Merge branch 'main' into NL2SQL-V1.0

.env

@@ -1 +0,0 @@
-HF_TOKEN='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'

.vscode/settings.json

@@ -0,0 +1,3 @@
+{
+    "postman.settings.dotenv-detection-notification-visibility": false
+}

backend/app/main.py

@@ -0,0 +1,38 @@
+# Path: app/main.py
+# Main entry point for the NL2SQL application
+import os
+from dotenv import load_dotenv
+from backend.src.scripts.interactive_mode import run_interactiveMode
+from backend.src.scripts.evaluation_mode import run_evaluation
+
+load_dotenv()
+# Load HuggingFace API token from environment variable
+hf_token = os.getenv("HF_TOKEN")
+if not hf_token:
+    raise ValueError("HuggingFace API token not found!")
+
+def main():
+    """Main application entry point and interactive menu"""
+    while True:
+        print("\n" + "="*30)
+        print(" NL2SQL Application Main Menu")
+        print("\n" + "="*30)
+        print("1. Run Interactive Agent NL2SQL Mode (Ask a single question)")
+        print("2. Run Batch Evaluation of NL2SQL Agent (Evaluate on 15 test cases)")
+        print("3. Exit")
+        print("\n" + "="*30)
+        
+        choice = input("Select an option (1-3): ")
+
+        if choice == '1':
+            run_interactiveMode()
+        elif choice == '2':
+            run_evaluation()
+        elif choice == '3':
+            print("Exiting application. Goodbye!")
+            break
+        else:
+            print("Invalid choice. Please select a valid option (1, 2, or 3).")
+
+if __name__ == "__main__":
+    main()

hf_test_bench.py → backend/hf_test_bench.py

@@ -1,6 +1,6 @@
 # Test the Hugging Face inference
-from src.nl2sql.hf_engine import generate_sql
-from src.database.db_manager import get_db_connection, get_schema_context
+from backend.src.nl2sql.hf_engine import generate_sql
+from backend.src.database.db_manager import get_db_connection, get_schema_context
 import pandas as pd
 
 def test_single_query():

{src → backend/src}/nl2sql/sql_agent.py

@@ -1,8 +1,8 @@
 # Path: src/nl2sql/sql_agent.py
 # SQL Agent for handling NL2SQL conversion with Auto-Correct functionality
-from src.database.db_manager import get_db_connection, get_schema_context
+from backend.src.database.db_manager import get_db_connection, get_schema_context
 from langchain_core.prompts import PromptTemplate
-from src.nl2sql.hf_engine import get_llm
+from backend.src.nl2sql.hf_engine import get_llm
 
 # Craft the Prompt Template to instruct LLM on its persona
 SQL_PROMPT_TEMPLATE = """You are an expert SQLite developer.