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app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import io
+
+# Load the model from Hugging Face
+model = tf.saved_model.load('https://huggingface.co/nivashuggingface/digit-recognition/resolve/main/saved_model')
+
+def preprocess_image(img):
+    """Preprocess the drawn image for prediction"""
+    # Convert to grayscale and resize
+    img = img.convert('L')
+    img = img.resize((28, 28))
+    # Convert to numpy array and normalize
+    img_array = np.array(img)
+    img_array = img_array.astype('float32') / 255.0
+    # Add batch dimension
+    img_array = np.expand_dims(img_array, axis=0)
+    # Add channel dimension
+    img_array = np.expand_dims(img_array, axis=-1)
+    return img_array
+
+def predict_digit(img):
+    """Predict digit from drawn image"""
+    # Preprocess the image
+    processed_img = preprocess_image(img)
+    # Make prediction
+    predictions = model(processed_img)
+    predicted_digit = tf.argmax(predictions, axis=1).numpy()[0]
+    # Get confidence scores
+    confidence_scores = tf.nn.softmax(predictions[0]).numpy()
+    # Create result string
+    result = f"Predicted Digit: {predicted_digit}\n\nConfidence Scores:\n"
+    for i, score in enumerate(confidence_scores):
+        result += f"Digit {i}: {score:.2%}\n"
+    return result
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=predict_digit,
+    inputs=gr.Image(type="pil", label="Draw a digit (0-9)"),
+    outputs=gr.Textbox(label="Prediction Results"),
+    title="Digit Recognition with CNN",
+    description="Draw a digit (0-9) in the box below. The model will predict which digit you drew.",
+    examples=[
+        ["examples/0.png"],
+        ["examples/1.png"],
+        ["examples/2.png"],
+    ],
+    theme=gr.themes.Soft()
+)
+
+# Launch the interface
+if __name__ == "__main__":
+    iface.launch()