Update app.py

app.py

@@ -2,6 +2,7 @@ from groq import Groq
 from pydantic import BaseModel
 import json
 import gradio as gr
+import pandas as pd
 
 class ValidationStatus(BaseModel):
     is_valid: bool
@@ -19,14 +20,41 @@ class SQLQueryGeneration(BaseModel):
     execution_results: str
     optimization_notes: list[str]
 
+def parse_execution_results_to_dataframe(execution_results):
+    """Convert text-based table results to pandas DataFrame"""
+    try:
+        lines = execution_results.strip().split('\n')
+        if len(lines) < 3:
+            return None
+        
+        # Extract header
+        header_line = lines[0]
+        headers = [col.strip() for col in header_line.split('|')]
+        
+        # Extract data rows (skip separator line)
+        data_rows = []
+        for line in lines[2:]:
+            if line.strip() and not line.strip().startswith('-'):
+                row = [cell.strip() for cell in line.split('|')]
+                if len(row) == len(headers):
+                    data_rows.append(row)
+        
+        if data_rows:
+            df = pd.DataFrame(data_rows, columns=headers)
+            return df
+        return None
+    except Exception as e:
+        print(f"Error parsing results: {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", "", "", "", "", ""
+            return "Error: Please enter your GROQ API key", "", "", "", None, ""
         
         if not user_query:
-            return "Error: Please enter a query description", "", "", "", "", ""
+            return "Error: Please enter a query description", "", "", "", None, ""
         
         client = Groq(api_key=api_key)
         
@@ -41,12 +69,24 @@ After generating the SQL query, you must:
 2. Populate the table with realistic sample data that demonstrates the query's functionality
 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
+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)
 - Sample data (INSERT statements or table visualization)
-- Query execution results
+- Query execution results (in pipe-delimited table format)
 - Any relevant notes about assumptions made or query optimization suggestions""",
                 },
                 {
@@ -87,18 +127,21 @@ 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)
+        
         return (
             sql_query_generation.query,
             metadata,
             sql_query_generation.table_schema,
             sql_query_generation.sample_data,
-            sql_query_generation.execution_results,
+            results_df,
             validation_text
         )
         
     except Exception as e:
         error_msg = f"Error: {str(e)}"
-        return error_msg, "", "", "", "", ""
+        return error_msg, "", "", "", None, ""
 
 # Create Gradio interface
 with gr.Blocks(title="SQL Query Generator", theme=gr.themes.Soft()) as demo:
@@ -173,9 +216,14 @@ with gr.Blocks(title="SQL Query Generator", theme=gr.themes.Soft()) as demo:
             )
     
     with gr.Row():
-        execution_output = gr.Textbox(
-            label="Execution Results",
-            lines=10
+        execution_output = gr.Dataframe(
+            label="📊 Execution Results",
+            headers=None,
+            datatype="str",
+            row_count=10,
+            col_count=None,
+            wrap=True,
+            interactive=False
         )
     
     generate_btn.click(
@@ -202,7 +250,7 @@ with gr.Blocks(title="SQL Query Generator", theme=gr.themes.Soft()) as demo:
         The app will provide:
         - A validated SQL query
         - Table schema and sample data
-        - Execution results
+        - Execution results in Excel-style table format
         - Optimization suggestions
         """
     )