pw*0.25,ph*0.22);
+        ctx.fillStyle='#0d1117';
+        ctx.fillRect(ox+pw*0.22,oy+ph*0.27,pw*0.12,ph*0.1);
+        ctx.fillRect(ox+pw*0.67,oy+ph*0.27,pw*0.12,ph*0.1);
+        // Mouth
+        ctx.fillStyle='#0d1117';
+        ctx.fillRect(ox+pw*0.3,oy+ph*0.6,pw*0.4,ph*0.08);
+        ctx.restore();
+    }
 }
 
 async function update(){
-    try{const r=await fetch('/step',{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify({action:pA})});const d=await r.json();draw(aC,d.ai,true);draw(pC,d.player,d.player.alive);document.getElementById('as').textContent=d.ai.score;document.getElementById('ps').textContent=d.player.score;document.getElementById('cc').textContent=d.player.coins_collected;document.getElementById('ep').textContent=d.epsilon.toFixed(3);document.getElementById('bs').textContent=d.best_score}catch(e){}
+    try{
+        const r=await fetch('/step',{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify({action:pA})});
+        const d=await r.json();
+        draw(aC,d.ai,true);draw(pC,d.player,d.player.alive);
+        document.getElementById('as').textContent=d.ai.score;
+        document.getElementById('ps').textContent=d.player.score;
+        document.getElementById('cc').textContent=d.player.coins_collected;
+        document.getElementById('ep').textContent=d.epsilon.toFixed(3);
+        document.getElementById('bs').textContent=d.best_score;
+    }catch(e){}
 }
+
 const sA=v=>{pA=v};
 document.getElementById('bL').onmousedown=()=>sA(1);document.getElementById('bL').onmouseup=()=>sA(0);
 document.getElementById('bR').onmousedown=()=>sA(2);document.getElementById('bR').onmouseup=()=>sA(0);
 document.getElementById('bJ').onmousedown=()=>sA(3);document.getElementById('bJ').onmouseup=()=>sA(0);
-document.addEventListener('keydown',e=>{if(e.key==='ArrowLeft'){e.preventDefault();sA(1)}else if(e.key==='ArrowRight'){e.preventDefault();sA(2)}else if(e.key==='ArrowUp'||e.key===' '){e.preventDefault();sA(3)}});
+document.addEventListener('keydown',e=>{
+    if(e.key==='ArrowLeft'){e.preventDefault();sA(1)}
+    else if(e.key==='ArrowRight'){e.preventDefault();sA(2)}
+    else if(e.key==='ArrowUp'||e.key===' '){e.preventDefault();sA(3)}
+});
 document.addEventListener('keyup',e=>{if(['ArrowLeft','ArrowRight','ArrowUp',' '].includes(e.key)){e.preventDefault();sA(0)}});
-document.getElementById('bReset').onclick=async()=>{const r=await fetch('/reset',{method:'POST'});const d=await r.json();draw(aC,d.ai,true);draw(pC,d.player,true);document.getElementById('as').textContent=d.ai.score;document.getElementById('ps').textContent=d.player.score};
+
+document.getElementById('bReset').onclick=async()=>{
+    const r=await fetch('/reset',{method:'POST'});const d=await r.json();
+    draw(aC,d.ai,true);draw(pC,d.player,true);
+    document.getElementById('as').textContent=d.ai.score;
+    document.getElementById('ps').textContent=d.player.score;
+};
 
