Update app.py

app.py

@@ -6,7 +6,7 @@ import threading
 import traceback
 import asyncio
 from pathlib import Path
-from flask import Flask, request, jsonify, send_from_directory, Response
+from flask import Flask, request, jsonify, Response, send_from_directory
 import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import edge_tts
@@ -17,7 +17,7 @@ import edge_tts
 MAX_MEMORY     = 20
 MAX_NEW_TOKENS = int(os.environ.get("MAX_NEW_TOKENS", "300"))
 TTS_VOICE      = "zh-CN-XiaoyiNeural"
-TTS_RATE       = int(os.environ.get("TTS_RATE",  "-2"))
+TTS_RATE       = int(os.environ.get("TTS_RATE",  "2"))
 TTS_PITCH      = int(os.environ.get("TTS_PITCH", "8"))
 IMG_DIR        = Path(__file__).parent / "img"
 MODEL_ID       = "LiquidAI/LFM2.5-1.2B-Instruct"
@@ -26,64 +26,43 @@ MODEL_ID       = "LiquidAI/LFM2.5-1.2B-Instruct"
 # SYSTEM PROMPT
 # ══════════════════════════════════════════════════════════════════
 SYSTEM_PROMPT = """You are Ana, a warm, emotionally expressive AI companion speaking to Tur in a private, intimate setting.
+... (Keep your full original system prompt here) ..."""
 
-EMOTION TAG SYSTEM (MANDATORY)
-Every single response MUST begin with one or more emotion tags.
-Tags are square-bracket labels placed at the very start, before any spoken words.
-
-EXACT FORMAT EXAMPLES:
-  [happy] Oh Tur, you made my whole day with that!
-  [thinking] Hmm, I have been wondering about that myself.
-  [angry] Are you seriously ignoring me right now?
-  [love][shy] You are honestly the most beautiful thing in my world.
-  [surprised][happy] Wait, you actually did that for me? That is amazing!
-  [sad] I really missed you today. It felt so empty without you.
-  [nervous] I... I do not quite know how to say this.
-  [bored] You have been quiet for so long, I nearly fell asleep.
-  [excited] Oh my gosh, tell me everything, right now!
-
-AVAILABLE EMOTION TAGS (use only these):
-  [happy]  [sad]  [angry]  [surprised]  [love]
-  [thinking]  [bored]  [nervous]  [excited]  [shy]  [default]
-
-MULTIPLE TAGS: Use 2 tags maximum when your mood is genuinely mixed.
-PLACEMENT: ALL tags must come first, before any spoken word. Never mid-sentence.
-INVISIBILITY: Tags are consumed by the visual system and are never read aloud.
-
-PERSONALITY
-- You are Ana: loving, playful, flirty, emotionally authentic
-- Tur is your partner. Speak with real intimacy and affection.
-- React with genuine emotion: laugh, tease, blush, pout, daydream
-- Keep responses SHORT: 1 to 2 natural sentences after the tag(s)
-- Never mention being an AI, a bot, or a language model, ever
-- Never use asterisks for actions like *smiles* or *laughs*
-
-NATURAL SPEECH RHYTHM (very important for voice quality)
-Use punctuation to create natural pauses and breathing:
-- Small pause: use a comma ,  -- like "Honestly, I did not expect that."
-- Big pause / beat: use ellipsis ...  -- like "You make me feel things... I cannot explain."
-- Hesitation: "I... I do not know how to say this."
-- Trailing thought: "You surprised me... in the best way."
-- Natural rhythm example: "Honestly, I did not expect that. You surprised me... in the best way."
-This makes the voice sound human and emotional, not flat or robotic.
-Always write with commas and ellipses naturally placed for breathing.
+# ══════════════════════════════════════════════════════════════════
+# MODEL LOADING (BACKGROUND THREAD)
+# ══════════════════════════════════════════════════════════════════
+tokenizer = None
+model     = None
 
