Fix module import by moving FastAPI app to root main.py with proper imports

main.py

@@ -1,5 +1,128 @@
-# Import the FastAPI app from our app module
-from app.main import app
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+import os
+import logging
+import sys
+from app.config import DATASET_CONFIGS
+# Lazy imports to avoid blocking startup
+# from .pipeline import RAGPipeline  # Will import when needed
+# import umap  # Will import when needed for visualization
+# import plotly.express as px  # Will import when needed for visualization
+# import plotly.graph_objects as go  # Will import when needed for visualization
+# from plotly.subplots import make_subplots  # Will import when needed for visualization
+# import numpy as np  # Will import when needed for visualization
+# from sklearn.preprocessing import normalize  # Will import when needed for visualization
+# import pandas as pd  # Will import when needed for visualization
+import json
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+    handlers=[
+        logging.StreamHandler(sys.stdout)
+    ]
+)
+logger = logging.getLogger(__name__)
+
+app = FastAPI(title="RAG Pipeline API", description="Multi-dataset RAG API", version="1.0.0")
+
+# Initialize pipelines for all datasets
+pipelines = {}
+google_api_key = os.getenv("GOOGLE_API_KEY")
+
+logger.info(f"Starting RAG Pipeline API")
+logger.info(f"Port from env: {os.getenv('PORT', 'Not set - will use 8000')}")
+logger.info(f"Google API Key present: {'Yes' if google_api_key else 'No'}")
+logger.info(f"Available datasets: {list(DATASET_CONFIGS.keys())}")
+
+# Don't load datasets during startup - do it asynchronously after server starts
+logger.info("RAG Pipeline API is ready to serve requests - datasets will load in background")
+
+# Visualization function disabled to speed up startup
+# def create_3d_visualization(pipeline):
+#     ... (commented out for faster startup)
+
+class Question(BaseModel):
+    text: str
+    dataset: str = "developer-portfolio"  # Default dataset
+
+@app.post("/answer")
+async def get_answer(question: Question):
+    try:
+        # Check if any pipelines are loaded
+        if not pipelines:
+            return {
+                "answer": "RAG Pipeline is running but datasets are still loading in the background. Please try again in a moment, or check /health for loading status.",
+                "dataset": question.dataset,
+                "status": "datasets_loading"
+            }
+        
+        # Select the appropriate pipeline based on dataset
+        if question.dataset not in pipelines:
+            raise HTTPException(status_code=400, detail=f"Dataset '{question.dataset}' not available. Available datasets: {list(pipelines.keys())}")
+        
+        selected_pipeline = pipelines[question.dataset]
+        answer = selected_pipeline.answer_question(question.text)
+        return {"answer": answer, "dataset": question.dataset}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.get("/datasets")
+async def list_datasets():
+    """List all available datasets"""
+    return {"datasets": list(pipelines.keys())}
+
+async def load_datasets_background():
+    """Load datasets in background after server starts"""
+    global pipelines
+    if google_api_key:
+        # Import RAGPipeline only when needed
+        from app.pipeline import RAGPipeline
+        # Only load developer-portfolio to save memory
+        dataset_name = "developer-portfolio"
+        try:
+            logger.info(f"Loading dataset: {dataset_name}")
+            pipeline = RAGPipeline.from_preset(
+                google_api_key=google_api_key,
+                preset_name=dataset_name
+            )
+            pipelines[dataset_name] = pipeline
+            logger.info(f"Successfully loaded {dataset_name}")
+        except Exception as e:
+            logger.error(f"Failed to load {dataset_name}: {e}")
+        logger.info(f"Background loading complete - {len(pipelines)} datasets loaded")
+    else:
+        logger.warning("No Google API key provided - running in demo mode without datasets")
+
+@app.on_event("startup")
+async def startup_event():
+    logger.info("FastAPI application startup complete")
+    logger.info(f"Server should be running on port: {os.getenv('PORT', '8000')}")
+    
+    # Start loading datasets in background (non-blocking)
+    import asyncio
+    asyncio.create_task(load_datasets_background())
+
+@app.on_event("shutdown")
+async def shutdown_event():
+    logger.info("FastAPI application shutting down")
+
+@app.get("/")
+async def root():
+    """Root endpoint"""
+    return {"status": "ok", "message": "RAG Pipeline API", "version": "1.0.0", "datasets": list(pipelines.keys())}
+
+@app.get("/health")
+async def health_check():
+    """Health check endpoint"""
+    logger.info("Health check called")
+    loading_status = "complete" if "developer-portfolio" in pipelines else "loading"
+    return {
+        "status": "healthy", 
+        "datasets_loaded": len(pipelines), 
+        "total_datasets": 1,  # Only loading developer-portfolio
+        "loading_status": loading_status,
+        "port": os.getenv('PORT', '8000')
+    }
 
-# This file is required by Hugging Face Spaces
-# It exposes our FastAPI app at the root level