New stcructure v1.5

core/analyzer.py

@@ -9,7 +9,7 @@ Purpose: Handles single-portfolio analysis using LLM. Fetches metrics, builds pr
 import asyncio
 from typing import Generator
 from services.tradelink_api import extract_portfolio_id, fetch_metrics_async
-from services.llm_client import llm_service  # Hugging Face client
+from services.llm_client import llm_service
 from prompts.system_prompts import ANALYSIS_SYSTEM_PROMPT
 from prompts.reference_templates import REFERENCE_PROMPT
 
@@ -17,8 +17,7 @@ from prompts.reference_templates import REFERENCE_PROMPT
 class PortfolioAnalyzer:
     """Main use-case class for analyzing a single portfolio."""
 
-    def __init__(self, llm=llm_service, model_name: str = "mistralai/Mixtral-8x7B-Instruct"):
-        # llm must expose .stream_chat(messages=list[dict], model=str) -> Iterable[str]
+    def __init__(self, llm=llm_service, model_name: str = "meta-llama/Meta-Llama-3.1-8B-Instruct"):
         self.llm = llm
         self.model_name = model_name
 
@@ -26,18 +25,18 @@ class PortfolioAnalyzer:
         """Stream analysis result step by step."""
         portfolio_id = extract_portfolio_id(text)
         if not portfolio_id:
-            yield "❗ Please provide a valid portfolio ID or link."
+            yield "❗ Пожалуйста, введите корректный portfolio ID или ссылку."
             return
 
-        yield "⏳ Fetching portfolio metrics..."
+        yield "⏳ Получаю метрики портфеля..."
         try:
             metrics = asyncio.run(fetch_metrics_async(portfolio_id))
         except Exception as e:
-            yield f"❌ Failed to fetch metrics: {e}"
+            yield f"❌ Ошибка при получении метрик: {e}"
             return
 
         if not metrics:
-            yield "❗ Unable to retrieve metrics from API."
+            yield "❗ Не удалось получить данные по метрикам."
             return
 
         # Build prompt text from metrics
@@ -56,4 +55,4 @@ class PortfolioAnalyzer:
                 yield partial
 
         except Exception as e:
-            yield f"❌ Error during LLM analysis: {e}"
+            yield f"❌ Ошибка при работе LLM: {e}"

core/chat.py

@@ -7,14 +7,14 @@ Purpose: General chat interface for user questions about TradeLink and its ecosy
 """
 
 from typing import Generator
-from services.llm_client import LLMAdapter
+from services.llm_client import llm_service
 from prompts.system_prompts import TRADELINK_CONTEXT
 
 
 class ChatAssistant:
-    """Handles generic user dialogue via LLM."""
+    """Handles general user dialogue via LLM."""
 
-    def __init__(self, llm: LLMAdapter, model_name: str):
+    def __init__(self, llm=llm_service, model_name: str = "meta-llama/Meta-Llama-3.1-8B-Instruct"):
         self.llm = llm
         self.model_name = model_name
 
@@ -25,7 +25,11 @@ class ChatAssistant:
             {"role": "user", "content": user_input},
         ]
 
-        partial = ""
-        for delta in self.llm.stream_chat(messages=messages, model=self.model_name):
-            partial += delta
-            yield partial
+        try:
+            partial = ""
+            for delta in self.llm.stream_chat(messages=messages, model=self.model_name):
+                partial += delta
+                yield partial
+
+        except Exception as e:
+            yield f"❌ Ошибка при генерации ответа: {e}"

core/comparer.py

@@ -3,13 +3,13 @@
 Purpose: Compares two portfolios using LLM. Fetches metrics for both and builds a unified comparison prompt.
 
 🇷🇺 Модуль: comparer.py
-Назначение: сравнение двух портфелей с помощью LLM. Получает метрики обоих портфелей, формирует промпт и выводит стриминговый результат.
+Назначение: сравнение двух портфелей с помощью LLM. Получает метрики обоих портфелей, формирует промпт и возвращает потоковый результат.
 """
 
 import asyncio
 from typing import Generator
 from services.tradelink_api import extract_portfolio_id, fetch_metrics_async
-from services.llm_client import LLMAdapter
+from services.llm_client import llm_service
 from prompts.system_prompts import COMPARISON_SYSTEM_PROMPT
 from prompts.reference_templates import REFERENCE_COMPARISON_PROMPT
 
@@ -17,7 +17,7 @@ from prompts.reference_templates import REFERENCE_COMPARISON_PROMPT
 class PortfolioComparer:
     """Main use-case class for comparing two portfolios."""
 
-    def __init__(self, llm: LLMAdapter, model_name: str):
+    def __init__(self, llm=llm_service, model_name: str = "meta-llama/Meta-Llama-3.1-8B-Instruct"):
         self.llm = llm
         self.model_name = model_name
 
@@ -27,26 +27,26 @@ class PortfolioComparer:
         id2 = extract_portfolio_id(text2)
 
         if text1 == text2:
-            yield "❗ Please provide two different portfolio IDs."
+            yield "❗ Пожалуйста, введите два разных portfolio ID."
             return
         if not id1 or not id2:
-            yield "❗ One or both portfolio IDs are invalid."
+            yield "❗ Один или оба portfolio ID некорректны."
             return
 
-        yield "⏳ Fetching metrics for both portfolios..."
+        yield "⏳ Получаю метрики для обоих портфелей..."
         try:
             m1 = asyncio.run(fetch_metrics_async(id1))
             m2 = asyncio.run(fetch_metrics_async(id2))
         except Exception as e:
-            yield f"❌ Failed to fetch data: {e}"
+            yield f"❌ Ошибка при получении данных: {e}"
             return
 
         if not m1 or not m2:
-            yield "❗ Metrics missing for one or both portfolios."
+            yield "❗ Не удалось получить метрики хотя бы одного портфеля."
             return
 
-        m1_text = ", ".join([f"{k}: {v}" for k, v in m1.items()])
-        m2_text = ", ".join([f"{k}: {v}" for k, v in m2.items()])
+        m1_text = ", ".join(f"{k}: {v}" for k, v in m1.items())
+        m2_text = ", ".join(f"{k}: {v}" for k, v in m2.items())
 
         prompt = (
             f"{REFERENCE_COMPARISON_PROMPT}\n"
@@ -67,4 +67,4 @@ class PortfolioComparer:
                 yield partial
 
         except Exception as e:
-            yield f"❌ Error during LLM comparison: {e}"
+            yield f"❌ Ошибка при сравнении портфелей через LLM: {e}"