Update app/export_utils.py

app/export_utils.py

@@ -1,251 +1,249 @@
-"""
-Export Utilities - CSV, Excel, and REAL Power BI export
-"""
-
-import pandas as pd
-import io
-import json
-from datetime import datetime
-
-class ExportUtils:
-    def __init__(self, df):
-        self.df = df
-        
-    def to_csv(self):
-        """Export to CSV"""
-        return self.df.to_csv(index=False).encode('utf-8')
-    
-    def to_excel(self):
-        """Export to Excel with formatting"""
-        output = io.BytesIO()
-        with pd.ExcelWriter(output, engine='openpyxl') as writer:
-            # Write main data
-            self.df.to_excel(writer, sheet_name='Data', index=False)
-            
-            # Add summary sheet
-            numeric_cols = self.df.select_dtypes(include=['number']).columns
-            if len(numeric_cols) > 0:
-                summary = self.df[numeric_cols].describe()
-                summary.to_excel(writer, sheet_name='Summary', index=True)
-            
-            # Add column info sheet
-            col_info = pd.DataFrame({
-                'Column': self.df.columns,
-                'Type': self.df.dtypes.astype(str),
-                'Nulls': self.df.isnull().sum(),
-                'Unique': self.df.nunique()
-            })
-            col_info.to_excel(writer, sheet_name='Column Info', index=False)
-        
-        output.seek(0)
-        return output.getvalue()
-    
-    def to_powerbi_ready(self):
-        """Prepare data for Power BI - Creates CSV optimized for Power BI"""
-        df_powerbi = self.df.copy()
-        
-        # Clean column names (Power BI friendly)
-        df_powerbi.columns = [col.replace(' ', '_').replace('-', '_').replace('/', '_') for col in df_powerbi.columns]
-        
-        # Clean datetime columns for Power BI
-        for col in df_powerbi.columns:
-            if 'datetime' in col.lower() or 'date' in col.lower() or 'time' in col.lower():
-                try:
-                    df_powerbi[col] = pd.to_datetime(df_powerbi[col])
-                except:
-                    pass
-        
-        # Convert to CSV for Power BI import
-        return df_powerbi.to_csv(index=False).encode('utf-8')
-    
-    def to_powerbi_with_metadata(self):
-        """Export to Power BI with metadata file"""
-        # Main data CSV
-        data_csv = self.to_powerbi_ready()
-        
-        # Create metadata JSON
-        numeric_cols = self.df.select_dtypes(include=['number']).columns
-        categorical_cols = self.df.select_dtypes(include=['object']).columns
-        date_cols = self.df.select_dtypes(include=['datetime64']).columns
-        
-        metadata = {
-            'export_date': datetime.now().isoformat(),
-            'table_name': 'Cleaned_Data',
-            'row_count': len(self.df),
-            'column_count': len(self.df.columns),
-            'columns': list(self.df.columns),
-            'numeric_columns': list(numeric_cols),
-            'categorical_columns': list(categorical_cols),
-            'date_columns': list(date_cols),
-            'recommended_measures': {},
-            'recommended_visuals': []
-        }
-        
-        # Add recommended measures
-        for col in numeric_cols[:10]:
-            metadata['recommended_measures'][f'Total_{col}'] = f'SUM(Cleaned_Data[{col}])'
-            metadata['recommended_measures'][f'Average_{col}'] = f'AVERAGE(Cleaned_Data[{col}])'
-        
-        # Add recommended visuals
-        if len(categorical_cols) > 0 and len(numeric_cols) > 0:
-            metadata['recommended_visuals'].append({
-                'type': 'bar_chart',
-                'category': categorical_cols[0],
-                'value': numeric_cols[0],
-                'title': f'{numeric_cols[0]} by {categorical_cols[0]}'
-            })
-        
-        if len(date_cols) > 0 and len(numeric_cols) > 0:
-            metadata['recommended_visuals'].append({
-                'type': 'line_chart',
-                'date': date_cols[0],
-                'value': numeric_cols[0],
-                'title': f'{numeric_cols[0]} Over Time'
-            })
-        
-        metadata_json = json.dumps(metadata, indent=2).encode('utf-8')
-        
-        return {
-            'data': data_csv,
-            'metadata': metadata_json,
-            'instructions': self._get_powerbi_instructions()
-        }
-    
-    def _get_powerbi_instructions(self):
-        """Get step-by-step Power BI import instructions"""
-        instructions = """
-=== POWER BI IMPORT INSTRUCTIONS ===
-
-METHOD 1: Direct Import (Recommended)
-1. Open Power BI Desktop
-2. Click "Get Data" → "Text/CSV"
-3. Select the exported CSV file
-4. Click "Load"
-5. Power BI will auto-detect data types
-
-METHOD 2: Advanced Import
-1. Click "Get Data" → "More..."
