Update README.md

README.md

@@ -2,7 +2,23 @@
 license: apache-2.0
 datasets:
 - prithivMLmods/Math-Shapes
+language:
+- en
+base_model:
+- google/siglip2-base-patch16-224
+pipeline_tag: image-classification
+library_name: transformers
+tags:
+- Shapes
+- Geometric
+- SigLIP2
+- art
 ---
+
+# **Geometric-Shapes-Classification**
+
+> **Geometric-Shapes-Classification** is an image classification vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for a multi-class shape recognition task. It classifies various geometric shapes using the **SiglipForImageClassification** architecture.
+
 ```py
 Classification Report:
                  precision    recall  f1-score   support
@@ -21,4 +37,86 @@ Parallelogram ▰     0.9926    0.9840    0.9883      1500
    weighted avg     0.9908    0.9908    0.9907     12000
 ```
 
-![download (3).png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/WAdeb9cy5DBb-zZ0TPx0k.png)
+![download (3).png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/WAdeb9cy5DBb-zZ0TPx0k.png)
+
+The model categorizes images into the following classes:
+
+- **Class 0:** Circle ◯  
+- **Class 1:** Kite ⬰  
+- **Class 2:** Parallelogram ▰  
+- **Class 3:** Rectangle ▭  
+- **Class 4:** Rhombus ◆  
+- **Class 5:** Square ◼  
+- **Class 6:** Trapezoid ⏢  
+- **Class 7:** Triangle ▲  
+
+---
+
+# **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 PIL import Image
+import torch
+
+# Load model and processor
+model_name = "prithivMLmods/Geometric-Shapes-Classification"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# Label mapping with symbols
+labels = {
+    "0": "Circle ◯",
+    "1": "Kite ⬰",
+    "2": "Parallelogram ▰",
+    "3": "Rectangle ▭",
+    "4": "Rhombus ◆",
+    "5": "Square ◼",
+    "6": "Trapezoid ⏢",
+    "7": "Triangle ▲"
+}
+
+def classify_shape(image):
+    """Classifies the geometric shape in the 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()
+
+    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
+    
+    return predictions
+
+# Gradio interface
+iface = gr.Interface(
+    fn=classify_shape,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Prediction Scores"),
+    title="Geometric Shapes Classification",
+    description="Upload an image to classify geometric shapes such as circle, triangle, square, and more."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+# **Intended Use**
+
+The **Geometric-Shapes-Classification** model is designed to recognize basic geometric shapes in images. Example use cases:
+
+- **Educational Tools:** For learning and teaching geometry visually.  
+- **Computer Vision Projects:** As a shape detector in robotics or automation.  
+- **Image Analysis:** Recognizing symbols in diagrams or engineering drafts.  
+- **Assistive Technology:** Supporting shape identification for visually impaired applications.