Update README.md

README.md

@@ -9,6 +9,13 @@ base_model:
 pipeline_tag: image-classification
 library_name: transformers
 ---
+
+![zdfghgdftg.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/N8f6dbYHMmE02vq5wseAE.png)
+
+# **Multilabel-Portrait-SigLIP2**
+
+> **Multilabel-Portrait-SigLIP2** is a vision-language model fine-tuned from [**google/siglip2-base-patch16-224**](https://huggingface.co/google/siglip2-base-patch16-224) using the `SiglipForImageClassification` architecture. It classifies portrait-style images into one of the following **visual portrait categories**:
+
 ```py
  Classification Report:
                   precision    recall  f1-score   support
@@ -23,4 +30,83 @@ Cartoon Portrait     0.9964    0.9926    0.9945      4444
     weighted avg     0.9972    0.9972    0.9972     17776
 ```
 
-![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/vOq9EHhJGLzRJSQJ5_liQ.png)
+![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/vOq9EHhJGLzRJSQJ5_liQ.png)
+
+---
+
+# **Model Objective**
+
+The model is designed to **analyze portrait images** and categorize them into **one of four distinct portrait types**:
+
+- **0:** Anime Portrait  
+- **1:** Cartoon Portrait  
+- **2:** Real Portrait  
+- **3:** Sketch Portrait
+
+---
+
+# **Try it with Transformers 🤗**
+
+Install dependencies:
+
+```bash
+pip install -q transformers torch pillow gradio
+```
+
+Run the model with the following script:
+
+```python
+import gradio as gr
+from transformers import AutoImageProcessor, SiglipForImageClassification
+from PIL import Image
+import torch
+
+# Load model and processor
+model_name = "prithivMLmods/Multilabel-Portrait-SigLIP2"  # Replace with actual HF model path
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# Label mapping
+id2label = {
+    0: "Anime Portrait",
+    1: "Cartoon Portrait",
+    2: "Real Portrait",
+    3: "Sketch Portrait"
+}
+
+def classify_portrait(image):
+    """Predict the type of portrait style from an 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()
+
+    predictions = {id2label[i]: round(probs[i], 3) for i in range(len(probs))}
+    predictions = dict(sorted(predictions.items(), key=lambda item: item[1], reverse=True))
+    return predictions
+
+# Gradio interface
+iface = gr.Interface(
+    fn=classify_portrait,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Portrait Type Prediction Scores"),
+    title="Multilabel-Portrait-SigLIP2",
+    description="Upload a portrait-style image (anime, cartoon, real, or sketch) to predict its most likely visual category."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+# **Intended Use Cases**
+
+-  **AI Art Curation** — Automatically organize large-scale datasets of artistic portraits.  
+-  **Style-based Portrait Analysis** — Determine artistic style in user-uploaded or curated portrait datasets.  
+-  **Content Filtering for Platforms** — Group and recommend based on visual aesthetics.  
+-  **Dataset Pre-labeling** — Helps reduce manual effort in annotation tasks.  
+-  **User Avatar Classification** — Profile categorization in social or gaming platforms.