Update README.md

README.md

@@ -14,6 +14,12 @@ tags:
 - CrossEmoji Classifier
 ---
 
+![14.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/_mq83JtFZrdirluVNXqX2.png)
+
+# **Emoji-Scope**  
+
+> **Emoji-Scope** is an image classification vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for a single-label classification task. It is designed to classify emoji images into different style categories using the **SiglipForImageClassification** architecture.  
+
 ```py
     Classification Report:
                      precision    recall  f1-score   support
@@ -36,3 +42,85 @@ tags:
 ```
 
 ![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/GAcbnAUhe4Tsf4G6LwEt8.png)
+
+The model categorizes images into eleven emoji styles:  
+- **Class 0:** "Apple Style"  
+- **Class 1:** "DoCoMo Style"  
+- **Class 2:** "Facebook Style"  
+- **Class 3:** "Gmail Style"  
+- **Class 4:** "Google Style"  
+- **Class 5:** "JoyPixels Style"  
+- **Class 6:** "KDDI Style"  
+- **Class 7:** "Samsung Style"  
+- **Class 8:** "SoftBank Style"  
+- **Class 9:** "Twitter Style"  
+- **Class 10:** "Windows Style"  
+
+# **Run with Transformers🤗**  
+
+```python
+!pip install -q transformers torch pillow gradio
+```
+
+```python
+import gradio as gr
+from transformers import AutoImageProcessor
+from transformers import SiglipForImageClassification
+from transformers.image_utils import load_image
+from PIL import Image
+import torch
+
+# Load model and processor
+model_name = "prithivMLmods/Emoji-Scope"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+def emoji_classification(image):
+    """Predicts the style category of an emoji image."""
+    image = Image.fromarray(image).convert("RGB")
+    inputs = processor(images=image, return_tensors="pt")
+    
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
+    
+    labels = {
+        "0": "Apple Style", 
+        "1": "DoCoMo Style", 
+        "2": "Facebook Style", 
+        "3": "Gmail Style", 
+        "4": "Google Style", 
+        "5": "JoyPixels Style", 
+        "6": "KDDI Style", 
+        "7": "Samsung Style", 
+        "8": "SoftBank Style", 
+        "9": "Twitter Style", 
+        "10": "Windows Style"
+    }
+    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
+    
+    return predictions
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=emoji_classification,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Prediction Scores"),
+    title="Emoji Style Classification",
+    description="Upload an emoji image to classify its style."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```  
+
+# **Intended Use:**  
+
+The **Emoji-Scope** model is designed to classify emoji images based on different style categories. Potential use cases include:  
+
+- **Emoji Standardization:** Identifying different emoji styles across platforms.  
+- **User Experience Design:** Helping developers ensure consistency in emoji usage.  
+- **Digital Art & Design:** Assisting artists in selecting preferred emoji styles.  
+- **Educational Purposes:** Teaching differences in emoji representation.