New stcructure v1.2

.idea/vcs.xml

@@ -2,6 +2,5 @@
 <project version="4">
   <component name="VcsDirectoryMappings">
     <mapping directory="" vcs="Git" />
-    <mapping directory="$PROJECT_DIR$/AIassistance" vcs="Git" />
   </component>
 </project>

app.py

@@ -7,7 +7,7 @@ from core.visualization import build_alpha_chart
 from core.metrics import show_metrics_table
 
 # === Model setup ===
-MODEL_NAME = "meta-llama/Meta-Llama-3.1-8B-Instruct"
+MODEL_NAME = "mistralai/Mixtral-8x7B-Instruct"
 
 # === Use case classes ===
 analyzer = PortfolioAnalyzer(llm_service, MODEL_NAME)

config.py

@@ -1,21 +1,20 @@
 """
 🇬🇧 Module: config.py
-Purpose: Central configuration file storing environment variables, constants, and settings
-used across the entire application.
+Purpose: Central configuration for environment variables and constants
+used across the entire TradeLink AI Analyzer project.
 
 🇷🇺 Модуль: config.py
-Назначение: центральный конфигурационный файл, где хранятся переменные окружения,
-константы и настройки, используемые во всём приложении.
+Назначение: централизованная конфигурация переменных окружения и констант
+для всего проекта TradeLink AI Analyzer.
 """
 
 import os
 
-# === LLM Configuration ===
-LLM_PROVIDER = os.getenv("LLM_PROVIDER", "featherless")
-LLM_API_KEY = os.getenv("featherless") or os.getenv("OPENAI_API_KEY")
+# === Hugging Face LLM Configuration ===
+HF_LLM_API_KEY = os.getenv("HF_LLM_integrate")  # token name in Space settings
+HF_LLM_API_URL = "https://api-inference.huggingface.co/models"
 
 # === API Base URLs ===
-FEATHERLESS_BASE_URL = "https://api.featherless.ai/v1"
 TRADELINK_API_URL = "https://api.tradelink.pro"
 
 # === Request/Connection Settings ===

requirements.txt

@@ -1,9 +1,5 @@
 gradio>=4.29.0
-openai>=1.30.1
-requests
-httpx
-pandas
-matplotlib
-fastapi
-uvicorn
-httpx
+requests>=2.31.0
+httpx>=0.27.0
+pandas>=2.2.0
+matplotlib>=3.8.0

services/llm_client.py

@@ -1,48 +1,56 @@
 """
 🇬🇧 Module: llm_client.py
-Purpose: Provides an adapter for different LLM providers (Featherless, OpenAI, etc.)
-to ensure unified interface for chat completions.
+Purpose: Adapter for Hugging Face Inference API (LLM).
+Provides a unified interface for text generation and future model extensions.
 
 🇷🇺 Модуль: llm_client.py
-Назначение: адаптер для различных LLM-провайдеров (Featherless, OpenAI и др.),
-создающий единый интерфейс для взаимодействия с моделями.
+Назначение: адаптер для LLM через Hugging Face Inference API.
+Обеспечивает единый интерфейс для генерации текста и расширения под другие модели в будущем.
 """
 
-from typing import Iterable, List, Dict
-from openai import OpenAI
-from config import LLM_API_KEY, LLM_PROVIDER, FEATHERLESS_BASE_URL
+import requests
+from typing import List, Dict, Generator
+from config import HF_LLM_API_KEY, HF_LLM_API_URL, DEBUG
 
 
-class LLMAdapter:
-    """Unified adapter for LLM providers."""
+class HuggingFaceLLMClient:
+    """Lightweight client for Hugging Face text generation models."""
 
-    def __init__(self, provider: str = LLM_PROVIDER, api_key: str | None = LLM_API_KEY):
+    def __init__(self, api_key: str):
         if not api_key:
-            raise ValueError("Missing LLM API key (env var 'featherless' or 'OPENAI_API_KEY').")
-        self.provider = provider
-        self.client = self._init_client(provider, api_key)
-
-    def _init_client(self, provider: str, api_key: str) -> OpenAI:
-        """Initialize OpenAI client depending on provider."""
-        if provider == "featherless":
-            return OpenAI(base_url=FEATHERLESS_BASE_URL, api_key=api_key)
-        if provider == "openai":
-            return OpenAI(api_key=api_key)
-        raise ValueError(f"Unsupported provider: {provider}")
-
-    def stream_chat(self, *, messages: List[Dict], model: str) -> Iterable[str]:
-        """Stream responses token by token."""
-        response = self.client.chat.completions.create(model=model, messages=messages, stream=True)
-        for chunk in response:
-            delta = chunk.choices[0].delta.content
-            if delta:
-                yield delta
-
-    def complete(self, *, messages: List[Dict], model: str) -> str:
-        """Return a single completion (non-streaming)."""
-        resp = self.client.chat.completions.create(model=model, messages=messages)
-        return resp.choices[0].message.content
-
-
-# Global ready-to-use instance
-llm_service = LLMAdapter()
+            raise ValueError("Missing Hugging Face API key (HF_LLM_integrate).")
+        self.api_key = api_key
+        self.headers = {"Authorization": f"Bearer {self.api_key}"}
+
+    def complete(self, model: str, prompt: str) -> str:
+        """Non-streaming completion request."""
+        url = f"{HF_LLM_API_URL}/{model}"
+        payload = {"inputs": prompt}
+
+        if DEBUG:
+            print(f"[DEBUG] Sending request to {url}")
+
+        resp = requests.post(url, headers=self.headers, json=payload)
+        resp.raise_for_status()
+        result = resp.json()
+
+        # Handle HF response structures
+        if isinstance(result, list) and "generated_text" in result[0]:
+            return result[0]["generated_text"]
+        if isinstance(result, dict) and "generated_text" in result:
+            return result["generated_text"]
+
+        return str(result)
+
+    def stream_chat(self, *, messages: List[Dict], model: str) -> Generator[str, None, None]:
+        """
+        Placeholder for streaming logic (not all Hugging Face endpoints support streaming).
+        For now, concatenates messages and sends as one prompt.
+        """
+        prompt = "\n".join(m["content"] for m in messages)
+        text = self.complete(model, prompt)
+        yield text
+
+
+# Global instance for reuse
+llm_service = HuggingFaceLLMClient(api_key=HF_LLM_API_KEY)