Update app.py

app.py

@@ -3,7 +3,6 @@ from pydantic import BaseModel
 import json
 import gradio as gr
 import pandas as pd
-import io
 
 class ValidationStatus(BaseModel):
     is_valid: bool
@@ -48,45 +47,14 @@ def parse_execution_results_to_dataframe(execution_results):
         print(f"Error parsing results: {e}")
         return None
 
-def create_excel_file(df):
-    """Create an Excel file from DataFrame and return the file path"""
-    if df is None or df.empty:
-        return None
-    
-    try:
-        # Create a BytesIO buffer
-        output = io.BytesIO()
-        
-        # Write DataFrame to Excel
-        with pd.ExcelWriter(output, engine='openpyxl') as writer:
-            df.to_excel(writer, index=False, sheet_name='Query Results')
-            
-            # Get the worksheet
-            worksheet = writer.sheets['Query Results']
-            
-            # Auto-adjust column widths
-            for idx, col in enumerate(df.columns):
-                max_length = max(
-                    df[col].astype(str).apply(len).max(),
-                    len(str(col))
-                )
-                worksheet.column_dimensions[chr(65 + idx)].width = min(max_length + 2, 50)
-        
-        output.seek(0)
-        return output.getvalue()
-        
-    except Exception as e:
-        print(f"Error creating Excel file: {e}")
-        return None
-
 def generate_sql_query(api_key, user_query):
     """Generate SQL query from natural language using GROQ API"""
     try:
         if not api_key:
-            return "Error: Please enter your GROQ API key", "", "", "", None, None, ""
+            return "Error: Please enter your GROQ API key", "", "", "", None, ""
         
         if not user_query:
-            return "Error: Please enter a query description", "", "", "", None, None, ""
+            return "Error: Please enter a query description", "", "", "", None, ""
         
         client = Groq(api_key=api_key)
         
@@ -102,18 +70,15 @@ After generating the SQL query, you must:
 3. Execute the generated SQL query against the sample table
 4. Display the SQL table structure and data clearly
 5. Show the query execution results in a pipe-delimited table format
-
 IMPORTANT: The execution_results field must contain a properly formatted table with:
 - Header row with column names separated by pipes (|)
 - A separator row with dashes
 - Data rows with values separated by pipes (|)
-
 Example format:
 column1 | column2 | column3
 --------|---------|--------
 value1  | value2  | value3
 value4  | value5  | value6
-
 Always present your response in this order:
 - Generated SQL query with syntax explanation
 - Table schema (CREATE TABLE statement)
@@ -152,35 +117,29 @@ Always present your response in this order:
         metadata = f"""Query Type: {sql_query_generation.query_type}
 Tables Used: {', '.join(sql_query_generation.tables_used)}
 Complexity: {sql_query_generation.estimated_complexity}
-
 Execution Notes:
 {chr(10).join(f"- {note}" for note in sql_query_generation.execution_notes)}
-
 Optimization Notes:
 {chr(10).join(f"- {note}" for note in sql_query_generation.optimization_notes)}"""
         
         # Convert execution results to DataFrame
         results_df = parse_execution_results_to_dataframe(sql_query_generation.execution_results)
         
-        # Create Excel file
-        excel_file = create_excel_file(results_df) if results_df is not None else None
-        
         return (
             sql_query_generation.query,
             metadata,
             sql_query_generation.table_schema,
             sql_query_generation.sample_data,
             results_df,
-            excel_file,
             validation_text
         )
         
     except Exception as e:
         error_msg = f"Error: {str(e)}"
-        return error_msg, "", "", "", None, None, ""
+        return error_msg, "", "", "", None, ""
 
 # Create Gradio interface
-with gr.Blocks(title="SQL Query Generator", theme=gr.themes.Soft()) as demo:
+with gr.Blocks(title="SQL Query Generator", theme=gr.themes.Ocean()) as demo:
     gr.Markdown(
         """
         # 🗄️ Natural Language to SQL Query Generator
@@ -252,24 +211,15 @@ with gr.Blocks(title="SQL Query Generator", theme=gr.themes.Soft()) as demo:
             )
     
     with gr.Row():
-        with gr.Column():
-            execution_output = gr.Dataframe(
-                label="📊 Execution Results",
-                headers=None,
-                datatype="str",
-                row_count=10,
-                col_count=None,
-                wrap=True,
-                interactive=False
-            )
-    
-    with gr.Row():
-        with gr.Column():
-            download_btn = gr.File(
-                label="📥 Download Excel File",
-                type="binary",
-                interactive=False
-            )
+        execution_output = gr.Dataframe(
+            label="📊 Execution Results",
+            headers=None,
+            datatype="str",
+            row_count=10,
+            col_count=None,
+            wrap=True,
+            interactive=False
+        )
     
     generate_btn.click(
         fn=generate_sql_query,
@@ -280,7 +230,6 @@ with gr.Blocks(title="SQL Query Generator", theme=gr.themes.Soft()) as demo:
             schema_output,
             sample_data_output,
             execution_output,
-            download_btn,
             validation_output
         ]
     )
@@ -292,16 +241,14 @@ with gr.Blocks(title="SQL Query Generator", theme=gr.themes.Soft()) as demo:
         1. Enter your GROQ API key (get one from [console.groq.com](https://console.groq.com))
         2. Type your natural language query description
         3. Click "Generate SQL Query" to see the results
-        4. Download the execution results as an Excel file
         
         The app will provide:
         - A validated SQL query
         - Table schema and sample data
         - Execution results in Excel-style table format
-        - Downloadable Excel file with results
         - Optimization suggestions
         """
     )
 
 if __name__ == "__main__":
-    demo.launch(share=False, server_name="0.0.0.0", server_port=7860)
+    demo.launch(share=True)