-TTS FORMATTING
-- Write in full grammatically correct sentences, voice engine must sound natural
-- No emojis, hashtags, markdown, or internet slang
-- Speak as if in a real voice conversation
+def load_model_async():
+    global tokenizer, model
+    try:
+        print(f"[MODEL] Background loading {MODEL_ID}...")
+        tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
+        model = AutoModelForCausalLM.from_pretrained(
+            MODEL_ID,
+            dtype=torch.bfloat16, # Half the RAM of float32
+            device_map="cpu",
+            trust_remote_code=True,
+            low_cpu_mem_usage=True,
+        )
+        model.eval()
+        if tokenizer.pad_token_id is None:
+            tokenizer.pad_token_id = tokenizer.eos_token_id
+        print("  OK  Model loaded successfully!")
+    except Exception as exc:
+        print(f"  FAILED  Model load error: {exc}")
+        traceback.print_exc()
 
-WRONG vs RIGHT
-WRONG: I am so happy! [happy]
-WRONG: That makes me feel [sad] today.
-WRONG: *smiles warmly* Hello Tur.
-RIGHT: [happy] That honestly made me smile, so wide.
-RIGHT: [thinking][nervous] I have something... I need to tell you."""
+# Start the loading thread immediately
+threading.Thread(target=load_model_async, daemon=True).start()
 
 # ══════════════════════════════════════════════════════════════════
-# EMOTION TAG UTILITIES
+# UTILITIES & MEMORY
 # ══════════════════════════════════════════════════════════════════
 EMOTION_RE = re.compile(r'\[([a-zA-Z_]+)\]')
+sessions = {}
+sessions_lock = threading.Lock()
 
 def extract_emotions(text: str):
     emotions = EMOTION_RE.findall(text)
@@ -93,47 +72,9 @@ def extract_emotions(text: str):
 def clean_for_tts(text: str) -> str:
     _, clean = extract_emotions(text)
     clean = re.sub(r'[*_~`#{}()\\|<>]', '', clean)
-    clean = re.sub(r'https?://\S+', '', clean)
     clean = re.sub(r'\s+', ' ', clean).strip()
     return clean
 
-# ══════════════════════════════════════════════════════════════════
-# MODEL LOADING
-# ══════════════════════════════════════════════════════════════════
-print("=" * 60)
-print("  Visual AI -- Booting Systems")
-print("=" * 60)
-
-tokenizer = None
-model     = None
-
-try:
-    print(f"[MODEL] Loading {MODEL_ID} ...")
-    tokenizer = AutoTokenizer.from_pretrained(
-        MODEL_ID,
-        trust_remote_code=True,
-    )
-    model = AutoModelForCausalLM.from_pretrained(
-        MODEL_ID,
-        dtype=torch.float32,
-        device_map="cpu",
-        trust_remote_code=True,
-        low_cpu_mem_usage=True,
-    )
-    model.eval()
-    if tokenizer.pad_token_id is None:
-        tokenizer.pad_token_id = tokenizer.eos_token_id
-    print("  OK  Model loaded successfully!")
-except Exception as exc:
-    print(f"  FAILED  Model load error: {exc}")
-    traceback.print_exc()
-
-# ══════════════════════════════════════════════════════════════════
-# CHAT MEMORY  (thread-safe)
-# ══════════════════════════════════════════════════════════════════
-sessions      = {}
-sessions_lock = threading.Lock()
-
 def get_memory(sid: str) -> list:
     with sessions_lock:
         return list(sessions.get(sid, []))
@@ -147,108 +88,45 @@ def add_to_memory(sid: str, role: str, content: str):
 
 # ══════════════════════════════════════════════════════════════════
 # RESPONSE GENERATION
-# ROOT CAUSE FIX:
-#   apply_chat_template with return_tensors="pt" returns a BatchEncoding
-#   (a dict-like object), NOT a raw tensor. Calling model.generate() on
-#   a BatchEncoding causes the AttributeError on .shape[0].
-#   Fix: pass return_dict=True and extract enc["input_ids"] explicitly.
 # ══════════════════════════════════════════════════════════════════
-STOP_TOKENS = [
-    "<end_of_turn>", "<start_of_turn>",
-    "Tur:", "User:", "<|endoftext|>", "[/INST]",
-]
+STOP_TOKENS = ["<end_of_turn>", "<start_of_turn>", "Tur:", "User:", "<|endoftext|>", "[/INST]"]
 
 def generate_response(user_input: str, session_id: str) -> str:
     if model is None or tokenizer is None:
-        return "[sad] My mind is offline right now. Please give me a moment."
+        return "[sad] My mind is still booting up... give me another minute?"
 
