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Update app.py
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app.py
CHANGED
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@@ -16,11 +16,11 @@ tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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model.eval()
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# 🔹
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MAX_HISTORY = 40
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queue = []
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class Message(BaseModel):
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@@ -28,7 +28,6 @@ class Message(BaseModel):
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def generate_ai(message: str):
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# 🔥 Улучшенный prompt
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prompt = f"User: {message}\nAssistant: Answer clearly and fully:\n"
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inputs = tokenizer(prompt, return_tensors="pt")
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@@ -37,14 +36,13 @@ def generate_ai(message: str):
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outputs = model.generate(
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**inputs,
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max_new_tokens=60,
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min_new_tokens=20,
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do_sample=True,
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temperature=0.7,
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top_p=0.9,
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eos_token_id=tokenizer.eos_token_id
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)
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# 🔥 ВАЖНО: декодим только НОВЫЕ токены
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input_length = inputs.input_ids.shape[1]
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generated_tokens = outputs[0][input_length:]
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@@ -53,23 +51,29 @@ def generate_ai(message: str):
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return reply
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# 🔥
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def worker():
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while True:
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if queue:
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message = queue.pop(0)
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reply = generate_ai(message)
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if message in db:
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db[message]["status"] = "done"
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db[message]["reply"] = reply
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# запускаем
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@app.get("/")
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@@ -81,11 +85,14 @@ async def root():
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@app.get("/ask")
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async def ask(message: str):
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if message not in db:
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db[message] = {"status": "pending", "reply": ""}
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queue.append(message)
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# ограничение до 40
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if len(db) > MAX_HISTORY:
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db.popitem(last=False)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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model.eval()
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# 🔹 настройки
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MAX_HISTORY = 40
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NUM_WORKERS = 3 # 🔥 ВАЖНО: количество потоков
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db = OrderedDict()
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queue = []
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class Message(BaseModel):
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def generate_ai(message: str):
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prompt = f"User: {message}\nAssistant: Answer clearly and fully:\n"
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inputs = tokenizer(prompt, return_tensors="pt")
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outputs = model.generate(
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**inputs,
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max_new_tokens=60,
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min_new_tokens=20,
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do_sample=True,
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temperature=0.7,
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top_p=0.9,
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eos_token_id=tokenizer.eos_token_id
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)
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input_length = inputs.input_ids.shape[1]
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generated_tokens = outputs[0][input_length:]
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return reply
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# 🔥 Поток-воркер
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def worker():
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while True:
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if queue:
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message = queue.pop(0)
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# ⚡ если уже есть ответ — пропускаем
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if message in db and db[message]["status"] == "done":
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continue
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reply = generate_ai(message)
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if message in db:
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db[message]["status"] = "done"
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db[message]["reply"] = reply
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else:
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time.sleep(0.01) # 🔥 меньше лаг
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# 🔥 запускаем несколько воркеров
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for _ in range(NUM_WORKERS):
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threading.Thread(target=worker, daemon=True).start()
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@app.get("/")
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@app.get("/ask")
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async def ask(message: str):
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# ⚡ МГНОВЕННЫЙ КЭШ
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if message in db and db[message]["status"] == "done":
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return PlainTextResponse("cached")
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if message not in db:
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db[message] = {"status": "pending", "reply": ""}
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queue.append(message)
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if len(db) > MAX_HISTORY:
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db.popitem(last=False)
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