 async function sendChat(){
     const inp=document.getElementById('ci');const msg=inp.value.trim();if(!msg)return;inp.value='';
-    const m=document.getElementById('msgs');m.innerHTML+=`<div class="u">👤 ${msg}</div>`;m.scrollTop=m.scrollHeight;
+    const m=document.getElementById('msgs');
+    m.innerHTML+=`<div class="u">👤 ${msg}</div>`;m.scrollTop=m.scrollHeight;
     const r=await fetch('/chat',{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify({message:msg})});
-    const d=await r.json();m.innerHTML+=`<div class="b">🧠 ${d.response}</div>`;m.scrollTop=m.scrollHeight;
+    const d=await r.json();
+    m.innerHTML+=`<div class="b">🧠 ${d.response}</div>`;m.scrollTop=m.scrollHeight;
+}
+
+async function startTrain(){
+    document.getElementById('ts').textContent='⏳ Запуск...';
+    const r=await fetch('/train',{method:'POST'});const d=await r.json();
+    document.getElementById('ts').textContent=d.message;
 }
-async function startTrain(){document.getElementById('ts').textContent='⏳ Запуск...';const r=await fetch('/train',{method:'POST'});const d=await r.json();document.getElementById('ts').textContent=d.message}
+
 document.querySelectorAll('.tab').forEach(t=>t.onclick=function(){
-    document.querySelectorAll('.tab').forEach(x=>x.classList.remove('active'));this.classList.add('active');const n=this.dataset.tab;
-    document.querySelectorAll('.tc>div').forEach(d=>d.classList.add('hidden'));document.getElementById(n+'Tab').classList.remove('hidden');
-    if(n==='stats')fetch('/stats').then(r=>r.json()).then(d=>{document.getElementById('sc').innerHTML=`<p>🧠 DQN шагов: ${d.steps}</p><p>📉 ε: ${d.epsilon}</p><p>🏆 Рекорд: ${d.best_score}</p><p>⚡ Тренируется: ${d.training?'✅':'❌'}</p><hr style="border-color:#30363d;margin:8px 0"><p>💬 Чат записей: ${d.chat.entries}</p><p>📝 Словарь: ${d.chat.vocab}</p><p>🔢 Параметров чата: ${d.chat.params.toLocaleString()}</p><p>📐 Эмбеддинг: ${d.chat.emb_dim}D</p>`});
+    document.querySelectorAll('.tab').forEach(x=>x.classList.remove('active'));
+    this.classList.add('active');const n=this.dataset.tab;
+    document.querySelectorAll('.tc>div').forEach(d=>d.classList.add('hidden'));
+    document.getElementById(n+'Tab').classList.remove('hidden');
+    if(n==='stats')fetch('/stats').then(r=>r.json()).then(d=>{
+        document.getElementById('sc').innerHTML=`
+            <p>🧠 DQN шагов: ${d.steps}</p>
+            <p>📉 ε: ${d.epsilon}</p>
+            <p>🏆 Рекорд: ${d.best_score}</p>
+            <p>⚡ Тренируется: ${d.training?'✅':'❌'}</p>
+            <hr style="border-color:#30363d;margin:8px 0">
+            <p>💬 Чат записей: ${d.chat.entries}</p>
+            <p>📝 Словарь: ${d.chat.vocab}</p>
+            <p>🔢 Параметров чата: ${d.chat.params.toLocaleString()}</p>
+            <p>📐 Эмбеддинг: ${d.chat.emb_dim}D</p>`;
+    });
 });
 
-// Load NN info
 fetch('/stats').then(r=>r.json()).then(d=>{
-    document.getElementById('nnInfo').textContent=`🧠 Чат-нейросеть: ${d.chat.params.toLocaleString()} параметров | Словарь: ${d.chat.vocab} | Эмбеддинг: ${d.chat.emb_dim}D | Записей: ${d.chat.entries}`;
+    document.getElementById('nnInfo').textContent=
+        `🧠 Чат-нейросеть: ${d.chat.params.toLocaleString()} параметров | Словарь: ${d.chat.vocab} | Эмбеддинг: ${d.chat.emb_dim}D | Записей: ${d.chat.entries}`;
 });
 
 setInterval(update,100);update();
@@ -724,6 +746,7 @@ setInterval(update,100);update();
 </html>
 """
 
+
 # ============================================================================
 # FLASK ROUTES
 # ============================================================================
@@ -741,7 +764,10 @@ def step():
     else: ai_env.reset()
     if pl_env.alive: pl_env.step(action)
     else: pl_env.reset()
-    return jsonify({'ai': ai_env.world_data(), 'player': pl_env.world_data(), 'epsilon': agent.eps, 'best_score': agent.best})
+    return jsonify({
+        'ai': ai_env.world_data(), 'player': pl_env.world_data(),
+        'epsilon': agent.eps, 'best_score': agent.best
+    })
 
 @app.route('/reset', methods=['POST'])
 def reset():
@@ -759,7 +785,7 @@ def chat_route():
         return jsonify({'response': chat.teach(p[0], p[1])})
     elif msg == '/stats':
         s = chat.stats
-        return jsonify({'response': f"🧠 Чат: {s['params']} парам., {s['vocab']} слов, {s['entries']} записей, {s['emb_dim']}D эмбеддинг"})
+        return jsonify({'response': f"🧠 Чат: {s['params']} парам., {s['vocab']} слов, {s['entries']} записей, {s['emb_dim']}D"})
     elif msg == '/train':
         return jsonify({'response': start_training()})
     else:
@@ -792,5 +818,6 @@ def start_training():
     threading.Thread(target=_t, daemon=True).start()
     return "🚀 Тренировка DQN запущена!"
 
+
 if __name__ == '__main__':
     app.run(host='0.0.0.0', port=C.PORT, debug=False)