Update README.md

README.md

@@ -1,12 +1,11 @@
 ---
----
 title: "Actuarial Model Point Generator"
 emoji: 🏗️
-colorFrom: gray
+colorFrom: green
 colorTo: red
 sdk: gradio
 sdk_version: 5.31.0
-app_file: app.py
+app_file: app.py  # update this if your main file name is different
 pinned: false
 license: mit
 short_description: Generate synthetic actuarial model points
@@ -18,44 +17,76 @@ tags:
   - data-generation
   - gradio
   - dashboard
-  - portfolio-analytics
+  - excel
 ---
 
-
 # 🏗️ Actuarial Model Point Generator
 
-A professional Gradio dashboard for **generating and analyzing synthetic insurance model points**.  
-Designed for actuaries and data scientists who need realistic portfolio data for testing, training, and analysis.
+A flexible Gradio app to generate fully customized **synthetic seriatim model points** for use in actuarial testing, clustering, or analytics.
 
 [![Open in Spaces](https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg)](https://huggingface.co/spaces/alidenewade/actuarial-model-point-generator)
 
 ---
 
-## 🌟 Key Features
+## 🌟 What’s New
+
+This version adds **complete UI control** over generation logic:
+
+- 👥 Number of policies (100 to 50,000)
+- 🎲 Random seed for reproducibility
+- 👶 Age range (min/max)
+- 💵 Sum assured range
+- 📆 Multiple selectable policy terms (5–30 years)
+- 🧑 Include or exclude sex (M/F)
+- 📦 Choose between fixed or variable policy count
+
+---
+
+## 🧮 Output Columns
+
+Each generated row represents a policy and includes:
+
+- `age_at_entry`: Issue age
+- `sex`: "M", "F", or "U" (unspecified)
+- `policy_term`: Chosen from selected terms
+- `policy_count`: Fixed (1) or random (1–100)
+- `sum_assured`: Uniformly distributed between min/max
+- `duration_mth`: In-force duration, capped by policy term
+
+All rows are indexed by `policy_id`.
+
+---
+
+## ✅ How to Use
 
-| Category | Highlights |
-| -------- | ---------- |
-| **📊 Interactive Generation** | • Set policy count, age/sum assured ranges, terms<br>• Positive **duration** ensures in-force modeling<br>• Supports **sex**, **policy counts**, and reproducible outputs |
-| **📈 Advanced Analytics** | • Distribution charts per field<br>• Correlation matrix<br>• Box plots by term and duration<br>• Portfolio-level summaries |
-| **📋 Professional Output** | • View table of generated policies<br>• Export to CSV<br>• Summary statistics by variable |
-| **🎯 Actuarial Applications** | Ideal for:<br>• Model clustering<br>• Stress testing<br>• Product development<br>• Risk exposure profiling<br>• Simulation input |
-| **🔧 Customization Options** | - Policy count: 100–50,000<br>- Age: 18–80<br>- Sum assured: custom min–max<br>- Policy term: multi-select (5–30 years)<br>- Sex: optional<br>- Policy count per row: fixed or variable |
+1. Adjust your filters on the left
+2. Click **“Generate Model Points”**
+3. Preview the results in the table
+4. Click **“Download Excel”** to save the data
 
 ---
 
-## 🚀 Quick Start (Hugging Face Spaces)
+## 🧠 Use Cases
 
-1. Open [this Space](https://huggingface.co/spaces/alidenewade/actuarial-model-point-generator)  
-2. Configure generation parameters  
-3. Click **“Generate Portfolio”**  
-4. Explore the analytics and export CSV if needed
+- Cluster-based model point selection
+- Stress testing & actuarial simulations
+- Product mix scenario planning
+- Teaching or training actuarial students
+- Model validation tools
+
+---
+
+## 📦 File Export
+
+The download button exports the data to Excel (`.xlsx`) with the index (`policy_id`) included.  
+Warnings will be shown if inputs are invalid (e.g., min age ≥ max age).
 
 ---
 
 ## 🛠️ Local Installation
 
 ```bash
-# Clone this repository
+# Clone the repo
 git clone https://github.com/YOUR-USERNAME/actuarial-model-point-generator.git
 cd actuarial-model-point-generator
 
@@ -64,3 +95,19 @@ pip install -r requirements.txt
 
 # Run the app
 python app.py
+```
+---
+## 🙌 Acknowledgements
+
+
+Huge thanks to the Lifelib community for their open-source contributions to life actuarial modeling in Python.
+This project draws inspiration from their work on model point clustering and stochastic modeling tools.
+
+Check them out at: https://github.com/lifelib-dev/lifelib
+
+---
+
+## 📄 License
+This project is released under the MIT License.
+
+Created with ❤️ by @alidenewade for the actuarial analytics community.