Create app.py

app.py

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+from fastapi import FastAPI, HTTPException
+from fastapi.responses import StreamingResponse
+import pandas as pd
+import psycopg2
+from psycopg2.extras import RealDictCursor
+import os
+from io import BytesIO
+
+app = FastAPI()
+
+# Database connection parameters
+DB_CONFIG = {
+    "host": "13.126.242.31",
+    "database": "aml",
+    "user": "dev_cbs_admin",
+    "password": "Finovate@2023"
+}
+
+def get_db_connection():
+    """Create database connection"""
+    return psycopg2.connect(**DB_CONFIG, cursor_factory=RealDictCursor)
+
+def read_columns_from_file(template_id: str):
+    """Read column names from output file"""
+    filename = f'output_{template_id}.txt'
+    try:
+        with open(filename, 'r') as f:
+            content = f.read()
+            # Safely evaluate the string representation of the list
+            columns = eval(content)
+        return columns
+    except Exception as e:
+        raise HTTPException(status_code=404, detail=f"Error reading columns from file: {str(e)}")
+
+@app.get("/download/{template_id}")
+async def download_excel(template_id: str):
+    """
+    Download data as Excel file based on template ID
+    """
+    # Read columns from selected file
+    columns = read_columns_from_file(template_id)
+    
+    if not columns:
+        raise HTTPException(status_code=404, detail="No columns found in output file")
+    
+    try:
+        # Connect to database
+        conn = get_db_connection()
+        cur = conn.cursor()
+        
+        # Get existing columns from the table
+        cur.execute("""
+            SELECT column_name 
+            FROM information_schema.columns 
+            WHERE table_name = 'large_transactions'
+        """)
+        existing_columns = {row['column_name'].lower(): row['column_name'] for row in cur.fetchall()}
+        
+        # Filter out non-existent columns and use actual column names from DB
+        valid_columns = []
+        for col in columns:
+            if col.lower() in existing_columns:
+                valid_columns.append(existing_columns[col.lower()])
+        
+        if not valid_columns:
+            raise HTTPException(status_code=404, detail="None of the specified columns exist in the table")
+            
+        # Create SQL query with proper column quoting
+        quoted_columns = [f'"{col}"' for col in valid_columns]
+        query = f"""
+            SELECT {', '.join(quoted_columns)}
+            FROM large_transactions
+        """
+        
+        # Execute query
+        cur.execute(query)
+        rows = cur.fetchall()
+        
+        # Convert to DataFrame
+        df = pd.DataFrame(rows)
+        
+        # Create Excel file in memory
+        output = BytesIO()
+        with pd.ExcelWriter(output, engine='xlsxwriter') as writer:
+            df.to_excel(writer, index=False, sheet_name='Data')
+        output.seek(0)
+        
+        # Close database connection
+        cur.close()
+        conn.close()
+        
+        # Return the Excel file as a downloadable response
+        return StreamingResponse(
+            output,
+            media_type="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
+            headers={
+                "Content-Disposition": f"attachment; filename=large_transactions_{template_id}.xlsx"
+            }
+        )
+        
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error fetching data: {str(e)}")
+
+@app.get("/templates")
+async def list_templates():
+    """List all available template IDs"""
+    try:
+        output_files = [f.replace('output_', '').replace('.txt', '') 
+                       for f in os.listdir('.') 
+                       if f.startswith('output_') and f.endswith('.txt')]
+        return {"templates": output_files}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error listing templates: {str(e)}")