Update README.md

README.md

@@ -1,3 +1,101 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+base_model:
+- google/siglip2-base-patch16-224
+pipeline_tag: image-classification
+library_name: transformers
+tags:
+- graphic
+- 2d
+- 3d
+- image-classifier
+- art
+---
+
+# **Graphic-Class**
+
+> **Graphic-Class** is a vision model fine-tuned from **google/siglip2-base-patch16-224** for **graphic content moderation**. It uses the **SiglipForImageClassification** architecture to classify graphical images (such as UI designs, 2D game assets, digital art) into **safe** or **problematic** categories.
+
+---
+
+## **Label Space: 2 Classes**
+
+The model classifies each image into one of the following categories:
+
+```
+0: bad
+1: good
+```
+
+* `bad`: images with bad symbols, inappropriate or offensive text, broken UI/UX elements, distorted or harmful designs.
+* `good`: plain, safe, or character-rich graphics, such as 2D game elements, educational visuals, or well-structured UI components.
+
+---
+
+## **Install Dependencies**
+
+```bash
+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/Graphic-Class"  # Replace with your model path if different
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# Label mapping
+id2label = {
+    "0": "bad",
+    "1": "good"
+}
+
+def classify_graphic(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()
+
+    prediction = {
+        id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))
+    }
+
+    return prediction
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=classify_graphic,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(num_top_classes=2, label="Graphic Content Classification"),
+    title="Graphic-Class",
+    description="Upload a graphic or design asset to classify it as 'good' or 'bad'."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+## **Intended Use**
+
+**Graphic-Class** can be used for:
+
+* **Graphic Content Moderation** – Automatically filter unsafe or visually inappropriate designs in creative pipelines.
+* **Game Asset Filtering** – Evaluate textures, objects, or sprites for suitability in game environments.
+* **UI/UX Quality Control** – Detect broken or low-quality interface components in design feedback loops.
+* **Educational & Kids App Filtering** – Ensure graphics meet safety and design standards for children's content.