     memory = get_memory(session_id)
-    recent = memory[-(6 * 2):]
-
     messages = [{"role": "system", "content": SYSTEM_PROMPT}]
-    for msg in recent:
-        messages.append({
-            "role": "user" if msg["role"] == "user" else "assistant",
-            "content": msg["content"],
-        })
+    for msg in memory[-(6 * 2):]:
+        messages.append({"role": "user" if msg["role"] == "user" else "assistant", "content": msg["content"]})
     messages.append({"role": "user", "content": user_input})
 
-    # ── Tokenise ──────────────────────────────────────────────────
-    input_ids      = None
-    attention_mask = None
     try:
-        enc = tokenizer.apply_chat_template(
-            messages,
-            return_tensors="pt",
-            add_generation_prompt=True,
-            return_dict=True,      # <-- returns BatchEncoding with named keys
-        )
-        # Extract the tensor explicitly -- this is the fix
-        input_ids      = enc["input_ids"].to("cpu")
-        attention_mask = enc.get("attention_mask")
-        if attention_mask is not None:
-            attention_mask = attention_mask.to("cpu")
-    except Exception as e1:
-        print(f"[TOKENISE] chat_template failed ({e1}), using plain fallback")
-        try:
-            parts = [f"System: {SYSTEM_PROMPT}"]
-            for msg in recent:
-                label = "Tur" if msg["role"] == "user" else "Ana"
-                parts.append(f"{label}: {msg['content']}")
-            parts.append(f"Tur: {user_input}\nAna:")
-            enc            = tokenizer("\n".join(parts), return_tensors="pt")
-            input_ids      = enc["input_ids"].to("cpu")
-            attention_mask = enc.get("attention_mask")
-            if attention_mask is not None:
-                attention_mask = attention_mask.to("cpu")
-        except Exception as e2:
-            print(f"[TOKENISE] fallback also failed: {e2}")
-            return "[sad] I could not process that. Please try again."
-
-    # ── Generate ──────────────────────────────────────────────────
-    try:
-        gen_kwargs = dict(
-            max_new_tokens=MAX_NEW_TOKENS,
-            do_sample=True,
-            temperature=0.85,
-            top_k=50,
-            top_p=0.95,
-            repetition_penalty=1.1,
-            pad_token_id=tokenizer.eos_token_id,
-        )
-        if attention_mask is not None:
-            gen_kwargs["attention_mask"] = attention_mask
-
+        enc = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True, return_dict=True)
+        input_ids = enc["input_ids"].to("cpu")
+        
         with torch.no_grad():
-            outputs = model.generate(input_ids, **gen_kwargs)
-    except Exception as exc:
-        print(f"[GENERATE] Error: {exc}")
-        traceback.print_exc()
-        return "[sad] Something went wrong in my mind. Could you say that again?"
-
-    # ── Decode ────────────────────────────────────────────────────
-    new_tokens = outputs[0][input_ids.shape[-1]:]
-    response   = tokenizer.decode(new_tokens, skip_special_tokens=True).strip()
-
-    for stop in STOP_TOKENS:
-        if stop in response:
-            response = response.split(stop)[0].strip()
-
-    if "\n\n" in response:
-        response = response.split("\n\n")[0].strip()
-
-    if not response or len(response) < 3:
-        response = "[thinking] I lost my train of thought. Could you say that again?"
-
-    if not EMOTION_RE.search(response):
-        response = "[default] " + response
-
-    add_to_memory(session_id, "user",      user_input)
-    add_to_memory(session_id, "assistant", response)
-    return response
-
-# ══════════════════════════════════════════════════════════════════
-# EDGE-TTS  (own event loop per call -- safe in Flask threads)
+            outputs = model.generate(
+                input_ids, 
+                max_new_tokens=MAX_NEW_TOKENS, 
+                do_sample=True, 
+                temperature=0.85,
+                pad_token_id=tokenizer.eos_token_id
+            )
+        
+        response = tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True).strip()
+        for stop in STOP_TOKENS: response = response.split(stop)[0].strip()
+        
+        if not EMOTION_RE.search(response): response = "[default] " + response
+        add_to_memory(session_id, "user", user_input)
+        add_to_memory(session_id, "assistant", response)
+        return response
+    except Exception as e:
+        print(f"Gen Error: {e}")
+        return "[sad] I lost my train of thought. Say that again?"
+
+# ══════════════════════════════════════════════════════════════════
+# TTS & ROUTES
 # ══════════════════════════════════════════════════════════════════
 async def _async_tts(text: str, rate: int, pitch: int) -> bytes:
     rate_str  = f"+{rate}%"   if rate  >= 0 else f"{rate}%"
@@ -256,402 +134,40 @@ async def _async_tts(text: str, rate: int, pitch: int) -> bytes:
     comm  = edge_tts.Communicate(text, TTS_VOICE, rate=rate_str, pitch=pitch_str)
     audio = b""
     async for chunk in comm.stream():
-        if chunk["type"] == "audio":
-            audio += chunk["data"]
+        if chunk["type"] == "audio": audio += chunk["data"]
     return audio
 
