aesthetics

app.py

@@ -5,7 +5,6 @@ from smolagents import tool, CodeAgent, HfApiModel
 import spaces
 import pandas as pd
 
-# Import the persistent database
 from database import engine, receipts
 
 import pandas as pd
@@ -69,7 +68,7 @@ def query_sql(user_query: str) -> str:
     Returns:
         The query result from the database as a formatted string.
     """
-    # Provide the AI with the correct schema and strict instructions
+
     schema_info = (
         "The database has a table named 'receipts' with the following schema:\n"
         "- receipt_id (INTEGER, primary key)\n"
@@ -80,14 +79,11 @@ def query_sql(user_query: str) -> str:
         "DO NOT explain your reasoning, and DO NOT return anything other than the SQL query itself."
     )
 
-    # Generate SQL query using the provided schema
     generated_sql = agent.run(f"{schema_info} Convert this request into SQL: {user_query}")
 
-    # Ensure generated_sql is always a string
     if not isinstance(generated_sql, str):
         return f"Unexpected result: {generated_sql}"  # Handle unexpected numerical result
 
-    # Log the generated SQL for debugging
     print(f"Generated SQL: {generated_sql}")
 
     if not generated_sql.strip().lower().startswith(("select", "show", "pragma")):
@@ -95,7 +91,6 @@ def query_sql(user_query: str) -> str:
 
     result = sql_engine(generated_sql)
 
-    #print(f"SQL Query Result: {result}")
     print(f"{result}")
     
     try:
@@ -114,11 +109,10 @@ def handle_query(user_input: str) -> str:
     Returns:
         The SQL query result as a plain string to be displayed in the UI.
     """
-    return query_sql(user_input)  # Directly return the processed result
+    return query_sql(user_input)
 
-# Initialize CodeAgent to generate SQL queries from natural language
 agent = CodeAgent(
-    tools=[sql_engine],  # Ensure sql_engine is properly registered
+    tools=[sql_engine],
     model=HfApiModel(model_id="Qwen/Qwen2.5-Coder-32B-Instruct"),
 )
 
@@ -126,18 +120,16 @@ with gr.Blocks() as demo:
     gr.Markdown("## Natural Language to SQL Executor with Live Data")
 
     with gr.Row():
-        with gr.Column(scale=1):  # Left: Query Interface
+        with gr.Column(scale=1):
             user_input = gr.Textbox(label="Enter your query in plain English")
             query_output = gr.Textbox(label="Query Result")
         
-        with gr.Column(scale=2):  # Right: Live Database Table
+        with gr.Column(scale=2):
             gr.Markdown("### Receipts Table (Live View)")
             receipts_table = gr.Dataframe(value=get_receipts_table(), label="Receipts Table")
 
-    # Query handling function
     user_input.change(fn=handle_query, inputs=user_input, outputs=query_output)
 
-    # Auto-refresh table every 5 seconds
     demo.load(fn=get_receipts_table, outputs=receipts_table)
 
 if __name__ == "__main__":