feat: enhance application functionality and telemetry monitoring

- Added new scripts in `package.json` for Python dependency installation and sidecar compilation.
- Integrated telemetry fetching in `src/App.tsx` to monitor CPU, RAM, GPU, and disk usage.
- Implemented model downloading functionality with progress tracking in `src/App.tsx`.
- Updated `Footer` and `Header` components to display telemetry data and settings options.
- Enhanced Tauri configuration for sidecar execution and application window settings.
- Updated Python requirements in `requirements.txt` to include additional dependencies for improved functionality.

src-python/app/api_server.py

@@ -0,0 +1,139 @@
+"""
+Servidor FastAPI para expor telemetria e download de modelos.
+Utiliza Server-Sent Events (SSE) para streaming de progresso em tempo real.
+"""
+import sys
+import os
+import json
+from pathlib import Path
+from typing import Dict, List
+from fastapi import FastAPI, HTTPException
+from fastapi.responses import StreamingResponse, JSONResponse
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+import asyncio
+
+# Detecta se está rodando como binário ou script
+if getattr(sys, 'frozen', False):
+    # Caminho do executável (PyInstaller)
+    BASE_DIR = os.path.dirname(sys.executable)
+    CONFIG_PATH = os.path.join(BASE_DIR, "modelConfig.yaml")
+    MODELS_DIR = os.path.join(BASE_DIR, "models")
+else:
+    # Rodando como script Python
+    # Ajusta PYTHONPATH antes dos imports relativos
+    script_dir = os.path.dirname(os.path.abspath(__file__))
+    project_root = os.path.dirname(os.path.dirname(script_dir))  # Sobe para raiz do projeto
+    if project_root not in sys.path:
+        sys.path.insert(0, project_root)
+    
+    BASE_DIR = project_root
+    CONFIG_PATH = os.path.join(BASE_DIR, "modelConfig.yaml")
+    MODELS_DIR = os.path.join(BASE_DIR, "src-python", "app", "models")
+
+# Importações relativas (funcionam quando executado como módulo)
+from .telemetry import SystemTelemetry
+from .model_downloader import ModelDownloader
+
+app = FastAPI(title="LocalAI API", version="1.0.0")
+
+# CORS para permitir comunicação com frontend Tauri
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Em produção, restringir ao localhost
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Instâncias globais
+downloader = ModelDownloader(models_dir=MODELS_DIR)
+
+# Modelos de dados
+class ModelInfo(BaseModel):
+    id: str
+    name: str
+    quant: str
+    size_gb: float
+    repo: str
+    filename: str
+    description: str
+
+class DownloadRequest(BaseModel):
+    model_id: str
+    repo_id: str
+    filename: str
+
+# Cache de modelos disponíveis (pode ser carregado de JSON)
+MODELS_REGISTRY: List[Dict] = [
+    {
+        "id": "lfm-2.5-1.2b",
+        "name": "Liquid LFM 2.5",
+        "quant": "Q6_K",
+        "size_gb": 1.12,
+        "repo": "LiquidAI/LFM2.5-1.2B-Instruct-GGUF",
+        "filename": "LFM2.5-1.2B-Instruct-Q6_K.gguf",
+        "description": "Otimizado para arquitetura sequencial de alta performance."
+    }
+]
+
+@app.get("/api/telemetry")
+async def get_telemetry():
+    """Retorna telemetria do sistema em tempo real."""
+    return SystemTelemetry.get_stats()
+
+@app.get("/api/models")
+async def list_models():
+    """Lista todos os modelos disponíveis com status de download."""
+    models_with_status = []
+    for model in MODELS_REGISTRY:
+        model_path = downloader.get_model_path(model["repo"], model["filename"])
+        model_copy = model.copy()
+        model_copy["downloaded"] = model_path.exists() if model_path else False
+        models_with_status.append(model_copy)
+    return {"models": models_with_status}
+
+@app.post("/api/models/download")
+async def download_model(request: DownloadRequest):
+    """
+    Inicia download de modelo com progresso via SSE.
+    O frontend deve consumir como EventSource.
+    """
+    async def generate_progress():
+        try:
+            async for progress_event in downloader.download_with_progress(
+                repo_id=request.repo_id,
+                filename=request.filename
+            ):
+                yield f"data: {json.dumps(progress_event)}\n\n"
+        except Exception as e:
+            error_event = {"type": "error", "message": str(e)}
+            yield f"data: {json.dumps(error_event)}\n\n"
+    
+    return StreamingResponse(
+        generate_progress(),
+        media_type="text/event-stream",
+        headers={
+            "Cache-Control": "no-cache",
+            "Connection": "keep-alive",
+            "X-Accel-Buffering": "no"
+        }
+    )
+
+@app.get("/api/models/{model_id}/status")
+async def get_model_status(model_id: str):
+    """Verifica se um modelo específico está baixado."""
+    model = next((m for m in MODELS_REGISTRY if m["id"] == model_id), None)
+    if not model:
+        raise HTTPException(status_code=404, detail="Modelo não encontrado")
+    
+    model_path = downloader.get_model_path(model["repo"], model["filename"])
+    return {
+        "model_id": model_id,
+        "downloaded": model_path.exists() if model_path else False,
+        "path": str(model_path) if model_path else None
+    }
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="127.0.0.1", port=8000)