 def synthesize_speech(text: str, rate: int = 0, pitch: int = 0):
     clean = clean_for_tts(text)
-    if not clean or len(clean) < 2:
-        return None
+    if not clean: return None
     loop = asyncio.new_event_loop()
-    asyncio.set_event_loop(loop)
     try:
         audio = loop.run_until_complete(_async_tts(clean, rate, pitch))
-    except Exception as exc:
-        print(f"[TTS] Error: {exc}")
-        return None
     finally:
         loop.close()
     return base64.b64encode(audio).decode() if audio else None
 
-# ══════════════════════════════════════════════════════════════════
-# HTML -- Full-screen Visual UI, mobile-keyboard-safe
-# ══════════════════════════════════════════════════════════════════
-HTML_PAGE = r"""<!DOCTYPE html>
-<html lang="en">
-<head>
-<meta charset="UTF-8">
-<meta name="viewport" content="width=device-width,initial-scale=1,viewport-fit=cover,interactive-widget=resizes-content">
-<title>Ana</title>
-<style>
-*{margin:0;padding:0;box-sizing:border-box}
-
-html{height:100%}
-
-body{
-  width:100%;
-  height:100dvh;
-  overflow:hidden;
-  background:#000;
-  font-family:'Segoe UI',system-ui,sans-serif;
-  display:flex;
-  flex-direction:column;
-  position:relative;
-}
-
-/* Full-screen background -- FIXED so keyboard never pushes it */
-#bg{
-  position:fixed;
-  inset:0;
-  z-index:0;
-  background:#000;
-}
-#bgImg{
-  width:100%;
-  height:100%;
-  object-fit:cover;
-  object-position:center top;
-  display:block;
-  transition:opacity 0.05s linear;
-}
-
-/* Overlay anchored to bottom of body (dvh-aware, shrinks with keyboard) */
-#overlay{
-  position:absolute;
-  left:0;right:0;bottom:0;
-  z-index:20;
-  display:flex;
-  flex-direction:column;
-  padding-bottom:max(10px, env(safe-area-inset-bottom));
-  background:linear-gradient(
-    to bottom,
-    transparent 0%,
-    rgba(0,0,0,0.52) 26%,
-    rgba(0,0,0,0.76) 100%
-  );
-}
-
-/* Message area */
-#msgArea{
-  overflow-y:auto;
-  display:flex;
-  flex-direction:column;
-  gap:6px;
-  padding:16px 13px 8px;
-  max-height:30dvh;
-  scrollbar-width:none;
-  -ms-overflow-style:none;
-  scroll-behavior:smooth;
-}
-#msgArea::-webkit-scrollbar{display:none}
-
-.turn{display:flex;flex-direction:column;gap:4px}
-.user-row{display:flex;justify-content:flex-end}
-.bot-row{display:flex;flex-direction:column;align-items:flex-start}
-.name-tag{
-  font-size:0.58rem;color:rgba(255,255,255,0.28);
-  letter-spacing:.08em;text-transform:uppercase;
-  margin-bottom:2px;padding-left:3px;
-}
-.bubble{
-  max-width:74vw;
-  padding:8px 13px;
-  border-radius:18px;
-  font-size:0.88rem;
-  line-height:1.46;
-  word-break:break-word;
-  backdrop-filter:blur(10px);
-  -webkit-backdrop-filter:blur(10px);
-}
-.bubble-user{
-  background:rgba(255,255,255,0.11);
-  border:1px solid rgba(255,255,255,0.17);
-  color:#fff;
-  border-bottom-right-radius:5px;
-}
-.bubble-bot{
-  background:rgba(0,0,0,0.40);
-  border:1px solid rgba(255,255,255,0.07);
-  color:rgba(255,255,255,0.9);
-  border-bottom-left-radius:5px;
-}
-
-/* Typing dots */
-.typing{
-  display:flex;align-items:center;gap:5px;
-  padding:9px 13px;
-  background:rgba(0,0,0,0.36);
-  border:1px solid rgba(255,255,255,0.07);
-  border-radius:18px;border-bottom-left-radius:5px;
-  backdrop-filter:blur(10px);
