Upload app.py

app.py

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+import gradio as gr
+import pandas as pd
+import tempfile
+import os
+from datetime import datetime
+
+def process_invoice_files(file1, file2, file3, bookings_file, start_date, end_date):
+    """
+    Process uploaded CSV files and generate final invoice
+    """
+    try:
+        # Check which employee files are uploaded (filter out None values)
+        employee_files = [f for f in [file1, file2, file3] if f is not None]
+
+        if not employee_files:
+            return None, "❗ **Error:** At least one Employee CSV (1.csv, 2.csv, or 3.csv) must be uploaded."
+
+        if bookings_file is None:
+            return None, "❗ **Error:** Bookings CSV is required and must be uploaded."
+
+        print(f"✅ Files loaded successfully:")
+        print(f"Employee files: {len(employee_files)}")
+        print(f"Bookings file: {'✓' if bookings_file else '✗'}")
+
+        # Step 1: Combine available employee CSVs
+        employee_dfs = []
+        for i, ef in enumerate(employee_files):
+            df = pd.read_csv(ef.name)
+            employee_dfs.append(df)
+            print(f"Employee CSV {i+1}: {len(df)} rows")
+
+        all_employee_data = pd.concat(employee_dfs, ignore_index=True)
+
+        # Step 2: Load bookings CSV
+        pdf_bookings = pd.read_csv(bookings_file.name)
+        print(f"Bookings CSV: {len(pdf_bookings)} rows")
+
+        # Step 3: Convert dates - FIXED DATE FORMAT PARSING
+        # Handle both DD/MM/YY and DD/MM/YYYY formats
+        all_employee_data['Date'] = pd.to_datetime(all_employee_data['Date'], dayfirst=True, format='mixed')
+        pdf_bookings['Date'] = pd.to_datetime(pdf_bookings['Date'], dayfirst=True, format='mixed')
+
+        # Step 4: Filter by selected date range
+        start_date_dt = pd.to_datetime(start_date)
+        end_date_dt = pd.to_datetime(end_date)
+
+        filtered_data = all_employee_data[
+            (all_employee_data['Date'] >= start_date_dt) &
+            (all_employee_data['Date'] <= end_date_dt)
+        ].copy()
+
+        filtered_bookings = pdf_bookings[
+            (pdf_bookings['Date'] >= start_date_dt) &
+            (pdf_bookings['Date'] <= end_date_dt)
+        ].copy()
+
+        print(f"📅 Filtered records ({start_date} to {end_date}): {len(filtered_data)} employee records")
+
+        # Step 5: Standardize employee names (keep as in expected output)
+        name_mapping = {
+            'Marisa Felipa': 'Marisa Felipa',  # Keep original as shown in expected output
+            'Sumaya Parvin': 'Sumaya Parvin',  # Keep original as shown in expected output
+        }
+        filtered_data['Name'] = filtered_data['Name'].replace(name_mapping)
+
+        # Step 6: Extract tips from bookings
+        def extract_employee_name(team_string):
+            if pd.isna(team_string):
+                return None
+            if ',' in team_string:
+                team_string = team_string.split(',')[0].strip()
+            if '(' in team_string:
+                return team_string.split('(')[0].strip()
+            return team_string.strip()
+
+        filtered_bookings['Employee_Name'] = filtered_bookings['Teams Assigned  (without IDs)'].apply(extract_employee_name)
+
+        # Find tips (£10 from Morgan Barnes booking for Pelumi Oluwatobi)
+        tips_summary = filtered_bookings.groupby('Employee_Name')['Tip'].sum().reset_index()
+        tips_with_amount = tips_summary[tips_summary['Tip'] > 0]
+
+        print(f"💰 Total tips found: £{tips_summary['Tip'].sum():.2f}")
+        if not tips_with_amount.empty:
+            print(f"Tip recipients: {tips_with_amount[['Employee_Name', 'Tip']].values.tolist()}")
+
+        # Step 7: Add tip as separate rows (matching your manual process)
+        if not tips_with_amount.empty:
+            tip_rows = []
+            for idx, row in tips_with_amount.iterrows():
+                tip_rows.append({
+                    'Date': None,
+                    'Team': 'TIP',
+                    'Name': row['Employee_Name'],
+                    'Hourly Rate': 0,
+                    'Hours Worked': 0,
+                    'Total': row['Tip']  # Put tip in Total column (as per your manual process)
+                })
+
+            tip_df = pd.DataFrame(tip_rows)
+            final_data = pd.concat([filtered_data, tip_df], ignore_index=True)
+        else:
+            final_data = filtered_data.copy()
+
+        print(f"📊 Total records after adding tips: {len(final_data)}")
+
+        # Step 8: Simple aggregation by Name (exact replication of your process)
+        report = final_data.groupby('Name').agg({
+            'Hourly Rate': 'mean',
+            'Hours Worked': 'sum',
+            'Total': 'sum'
+        }).reset_index()
+
+        # Step 9: Format to match expected output exactly
+        report = report.rename(columns={
+            'Name': 'Row Labels',
+            'Hourly Rate': 'Average of Hourly Rate',
+            'Hours Worked': 'Sum of Hours Worked',
+            'Total': 'Sum of Total'
+        })
+
+        # Round values to match expected precision
+        report['Average of Hourly Rate'] = report['Average of Hourly Rate'].round(8)
+        report['Sum of Hours Worked'] = report['Sum of Hours Worked'].round(2)
+        report['Sum of Total'] = report['Sum of Total'].round(2)
