run_demo.py

import os
import sys
from src.benchmark_db import setup_benchmark_databases, PAGILA_DB_PATH
from src.dataset_generator import build_dataset, DATASET_PATH
from src.train import train_model, MODEL_PATH
from app import app, load_ai_assets

def main():
    print("=========================================================================")
    print(" DeepQuery: AI-Based SQL Query Recommendation and Performance Ranking ")
    print("=========================================================================\n")

    # Step 1: Initialize Benchmark Databases
    if not os.path.exists(PAGILA_DB_PATH):
        print("[Step 1/3] Building Pagila & TPC-H Benchmark Databases...")
        setup_benchmark_databases()
    else:
        print("[Step 1/3] Pagila & TPC-H Benchmark Databases found.")

    # Step 2: Build Dataset
    if not os.path.exists(DATASET_PATH):
        print("[Step 2/3] Profiling Queries & Building Dataset...")
        build_dataset()
    else:
        print("[Step 2/3] Benchmark Dataset found.")

    # Step 3: Train ANN Model
    if not os.path.exists(MODEL_PATH):
        print("[Step 3/3] Training 4-Layer Artificial Neural Network (ANN)...")
        train_model()
    else:
        print("[Step 3/3] Trained Deep Learning Model found.")

    print("\n-------------------------------------------------------------------------")
    print(" DeepQuery AI System Ready! Launching Web Dashboard on http://127.0.0.1:5000")
    print("-------------------------------------------------------------------------\n")

    load_ai_assets()
    app.run(host='127.0.0.1', port=5000, debug=False)

if __name__ == '__main__':
    main()