Update README.md

README.md

@@ -1,39 +1,55 @@
----
-language: en
-tags:
-- tensorflow
-- image-classification
-license: mit
-datasets: []
-pipeline_tag: image-classification
-library_name: tensorflow
----
-
-# Wrist Undertone Classifier 🩵🩷💛
-
-This TensorFlow model predicts whether a user's wrist undertone is **Cool**, **Warm**, or **Neutral** based on an uploaded image of their wrist.
-
-## 🧠 Model Details
-
-- Framework: TensorFlow (SavedModel format)
-- Input: Wrist image (`.jpg`, `.png`, etc.)
-- Output: One of three classes:  
-  - `cool`
-  - `warm`
-  - `neutral`
-
-## 🖼️ Example Usage (Python)
-
-```python
-from huggingface_hub import InferenceClient
-
-client = InferenceClient("https://huggingface.co/Salvatore7262/ronaundertone-detector")
-
-with open("my_wrist.jpg", "rb") as f:
-    result = client.post(
-        json=None,
-        data=f,
-        headers={"Content-Type": "image/jpeg"},
-    )
-
-print(result.json())
+---
+language: en
+tags:
+- tensorflow
+- image-classification
+- undertone
+- skin-tone
+- beauty-tech
+license: mit
+datasets: []
+pipeline_tag: image-classification
+library_name: tensorflow
+---
+
+# 🩵 Wrist Undertone Classifier 🩷💛  
+> Determine your skin undertone (Cool, Warm, or Neutral) using a wrist photo!
+
+---
+
+## 🧠 Overview
+
+This TensorFlow model predicts whether a user's wrist undertone is **Cool**, **Warm**, or **Neutral** based on an uploaded image of their wrist.  
+It is designed for use in beauty, skincare, and fashion applications — helping users find colors that best match their natural undertone.
+
+---
+
+## ⚙️ Model Details
+
+| Property | Description |
+|-----------|--------------|
+| **Framework** | TensorFlow (SavedModel format) |
+| **Input** | Wrist image (`.jpg`, `.jpeg`, `.png`) |
+| **Output** | One of three classes: `cool`, `warm`, or `neutral` |
+| **License** | MIT |
+| **Language** | English |
+
+---
+
+## 🧩 Example Inference (Python)
+
+You can use the [🤗 `huggingface_hub`](https://pypi.org/project/huggingface-hub/) client to test this model programmatically.
+
+```python
+from huggingface_hub import InferenceClient
+
+client = InferenceClient("https://api-inference.huggingface.co/models/Salvatore7262/ronaundertone-detector")
+
+with open("my_wrist.jpg", "rb") as f:
+    result = client.post(
+        json=None,
+        data=f,
+        headers={"Content-Type": "image/jpeg"},
+    )
+
+print(result.json())