v1.6.0: max_bot fixes — feature keys, flush→commit, test-run, categories, broadcast page, proxy error handling, deploy scripts

2026-05-24 07:50:38 +03:00
parent bd048ea23d
commit 493e0b37a1
127 changed files with 6082 additions and 65 deletions
@@ -0,0 +1,80 @@
+import aiohttp
+import logging
+from app.settings_cache import SettingsCache
+
+logger = logging.getLogger(__name__)
+
+
+async def categorize_with_gpt(text: str) -> str:
+    gpt_key = SettingsCache.get("yandex_gpt_key")
+    folder_id = SettingsCache.get("yandex_folder_id")
+    if not gpt_key or not folder_id:
+        return ""
+
+    prompt = (
+        "Определи категорию обращения клиента из списка: "
+        "Видеонаблюдение, СКУД, Пожарная сигнализация, IT-инфраструктура, "
+        "Обслуживание, Консультация, Другое. "
+        "Ответь только названием категории, без пояснений. "
+        f"Текст обращения: {text}"
+    )
+
+    url = "https://llm.api.cloud.yandex.net/foundationModels/v1/completion"
+    headers = {
+        "Authorization": f"Api-Key {gpt_key}",
+        "Content-Type": "application/json",
+        "x-folder-id": folder_id,
+    }
+    body = {
+        "modelUri": f"gpt://{folder_id}/yandexgpt-lite/latest",
+        "completionOptions": {"stream": False, "temperature": 0.1, "maxTokens": 50},
+        "messages": [{"role": "user", "text": prompt}],
+    }
+
+    try:
+        async with aiohttp.ClientSession() as session:
+            async with session.post(url, headers=headers, json=body) as resp:
+                if resp.status == 200:
+                    data = await resp.json()
+                    return data.get("result", {}).get("alternatives", [{}])[0].get("message", {}).get("text", "").strip()
+                logger.warning(f"GPT categorize returned {resp.status}")
+    except Exception as e:
+        logger.error(f"GPT categorize error: {e}")
+    return ""
+
+
+async def analyze_emotion_with_gpt(text: str) -> str:
+    gpt_key = SettingsCache.get("yandex_gpt_key")
+    folder_id = SettingsCache.get("yandex_folder_id")
+    if not gpt_key or not folder_id:
+        return "neutral"
+
+    prompt = (
+        "Определи эмоциональную окраску текста клиента. "
+        "Варианты: positive, neutral, negative. "
+        "Ответь только одним словом. "
+        f"Текст: {text}"
+    )
+
+    url = "https://llm.api.cloud.yandex.net/foundationModels/v1/completion"
+    headers = {
+        "Authorization": f"Api-Key {gpt_key}",
+        "Content-Type": "application/json",
+        "x-folder-id": folder_id,
+    }
+    body = {
+        "modelUri": f"gpt://{folder_id}/yandexgpt-lite/latest",
+        "completionOptions": {"stream": False, "temperature": 0.1, "maxTokens": 10},
+        "messages": [{"role": "user", "text": prompt}],
+    }
+
+    try:
+        async with aiohttp.ClientSession() as session:
+            async with session.post(url, headers=headers, json=body) as resp:
+                if resp.status == 200:
+                    data = await resp.json()
+                    return data.get("result", {}).get("alternatives", [{}])[0].get("message", {}).get("text", "").strip().lower()
+                logger.warning(f"GPT emotion analysis returned {resp.status}")
+    except Exception as e:
+        logger.error(f"GPT emotion analysis error: {e}")
+    return "neutral"