-2. Search for "CSV" or "Text"
-3. Select your file
-4. Configure:
-   - First row as headers: YES
-   - Data type detection: Based on first 200 rows
-5. Click "Load"
-
-=== AFTER IMPORT ===
-
-Recommended DAX Measures to Create:
-
-"""
-        return instructions
-    
-    def to_powerbi_zip(self):
-        """Create a zip file with all Power BI resources"""
-        import zipfile
-        
-        output = io.BytesIO()
-        with zipfile.ZipFile(output, 'w', zipfile.ZIP_DEFLATED) as zipf:
-            # Add data CSV
-            data_csv = self.to_powerbi_ready()
-            zipf.writestr('data.csv', data_csv)
-            
-            # Add metadata
-            powerbi_data = self.to_powerbi_with_metadata()
-            zipf.writestr('metadata.json', powerbi_data['metadata'])
-            
-            # Add instructions
-            zipf.writestr('instructions.txt', powerbi_data['instructions'])
-            
-            # Add sample DAX file
-            dax_content = self._generate_dax_file()
-            zipf.writestr('measures.dax', dax_content)
-        
-        output.seek(0)
-        return output.getvalue()
-    
-    def _generate_dax_file(self):
-        """Generate DAX file for Power BI"""
-        numeric_cols = self.df.select_dtypes(include=['number']).columns
-        
-        dax = f"""// DAX Measures for Power BI
-// Generated on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
-// Table Name: Cleaned_Data
-
-// ============ BASIC MEASURES ============
-
-Total Records = COUNTROWS(Cleaned_Data)
-
-"""
-        
-        for col in numeric_cols[:15]:
-            dax += f"""
-// {col} Measures
-Total {col} = SUM(Cleaned_Data[{col}])
-Average {col} = AVERAGE(Cleaned_Data[{col}])
-Min {col} = MIN(Cleaned_Data[{col}])
-Max {col} = MAX(Cleaned_Data[{col}])
-
-"""
-        
-        dax += """
-// ============ HOW TO USE ============
-// 1. In Power BI, go to "Modeling" tab
-// 2. Click "New Measure"
-// 3. Copy-paste any measure above
-// 4. Press Enter to save
-
-// ============ EXAMPLE VISUALS ============
-// - Card Visual: Total Records
-// - Bar Chart: Category vs Total Sales
-// - Line Chart: Date vs Average Value
-"""
-        
-        return dax
-    
-    def to_json(self):
-        """Export to JSON"""
-        return self.df.to_json(orient='records', indent=2).encode('utf-8')
-    
-    def get_powerbi_template(self):
-        """Get Power BI DAX template (legacy - kept for compatibility)"""
-        numeric_cols = self.df.select_dtypes(include=['number']).columns
-        categorical_cols = self.df.select_dtypes(include=['object']).columns
-        
-        template = f"""// Power BI DAX Template for your data
-// Generated on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
-// Table name: Cleaned_Data
-
-// ============ BASIC MEASURES ============
-
-Total Records = COUNTROWS(Cleaned_Data)
-
-"""
-        
-        for col in numeric_cols[:10]:
-            template += f"""
-Total {col} = SUM(Cleaned_Data[{col}])
-Average {col} = AVERAGE(Cleaned_Data[{col}])
-"""
-        
-        template += """
-// ============ HOW TO USE ============
-// 1. Export your data as CSV first
-// 2. In Power BI: Get Data → CSV → Select your file
-// 3. Go to Modeling tab → New Measure
-// 4. Copy and paste any measure above
-// 5. Drag measures to visuals
-
-// ============ RECOMMENDED VISUALS ============
-"""
-        
-        if len(categorical_cols) > 0 and len(numeric_cols) > 0:
-            template += f"""
-- Bar Chart: {categorical_cols[0]} vs {numeric_cols[0]}
-"""
-        
-        if len(self.df.select_dtypes(include=['datetime64']).columns) > 0:
-            template += f"""
-- Line Chart: Date vs {numeric_cols[0] if len(numeric_cols) > 0 else 'Value'}
-"""
-        
+"""Export Utilities - CSV, Excel, and REAL Power BI export"""
+
+import pandas as pd
+import io
+import json
+from datetime import datetime
+
+class ExportUtils:
+    def __init__(self, df):
+        self.df = df
+        
+    def to_csv(self):
+        """Export to CSV"""
+        return self.df.to_csv(index=False).encode('utf-8')
+    
+    def to_excel(self):
+        """Export to Excel with formatting"""
+        output = io.BytesIO()
+        with pd.ExcelWriter(output, engine='openpyxl') as writer:
