Update README.md

README.md

@@ -3,6 +3,11 @@ license: apache-2.0
 datasets:
 - deepghs/anime_classification
 ---
+![7.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/mc0ozkey2t5-c_YM8lQ21.png)
+
+# **Anime-Classification-v1.0**
+
+> **Anime-Classification-v1.0** 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 anime-related images using the **SiglipForImageClassification** architecture.
 
 ```py
 Classification Report:
@@ -20,3 +25,77 @@ weighted avg     0.8670    0.8648    0.8656     21373
 
 ![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/5jH61328ZIygBR0ExlWVv.png)
 
+---
+
+The model categorizes images into 4 anime-related classes:
+
+```
+    Class 0: "3D"
+    Class 1: "Bangumi"
+    Class 2: "Comic"
+    Class 3: "Illustration"
+```
+
+---
+
+## **Install dependencies**
+
+```python
+!pip install -q transformers torch pillow gradio
+```
+
+---
+
+## **Inference Code**
+
+```python
+import gradio as gr
+from transformers import AutoImageProcessor, SiglipForImageClassification
+from PIL import Image
+import torch
+
+# Load model and processor
+model_name = "prithivMLmods/Anime-Classification-v1.0"  # New model name
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+def classify_anime_image(image):
+    """Predicts the anime category for an input 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": "3D", "1": "Bangumi", "2": "Comic", "3": "Illustration"
+    }
+    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
+    
+    return predictions
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=classify_anime_image,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Prediction Scores"),
+    title="Anime Classification v1.0",
+    description="Upload an image to classify the anime style category."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+## **Intended Use:**
+
+The **Anime-Classification-v1.0** model is designed to classify anime-related images. Potential use cases include:
+
+- **Content Tagging:** Automatically label anime artwork on platforms or apps.
+- **Recommendation Engines:** Enhance personalized anime content suggestions.
+- **Digital Art Curation:** Organize galleries by anime style for artists and fans.
+- **Dataset Filtering:** Categorize and filter images during dataset creation.