Update main entry point for nl2sql application > app/main.py

1. Run from the main entry point, main.py
2. Remove local test block for db_manager, hf_engine, and sql_agent

app/main.py

@@ -0,0 +1,60 @@
+# Path: app/main.py
+# Main entry point for the NL2SQL application
+import os
+from dotenv import load_dotenv
+from src.nl2sql.sql_agent import nl2sql_agent
+from src.scripts.evaluate_hf import run_evaluation
+
+load_dotenv()
+
+# User prompt question manually and see the agent's response
+def interactive_mode():
+    """Allows user to manually type questions and get agent's response."""
+    print("\n========= Interactive NL2SQL Mode =========")
+    print("Type 'exit' or 'q' to return to the main menu.\n")
+
+    while True:
+        question = input("\nEnter your question: ")
+        if question.lower() in ['exit', 'q']:
+            break
+        if not question.strip():
+            continue
+
+        print("\nProcessing your question...")
+        response = nl2sql_agent(question)
+
+        print("\n========= Agent Response =========")
+        print(f"Status: {response.get('status')}")
+        print(f"Generated SQL:\n{response.get('query')}")
+
+        if response.get('status') == 'success':
+            print(f"\nresults (First 5 rows):\n{response.get('results')[:5]}")
+        else:
+            print(f"\nError Details:\n{response.get('error')}")
+        print("==================================\n")
+
+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':
+            interactive_mode()
+        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()

src/database/db_manager.py

@@ -3,14 +3,11 @@
 # Include a RAG-based dynamic schema retrieval.
 import os
 import sqlite3
-from dotenv import load_dotenv
 from langchain_community.utilities import SQLDatabase
 from langchain_chroma import Chroma
 from langchain_huggingface import HuggingFaceEmbeddings
 from langchain_core.documents import Document
 
-load_dotenv()
-
 # The path to SQLite database
 DB_PATH = os.getenv("SQLITE_DB_PATH", "src/database/Chinook_Sqlite.sqlite")
 DB_URI = f"sqlite:///{DB_PATH}"
@@ -78,10 +75,4 @@ def get_schema_context(question=None):
         db = SQLDatabase.from_uri(DB_URI, include_tables = tables_to_include, sample_rows_in_table_info=5)
         return db.get_table_info()
     except Exception as e:
-        return f"Error retrieving database schema: {e}"
-    
-if __name__ == "__main__":
-    # Example usage: Get schema context for a specific question
-    question = "What tables contain information about music tracks and their genres?"
-    schema_context = get_schema_context(question)
-    print(schema_context)
+        return f"Error retrieving database schema: {e}"

src/nl2sql/hf_engine.py

@@ -47,17 +47,4 @@ def get_llm(model_id: str = DEFAULT_MODEL_ID):
     client = InferenceClient(api_key=hf_token)
     llm = HFChatWrapper(client=client, model_id=model_id)
 
-    return llm
-
-# Local Test block
-if __name__ == "__main__":
-    from dotenv import load_dotenv
-    load_dotenv()
-
-    try:
-        test_llm = get_llm()
-        print("Model loaded successfully! Running a quick ping...")
-        response = test_llm.invoke("Write a single SQL statement to count all rows in a table named 'Employee'.")
-        print(f"\nResponse:\n{response}")
-    except Exception as e:
-        print(f"Error during LLM initialization or invocation: {e}")
+    return llm

src/nl2sql/sql_agent.py

@@ -90,18 +90,4 @@ def nl2sql_agent(user_question: str) -> dict:
             "status": f"error executing SQL: {e}"
         }
     finally:
-        connection.close()
-
-# Local Test block
-if __name__ == "__main__":
-    from dotenv import load_dotenv
-    load_dotenv()
-
-    test_question = "How many employees are there in the Employee table?"
-
-    print("------Starting NL2SQL Agent Test------")
-    result = nl2sql_agent(test_question)
-
-    print("------Final Output------")
-    for key, value in result.items():
-        print(f"{key.capitalize()}: {value}")
+        connection.close()