+            # Write main data
+            self.df.to_excel(writer, sheet_name='Data', index=False)
+            
+            # Add summary sheet
+            numeric_cols = self.df.select_dtypes(include=['number']).columns
+            if len(numeric_cols) > 0:
+                summary = self.df[numeric_cols].describe()
+                summary.to_excel(writer, sheet_name='Summary', index=True)
+            
+            # Add column info sheet
+            col_info = pd.DataFrame({
+                'Column': self.df.columns,
+                'Type': self.df.dtypes.astype(str),
+                'Nulls': self.df.isnull().sum(),
+                'Unique': self.df.nunique()
+            })
+            col_info.to_excel(writer, sheet_name='Column Info', index=False)
+        
+        output.seek(0)
+        return output.getvalue()
+    
+    def to_powerbi_ready(self):
+        """Prepare data for Power BI - Creates CSV optimized for Power BI"""
+        df_powerbi = self.df.copy()
+        
+        # Clean column names (Power BI friendly)
+        df_powerbi.columns = [col.replace(' ', '_').replace('-', '_').replace('/', '_') for col in df_powerbi.columns]
+        
+        # Clean datetime columns for Power BI
+        for col in df_powerbi.columns:
+            if 'datetime' in col.lower() or 'date' in col.lower() or 'time' in col.lower():
+                try:
+                    df_powerbi[col] = pd.to_datetime(df_powerbi[col])
+                except:
+                    pass
+        
+        # Convert to CSV for Power BI import
+        return df_powerbi.to_csv(index=False).encode('utf-8')
+    
+    def to_powerbi_with_metadata(self):
+        """Export to Power BI with metadata file"""
+        # Main data CSV
+        data_csv = self.to_powerbi_ready()
+        
+        # Create metadata JSON
+        numeric_cols = self.df.select_dtypes(include=['number']).columns
+        categorical_cols = self.df.select_dtypes(include=['object']).columns
+        date_cols = self.df.select_dtypes(include=['datetime64']).columns
+        
+        metadata = {
+            'export_date': datetime.now().isoformat(),
+            'table_name': 'Cleaned_Data',
+            'row_count': len(self.df),
+            'column_count': len(self.df.columns),
+            'columns': list(self.df.columns),
+            'numeric_columns': list(numeric_cols),
+            'categorical_columns': list(categorical_cols),
+            'date_columns': list(date_cols),
+            'recommended_measures': {},
+            'recommended_visuals': []
+        }
+        
+        # Add recommended measures
+        for col in numeric_cols[:10]:
+            metadata['recommended_measures'][f'Total_{col}'] = f'SUM(Cleaned_Data[{col}])'
+            metadata['recommended_measures'][f'Average_{col}'] = f'AVERAGE(Cleaned_Data[{col}])'
+        
+        # Add recommended visuals
+        if len(categorical_cols) > 0 and len(numeric_cols) > 0:
+            metadata['recommended_visuals'].append({
+                'type': 'bar_chart',
+                'category': categorical_cols[0],
+                'value': numeric_cols[0],
+                'title': f'{numeric_cols[0]} by {categorical_cols[0]}'
+            })
+        
+        if len(date_cols) > 0 and len(numeric_cols) > 0:
+            metadata['recommended_visuals'].append({
+                'type': 'line_chart',
+                'date': date_cols[0],
+                'value': numeric_cols[0],
+                'title': f'{numeric_cols[0]} Over Time'
+            })
+        
+        metadata_json = json.dumps(metadata, indent=2).encode('utf-8')
+        
+        return {
+            'data': data_csv,
+            'metadata': metadata_json,
+            'instructions': self._get_powerbi_instructions()
+        }
+    
+    def _get_powerbi_instructions(self):
+        """Get step-by-step Power BI import instructions"""
+        instructions = """
+=== POWER BI IMPORT INSTRUCTIONS ===
+
+METHOD 1: Direct Import (Recommended)
+1. Open Power BI Desktop
+2. Click "Get Data" → "Text/CSV"
+3. Select the exported CSV file
+4. Click "Load"
+5. Power BI will auto-detect data types
+
+METHOD 2: Advanced Import
+1. Click "Get Data" → "More..."