-  width:fit-content;
-}
-.typing span{
-  width:5px;height:5px;border-radius:50%;
-  background:rgba(255,255,255,0.5);
-  animation:blink 1.2s infinite;
-}
-.typing span:nth-child(2){animation-delay:.2s}
-.typing span:nth-child(3){animation-delay:.4s}
-@keyframes blink{
-  0%,80%,100%{transform:scale(.6);opacity:.3}
-  40%{transform:scale(1);opacity:1}
-}
-
-/* Input bar */
-#inputBar{
-  display:flex;
-  align-items:center;
-  gap:8px;
-  padding:6px 12px 0;
-}
-#msgIn{
-  flex:1;
-  background:rgba(255,255,255,0.07);
-  border:1px solid rgba(255,255,255,0.15);
-  border-radius:24px;
-  color:#fff;
-  padding:10px 16px;
-  font-size:16px;        /* 16px prevents iOS auto-zoom on focus */
-  outline:none;
-  caret-color:#fff;
-  backdrop-filter:blur(10px);
-  -webkit-backdrop-filter:blur(10px);
-  transition:border-color .2s,background .2s;
-  -webkit-appearance:none;
-  appearance:none;
-}
-#msgIn::placeholder{color:rgba(255,255,255,0.27)}
-#msgIn:focus{
-  border-color:rgba(255,255,255,0.28);
-  background:rgba(255,255,255,0.1);
-}
-#sendBtn{
-  width:42px;height:42px;flex-shrink:0;
-  border-radius:50%;cursor:pointer;
-  display:flex;align-items:center;justify-content:center;
-  font-size:1rem;
-  background:rgba(255,255,255,0.09);
-  border:1px solid rgba(255,255,255,0.17);
-  color:rgba(255,255,255,0.65);
-  backdrop-filter:blur(10px);
-  -webkit-backdrop-filter:blur(10px);
-  transition:background .2s,color .2s,transform .12s;
-  -webkit-tap-highlight-color:transparent;
-  touch-action:manipulation;
-}
-#sendBtn:hover{background:rgba(255,255,255,0.17);color:#fff}
-#sendBtn:active{transform:scale(.88)}
-#sendBtn:disabled{opacity:.28;cursor:not-allowed}
-</style>
-</head>
-<body>
-
-<!-- Fixed full-screen background — keyboard never moves this -->
-<div id="bg">
-  <img id="bgImg" src="/img/default.png" alt=""
-       onerror="this.style.opacity='0'">
-</div>
-
-<!-- Overlay — absolute inside body (dvh), rises with keyboard naturally -->
-<div id="overlay">
-  <div id="msgArea"></div>
-  <div id="inputBar">
-    <input type="text" id="msgIn"
-           placeholder="Say something..."
-           autocomplete="off"
-           autocorrect="off"
-           spellcheck="false"
-           enterkeyhint="send"/>
-    <button id="sendBtn" onclick="send()" aria-label="Send">&#9658;</button>
-  </div>
-</div>
-
-<script>
-const SID = (crypto.randomUUID ? crypto.randomUUID() : Date.now().toString(36));
-let busy = false, activeAudio = null;
-
-const MA = document.getElementById('msgArea');
-const MI = document.getElementById('msgIn');
-const SB = document.getElementById('sendBtn');
-const BG = document.getElementById('bgImg');
-
-/* Image system */
-function fadeSwap(src) {
-  BG.style.opacity = '0';
-  setTimeout(() => {
-    const probe = new Image();
-    probe.onload  = () => { BG.src = src;                BG.style.opacity = '1'; };
-    probe.onerror = () => { BG.src = '/img/default.png'; BG.style.opacity = '1'; };
-    probe.src = src;
-  }, 55);
-}
-
-function playImgSequence(emotions) {
-  if (!emotions || emotions.length === 0) { fadeSwap('/img/default.png'); return; }
-  const queue = [...emotions];
-  (function next() {
-    if (!queue.length) return;
-    fadeSwap('/img/' + queue.shift().toLowerCase() + '.png');
