Create mnist_digits.py

mnist_digits.py

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+import gradio as gr
+import spaces
+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)
+
+@spaces.GPU
+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()