📊 Service Request Analytics Dashboard

A powerful analytics dashboard built with Streamlit for visualizing and analyzing service request data.

🚀 Features

• File Upload: Users can upload their own Excel files (.xlsx)
• Interactive Dashboard: Multiple visualizations including:
  • Daily volume trends with moving averages
  • Request type analysis
  • Location-based insights
  • Team distribution
  • Time-based heatmaps
  • Monthly trends
• Filtering: Filter data by date range, location, and team
• Recurring Issues Analysis: View AI-identified recurring issues (if available)
• Raw Data Explorer: Browse and download filtered data

📋 Requirements

Your Excel file should contain the following columns:

• Request_Date: Date and time of the request
• Location: Location of the request
• Team: Team handling the request
• Request_Type: Type of service request
• SLA_Status: SLA compliance status (BREACH/MET)
• Requester_Name: Name of the requester

🛠️ Installation

1. Install Python 3.8 or higher
2. Install dependencies:

pip install -r requirements.txt

🏃 Running Locally

Run the dashboard with:

streamlit run dashboard.py

The dashboard will open in your browser at http://localhost:8501

☁️ Deployment Options

Streamlit Cloud (Recommended)

1. Push your code to GitHub
2. Go to share.streamlit.io
3. Sign in with GitHub
4. Deploy the app by selecting your repository
5. Users can then upload their Excel files directly in the web interface

Other Options

• Heroku: Use the Streamlit buildpack
• AWS/Azure/GCP: Deploy as a containerized application
• On-premise: Run on your own server with reverse proxy

📝 Usage

1. Open the dashboard
2. Upload your Excel file using the file uploader
3. Use the sidebar filters to narrow down your data
4. Navigate between different pages:
   • Dashboard: Main analytics overview
   • Recurring Issues: AI-identified recurring problems (optional)
   • Raw Data: View and download filtered data

🔐 Security Note

When deploying publicly, ensure:

• Users understand data privacy implications
• Sensitive data is handled appropriately
• Access controls are in place if needed

📊 Optional: Recurring Issues

The "Recurring Issues" feature requires pre-processed CSV files. To enable this:

1. Place one of these files in the same directory as the dashboard:
   • gemini_analysis_cache.pkl
   • recurring_issues_detailed_ar_en.csv
   • recurring_issues_simple_20251129_2242.csv

💡 Tips

• The dashboard caches data for better performance
• Large files may take a moment to process
• Use the filters to focus on specific time periods or locations
• Download filtered data as CSV for further analysis

🐛 Troubleshooting

Error loading file?

• Ensure your Excel file has all required columns
• Check that date formats are correct
• Verify the file isn't password-protected

Slow performance?

• Try filtering to a smaller date range
• Ensure your file isn't excessively large (>100MB)

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Built with ❤️ using Streamlit