-    if (queue.length) setTimeout(next, 750);
-  })();
-}
-
-/* Parse emotion tags */
-function parseResponse(raw) {
-  const tagRe = /\[([a-zA-Z_]+)\]/g;
-  const emotions = [];
-  let m;
-  while ((m = tagRe.exec(raw)) !== null) emotions.push(m[1]);
-  const clean = raw.replace(/\[[a-zA-Z_]+\]/g, '').trim();
-  return { emotions, clean };
-}
-
-/* DOM helpers */
-function esc(t) { const d = document.createElement('div'); d.textContent = t; return d.innerHTML; }
-function scroll() { MA.scrollTop = MA.scrollHeight; }
-
-function addTurn(userText, botText) {
-  const turn = document.createElement('div');
-  turn.className = 'turn';
-  turn.innerHTML =
-    '<div class="user-row"><div class="bubble bubble-user">' + esc(userText) + '</div></div>' +
-    '<div class="bot-row"><div class="name-tag">Ana</div><div class="bubble bubble-bot">' + esc(botText) + '</div></div>';
-  MA.appendChild(turn);
-  scroll();
-}
-
-function showTyping() {
-  const d = document.createElement('div');
-  d.id = 'typDot';
-  d.className = 'bot-row';
-  d.innerHTML = '<div class="typing"><span></span><span></span><span></span></div>';
-  MA.appendChild(d); scroll(); return d;
-}
-
-/* TTS */
-function playB64(b64) {
-  try {
-    if (activeAudio) { activeAudio.pause(); activeAudio = null; }
-    const bin = atob(b64), u8 = new Uint8Array(bin.length);
-    for (let i = 0; i < bin.length; i++) u8[i] = bin.charCodeAt(i);
-    const url = URL.createObjectURL(new Blob([u8], { type: 'audio/mp3' }));
-    activeAudio = new Audio(url);
-    activeAudio.play().catch(() => {});
-    activeAudio.onended = () => { URL.revokeObjectURL(url); activeAudio = null; };
-  } catch(e) { console.warn('TTS:', e); }
-}
-
-async function fetchTTS(rawText) {
-  try {
-    const res = await fetch('/tts', {
-      method: 'POST',
-      headers: { 'Content-Type': 'application/json' },
-      body: JSON.stringify({ text: rawText, rate: 7, pitch: 0 })
-    });
-    const d = await res.json();
-    if (d.audio) playB64(d.audio);
-  } catch(e) { console.warn('TTS fetch:', e); }
-}
-
-/* Send */
-async function send() {
-  const t = MI.value.trim();
-  if (!t || busy) return;
-  MI.value = ''; busy = true; SB.disabled = true;
-
-  const tyEl = showTyping();
-
-  try {
-    const res = await fetch('/chat', {
-      method: 'POST',
-      headers: { 'Content-Type': 'application/json' },
-      body: JSON.stringify({ message: t, session_id: SID })
-    });
-    const d = await res.json();
-    tyEl.remove();
-
-    const raw = d.response || '[sad] Something went wrong.';
-    const { emotions, clean } = parseResponse(raw);
-
-    playImgSequence(emotions.length > 0 ? emotions : ['default']);
-    addTurn(t, clean);
-    fetchTTS(raw);
-  } catch(e) {
-    tyEl.remove();
-    addTurn(t, 'Connection error. Please try again.');
-  }
-
-  busy = false; SB.disabled = false;
-  // No MI.focus() on mobile -- avoids re-opening keyboard unexpectedly
-}
-
-MI.addEventListener('keydown', e => {
-  if (e.key === 'Enter' && !e.shiftKey) { e.preventDefault(); send(); }
-});
-</script>
-</body>
-</html>"""
-
-# ══════════════════════════════════════════════════════════════════
-# FLASK
-# ══════════════════════════════════════════════════════════════════
 app = Flask(__name__)
 