+2. Search for "CSV" or "Text"
+3. Select your file
+4. Configure:
+   - First row as headers: YES
+   - Data type detection: Based on first 200 rows
+5. Click "Load"
+
+=== AFTER IMPORT ===
+
+Recommended DAX Measures to Create:
+
+"""
+        return instructions
+    
+    def to_powerbi_zip(self):
+        """Create a zip file with all Power BI resources"""
+        import zipfile
+        
+        output = io.BytesIO()
+        with zipfile.ZipFile(output, 'w', zipfile.ZIP_DEFLATED) as zipf:
+            #Add data CSV
+            data_csv = self.to_powerbi_ready()
+            zipf.writestr('data.csv', data_csv)
+            
+            #Add metadata
+            powerbi_data = self.to_powerbi_with_metadata()
+            zipf.writestr('metadata.json', powerbi_data['metadata'])
+            
+            #Add instructions
+            zipf.writestr('instructions.txt', powerbi_data['instructions'])
+            
+            #Add sample DAX file
+            dax_content = self._generate_dax_file()
+            zipf.writestr('measures.dax', dax_content)
+        
+        output.seek(0)
+        return output.getvalue()
+    
+    def _generate_dax_file(self):
+        """Generate DAX file for Power BI"""
+        numeric_cols = self.df.select_dtypes(include=['number']).columns
+        
+        dax = f"""// DAX Measures for Power BI
+// Generated on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
+// Table Name: Cleaned_Data
+
+// ============ BASIC MEASURES ============
+
+Total Records = COUNTROWS(Cleaned_Data)
+
+"""
+        
+        for col in numeric_cols[:15]:
+            dax += f"""
+// {col} Measures
+Total {col} = SUM(Cleaned_Data[{col}])
+Average {col} = AVERAGE(Cleaned_Data[{col}])
+Min {col} = MIN(Cleaned_Data[{col}])
+Max {col} = MAX(Cleaned_Data[{col}])
+
+"""
+        
+        dax += """
+// ============ HOW TO USE ============
+// 1. In Power BI, go to "Modeling" tab
+// 2. Click "New Measure"
+// 3. Copy-paste any measure above
+// 4. Press Enter to save
+
+// ============ EXAMPLE VISUALS ============
+// - Card Visual: Total Records
+// - Bar Chart: Category vs Total Sales
+// - Line Chart: Date vs Average Value
+"""
+        
+        return dax
+    
+    def to_json(self):
+        """Export to JSON"""
+        return self.df.to_json(orient='records', indent=2).encode('utf-8')
+    
+    def get_powerbi_template(self):
+        """Get Power BI DAX template (legacy - kept for compatibility)"""
+        numeric_cols = self.df.select_dtypes(include=['number']).columns
+        categorical_cols = self.df.select_dtypes(include=['object']).columns
+        
+        template = f"""// Power BI DAX Template for your data
+// Generated on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
+// Table name: Cleaned_Data
+
+// ============ BASIC MEASURES ============
+
+Total Records = COUNTROWS(Cleaned_Data)
+
+"""
+        
+        for col in numeric_cols[:10]:
+            template += f"""
+Total {col} = SUM(Cleaned_Data[{col}])
+Average {col} = AVERAGE(Cleaned_Data[{col}])
+"""
+        
+        template += """
+// ============ HOW TO USE ============
+// 1. Export your data as CSV first
+// 2. In Power BI: Get Data → CSV → Select your file
+// 3. Go to Modeling tab → New Measure
+// 4. Copy and paste any measure above
+// 5. Drag measures to visuals
+
+// ============ RECOMMENDED VISUALS ============
+"""
+        
+        if len(categorical_cols) > 0 and len(numeric_cols) > 0:
+            template += f"""
+- Bar Chart: {categorical_cols[0]} vs {numeric_cols[0]}
+"""
+        
+        if len(self.df.select_dtypes(include=['datetime64']).columns) > 0:
+            template += f"""
+- Line Chart: Date vs {numeric_cols[0] if len(numeric_cols) > 0 else 'Value'}
+"""
+        
         return template