Finally running backend

backend/app/config.py

@@ -0,0 +1,74 @@
+"""
+Configuration management for FastAPI backend
+Loads ALL settings from .env.local at project ROOT
+"""
+from pydantic_settings import BaseSettings
+from pathlib import Path
+from typing import List
+import logging
+
+
+def find_env_file() -> Path:
+    """Find .env.local by checking multiple paths"""
+    possible_paths = [
+        Path("/Users/arif/Projects/Personal/LLM-Data-Analyzer/.env.local"),
+        Path.cwd() / ".env.local",
+        Path(__file__).parent.parent.parent / ".env.local",
+        Path(__file__).parent.parent / ".env.local",
+    ]
+    
+    for path in possible_paths:
+        if path.exists():
+            return path
+    
+    raise FileNotFoundError(
+        f"❌ .env.local not found! Checked:\n" + 
+        "\n".join([f"  - {p}" for p in possible_paths])
+    )
+
+
+class Settings(BaseSettings):
+    """Application settings - ALL loaded from .env.local"""
+    
+    # API Configuration
+    fastapi_env: str
+    api_host: str
+    api_port: int
+    log_level: str
+    
+    # LLM Configuration
+    llm_model_name: str
+    llm_max_tokens: int
+    llm_temperature: float
+    llm_device: str
+    
+    # File Upload
+    max_file_size: int
+    upload_timeout: int
+    
+    # CORS
+    cors_origins: List[str]
+    
+    class Config:
+        env_file = str(find_env_file())
+        case_sensitive = False
+        extra = "ignore"
+
+
+settings = Settings()
+
+
+def get_logger(name: str) -> logging.Logger:
+    """Get configured logger instance"""
+    logger = logging.getLogger(name)
+    
+    if not logger.handlers:
+        handler = logging.StreamHandler()
+        formatter = logging.Formatter(
+            '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+        )
+        handler.setFormatter(formatter)
+        logger.addHandler(handler)
+    
+    logger.setLevel(settings.log_level)
+    return logger

backend/app/main.py

@@ -0,0 +1,73 @@
+"""
+Main FastAPI application entry point
+Uses relative imports to work correctly from any directory
+"""
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from contextlib import asynccontextmanager
+
+from .config import settings, get_logger
+
+logger = get_logger(__name__)
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """Manage app lifecycle"""
+    # Startup
+    logger.info("🚀 FastAPI application starting...")
+    logger.info(f"Environment: {settings.fastapi_env}")
+    yield
+    # Shutdown
+    logger.info("🛑 FastAPI application shutting down...")
+
+
+# Create FastAPI app
+app = FastAPI(
+    title="LLM Data Analyzer API",
+    description="Backend API for LLM-based data analysis and ML suggestions",
+    version="0.1.0",
+    lifespan=lifespan
+)
+
+# Add CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=settings.cors_origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+# Health check endpoint
+@app.get("/health")
+async def health_check():
+    """Health check endpoint"""
+    return {
+        "status": "healthy",
+        "environment": settings.fastapi_env,
+        "service": "llm-data-analyzer-backend"
+    }
+
+
+# Root endpoint
+@app.get("/")
+async def root():
+    """Root endpoint with API information"""
+    return {
+        "service": "LLM Data Analyzer API",
+        "version": "0.1.0",
+        "docs_url": "/docs",
+        "health_url": "/health"
+    }
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(
+        "backend.app.main:app",
+        host=settings.api_host,
+        port=settings.api_port,
+        reload=settings.fastapi_env == "development"
+    )

backend/app/services/analyzer.py

@@ -0,0 +1,14 @@
+"""Analysis Service - statistical and exploratory analysis"""
+from app.config import get_logger
+
+logger = get_logger(__name__)
+
+class Analyzer:
+    """Performs statistical analysis on data"""
+    
+    def __init__(self):
+        logger.info("Analyzer initialized")
+    
+    async def analyze(self, data):
+        """Analyze data"""
+        return {"status": "Analysis coming in Phase 4"}

backend/app/services/dashboard_generator.py

@@ -0,0 +1,14 @@
+"""Dashboard Generation Service - creates interactive dashboards"""
+from app.config import get_logger
+
+logger = get_logger(__name__)
+
+class DashboardGenerator:
+    """Generates auto-configured Plotly dashboards"""
+    
+    def __init__(self):
+        logger.info("DashboardGenerator initialized")
+    
+    async def generate(self, data):
+        """Generate dashboard from data"""
+        return {"dashboard": "Coming in Phase 4"}

backend/app/services/data_processor.py

@@ -0,0 +1,14 @@
+"""Data Processing Service - handles file uploads and data parsing"""
+from app.config import get_logger
+
+logger = get_logger(__name__)
+
+class DataProcessor:
+    """Handles data file processing (CSV, XLS, XLSX)"""
+    
+    def __init__(self):
+        logger.info("DataProcessor initialized")
+    
+    async def process_file(self, file_path: str):
+        """Process uploaded file"""
+        return {"status": "File processing coming in Phase 3"}

backend/app/services/llm_service.py

@@ -0,0 +1,17 @@
+"""LLM Service - handles MLX Llama 2 inference"""
+from app.config import get_logger
+
+logger = get_logger(__name__)
+
+class LLMService:
+    """Wrapper around MLX LLM for convenient inference"""
+    
+    def __init__(self):
+        """Initialize LLM Service - actual LLM loading in Phase 2"""
+        self.llm = None
+        logger.info("LLMService initialized (Phase 2 will load actual model)")
+    
+    async def chat(self, message: str, history: list = None) -> str:
+        """Process user message and return LLM response"""
+        logger.info(f"Chat request: {message}")
+        return "LLM response will be here in Phase 2"

backend/app/services/ml_suggester.py

@@ -0,0 +1,14 @@
+"""ML Suggestion Service - suggests models and business problems"""
+from app.config import get_logger
+
+logger = get_logger(__name__)
+
+class MLSuggester:
+    """Suggests appropriate ML models and identifies business problems"""
+    
+    def __init__(self):
+        logger.info("MLSuggester initialized")
+    
+    async def suggest_models(self, data_summary: dict):
+        """Suggest ML models based on data characteristics"""
+        return {"suggestions": []}

main.py

@@ -1,6 +0,0 @@
-def main():
-    print("Hello from llm-data-analyzer!")
-
-
-if __name__ == "__main__":
-    main()