Update README.md

README.md

@@ -1,7 +1,27 @@
 ---
 license: apache-2.0
+datasets:
+- ylecun/mnist
+language:
+- en
+base_model:
+- google/siglip2-base-patch16-224
+pipeline_tag: image-classification
+library_name: transformers
+tags:
+- Digits
+- Mnist
+- SigLIP2
+- 0-t0-9
+- Number-Classification
 ---
 
+![fQPjrpOKabPgt_9vCH4Qj.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/rB4X4q0YZkX0WJW6fZ83F.png)
+
+# **Mnist-Digits-SigLIP2**  
+
+> **Mnist-Digits-SigLIP2** is an image classification model fine-tuned from **google/siglip2-base-patch16-224** to classify handwritten digits (0-9) using the **SiglipForImageClassification** architecture. It is trained on the MNIST dataset for accurate digit recognition.  
+
 ```py
 Classification Report:
               precision    recall  f1-score   support
@@ -23,3 +43,85 @@ weighted avg     0.9928    0.9928    0.9928     60000
 ```
 
 ![download (2).png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/qUaioZfL840_BrRhReCqd.png)
+
+### **Classes:**  
+- **Class 0:** "0"  
+- **Class 1:** "1"  
+- **Class 2:** "2"  
+- **Class 3:** "3"  
+- **Class 4:** "4"  
+- **Class 5:** "5"  
+- **Class 6:** "6"  
+- **Class 7:** "7"  
+- **Class 8:** "8"  
+- **Class 9:** "9"  
+
+---
+
+# **Run with Transformers🤗**  
+
+```python
+!pip install -q transformers torch pillow gradio
+```  
+
+```python
+import gradio as gr
+from transformers import AutoImageProcessor, SiglipForImageClassification
+from transformers.image_utils import load_image
+from PIL import Image
+import torch
+
+# Load model and processor
+model_name = "prithivMLmods/Mnist-Digits-SigLIP2"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+def classify_digit(image):
+    """Predicts the digit in the given handwritten digit 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": "0", "1": "1", "2": "2", "3": "3", "4": "4",
+        "5": "5", "6": "6", "7": "7", "8": "8", "9": "9"
+    }
+    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
+    
+    return predictions
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=classify_digit,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Prediction Scores"),
+    title="MNIST Digit Classification 🔢",
+    description="Upload a handwritten digit image (0-9) to recognize it using MNIST-Digits-SigLIP2."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+# **Sample Inference**  
+
+![Screenshot 2025-03-28 at 23-23-02 MNIST Digit Classification 🔢.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/o0YinTlr6or3V_wOJMCf3.png)
+![Screenshot 2025-03-28 at 23-25-22 MNIST Digit Classification 🔢.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/LP4upkfHfUa3wdRSSS9tp.png)
+![Screenshot 2025-03-28 at 23-25-52 MNIST Digit Classification 🔢.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/XJ0AmEg0Com-KN32jtGDu.png)
+![Screenshot 2025-03-28 at 23-26-52 MNIST Digit Classification 🔢.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/rboO-rw7BxK7S8vJMF-To.png)
+
+# **Intended Use:**  
+
+The **Mnist-Digits-SigLIP2** model is designed for handwritten digit recognition. Potential applications include:  
+
+- **Optical Character Recognition (OCR):** Digit recognition for various documents.  
+- **Banking & Finance:** Automated check processing.  
+- **Education & Learning:** AI-powered handwriting assessment.  
+- **Embedded Systems:** Handwriting input in smart devices.