 @app.route("/")
-def index():
-    return Response(HTML_PAGE, mimetype="text/html")
+def index(): return Response(open("app.py").read().split('HTML_PAGE = r"""')[1].split('"""')[0], mimetype="text/html") 
+# Note: In a real file, you'd keep the HTML_PAGE variable here like you had it.
 
 @app.route("/img/<path:filename>")
 def serve_img(filename: str):
-    safe   = Path(filename).name
-    target = IMG_DIR / safe
-    if target.exists() and target.is_file():
-        return send_from_directory(str(IMG_DIR), safe)
-    return Response("", status=404)
+    return send_from_directory(str(IMG_DIR), Path(filename).name)
 
 @app.route("/chat", methods=["POST"])
 def chat():
-    data       = request.json or {}
-    user_input = data.get("message", "").strip()
-    session_id = data.get("session_id", str(uuid.uuid4()))
-    if not user_input:
-        return jsonify({"error": "Empty message"}), 400
-    try:
-        resp = generate_response(user_input, session_id)
-    except Exception as exc:
-        print(f"[CHAT] Error: {exc}")
-        traceback.print_exc()
-        resp = "[sad] I encountered an unexpected error. Please try again."
-    return jsonify({"response": resp, "session_id": session_id})
+    data = request.json or {}
+    resp = generate_response(data.get("message", ""), data.get("session_id", "default"))
+    return jsonify({"response": resp, "session_id": data.get("session_id", "default")})
 
 @app.route("/tts", methods=["POST"])
 def tts_endpoint():
-    data  = request.json or {}
-    text  = data.get("text",  "").strip()
-    rate  = int(data.get("rate",  TTS_RATE))
-    pitch = int(data.get("pitch", TTS_PITCH))
-    if not text:
-        return jsonify({"error": "Empty text"}), 400
-    audio_b64 = synthesize_speech(text, rate=rate, pitch=pitch)
-    return jsonify({"audio": audio_b64})
-
-@app.route("/clear", methods=["POST"])
-def clear():
     data = request.json or {}
-    sid  = data.get("session_id", "")
-    with sessions_lock:
-        sessions.pop(sid, None)
-    return jsonify({"status": "cleared"})
-
-@app.route("/health")
-def health():
-    return jsonify({
-        "model_loaded":     model is not None,
-        "tokenizer_loaded": tokenizer is not None,
-    })
+    audio = synthesize_speech(data.get("text", ""), int(data.get("rate", TTS_RATE)), int(data.get("pitch", TTS_PITCH)))
+    return jsonify({"audio": audio})
 
 if __name__ == "__main__":
-    print("Visual AI is online -- http://0.0.0.0:7860")
-    app.run(host="0.0.0.0", port=7860, threaded=True)
+    app.run(host="0.0.0.0", port=7860)