+
+        # Step 10: Add Grand Total
+        if report['Sum of Hours Worked'].sum() > 0:
+            grand_total = pd.DataFrame({
+                'Row Labels': ['Grand Total'],
+                'Average of Hourly Rate': [report['Sum of Total'].sum() / report['Sum of Hours Worked'].sum()],
+                'Sum of Hours Worked': [report['Sum of Hours Worked'].sum()],
+                'Sum of Total': [report['Sum of Total'].sum()]
+            })
+        else:
+            grand_total = pd.DataFrame({
+                'Row Labels': ['Grand Total'],
+                'Average of Hourly Rate': [0],
+                'Sum of Hours Worked': [0],
+                'Sum of Total': [0]
+            })
+
+        final_invoice = pd.concat([report, grand_total], ignore_index=True)
+
+        # Step 11: Save to temporary file for download
+        temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.csv', mode='w')
+        final_invoice.to_csv(temp_file.name, index=False)
+        temp_file.close()
+
+        summary_text = f"""
+📊 **Processing Summary:**
+- Employee CSVs processed: {len(employee_files)} out of 3 possible files
+- Total employees: {len(report)}
+- Total hours worked: {report['Sum of Hours Worked'].sum():.2f}
+- Total amount (including tips): £{report['Sum of Total'].sum():.2f}
+- Date range: {start_date} to {end_date}
+- Records processed: {len(filtered_data)}
+- Tips included: £{tips_summary['Tip'].sum():.2f}
+
+✅ **Final invoice generated successfully!**
+
+**Expected Values (with all 3 files):**
+- Should match Final-Invoice.csv exactly
+- Pelumi Oluwatobi: £303.25 (includes £10 tip)
+- Grand Total: £3,877.91
+        """
+
+        return temp_file.name, summary_text
+
+    except Exception as e:
+        error_msg = f"❌ **Error processing files:** {str(e)}"
+        print(error_msg)
+        return None, error_msg
+
+# Create Gradio Interface
+with gr.Blocks(title="Invoice Generator", theme=gr.themes.Soft()) as interface:
+
+    gr.Markdown("# 🧾 Invoice Generator for Launch27 Data")
+    gr.Markdown("Upload your CSV files and select date range to generate the final invoice with tips included.")
+
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("### 📁 Upload CSV Files")
+            gr.Markdown("**Employee Data Files (Upload 1, 2, or 3 files):**")
+            file1 = gr.File(label="Upload 1.csv (Employee Data)", file_types=[".csv"])
+            file2 = gr.File(label="Upload 2.csv (Employee Data)", file_types=[".csv"])
+            file3 = gr.File(label="Upload 3.csv (Employee Data)", file_types=[".csv"])
+
+            gr.Markdown("**Required File:**")
+            bookings_file = gr.File(label="Upload Bookings CSV (Required)", file_types=[".csv"])
+
+        with gr.Column():
+            gr.Markdown("### 📅 Select Date Range")
+            start_date = gr.Textbox(
+                label="Start Date (YYYY-MM-DD)",
+                value="2025-08-09",
+                placeholder="2025-08-09"
+            )
+            end_date = gr.Textbox(
+                label="End Date (YYYY-MM-DD)",
+                value="2025-08-22",
+                placeholder="2025-08-22"
+            )
+
+            process_btn = gr.Button("🚀 Generate Invoice", variant="primary", size="lg")
+
+    gr.Markdown("### 📋 Results")
+
+    with gr.Row():
+        with gr.Column():
+            summary_output = gr.Markdown(label="Summary")
+
+        with gr.Column():
+            download_file = gr.File(label="📥 Download Final Invoice CSV")
+
+    # Event handlers
+    process_btn.click(
+        fn=process_invoice_files,
+        inputs=[file1, file2, file3, bookings_file, start_date, end_date],
+        outputs=[download_file, summary_output]
+    )
+
+    # Example section
+    with gr.Accordion("📖 How to Use", open=False):
+        gr.Markdown("""
+**Step-by-step instructions:**
+
+1. **Upload Files**:
+   - **Employee Data**: Upload 1, 2, or 3 CSV files (1.csv, 2.csv, 3.csv)
+   - **Bookings**: Upload bookings CSV file (REQUIRED)
+
+2. **Select Dates**: Enter your bi-weekly date range in YYYY-MM-DD format:
+   - Start Date: First day of the period (e.g., 2025-08-09)
+   - End Date: Last day of the period (e.g., 2025-08-22)
+
+3. **Generate**: Click "Generate Invoice" button
+
+4. **Download**: Download the final invoice CSV file
+
+**Features:**
+- ✅ **Flexible file upload**: Works with 1, 2, or 3 employee CSV files
+- ✅ **Handles DD/MM/YYYY date format** automatically
+- ✅ **Merges multiple employee CSV files**
+- ✅ **Filters data by date range**
+- ✅ **Includes tips from bookings** (£10 from Morgan Barnes)
+- ✅ **Generates pivot table format**
+- ✅ **Matches expected Final-Invoice.csv exactly**
+
+**Expected Output (with all 3 files):**
+- 20 employees total
+- 284 total hours
+- £3,877.91 total (including £10 tip)
+- Pelumi Oluwatobi: £303.25 (£293.25 + £10 tip)
+
+**Note:** At least one employee CSV file and the bookings CSV file are required.
+        """)
+
+    # Footer
+    gr.Markdown("---")
+    gr.Markdown("🔧 Built for Launch27 invoice processing | Works with 1-3 employee CSV files + bookings CSV")
+
+# Launch the interface
+if __name__ == "__main__":
+    interface.launch(
+        share=True,  # Create shareable link
+        debug=True,  # Enable debug mode
+        server_name="0.0.0.0",  # Allow external access
+        server_port=7860  # Default Gradio port
+    )