Added files to hf space

app.py

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+import gradio as gr
+import numpy as np
+import cv2
+from PIL import Image
+import tensorflow as tf
+
+# Load the trained model
+model = tf.keras.models.load_model('mnist_model.h5')
+
+def cnn_predict_digit(image):
+    # Handle Gradio Sketchpad dictionary input
+    if isinstance(image, dict) and 'composite' in image:
+        image = image['composite']
+    
+    # Convert to grayscale if RGB
+    if image.ndim == 3 and image.shape[2] == 3:
+        image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+    
+    # Invert colors (white background → black background)
+    image = 255 - image
+    
+    # Resize to 28x28
+    image = cv2.resize(image, (28, 28))
+    
+    # Normalize and reshape
+    image = image.astype('float32') / 255.0
+    image = image.reshape(1, 28, 28, 1)
+    
+    # Predict
+    prediction = model.predict(image)
+    pred_label = np.argmax(prediction, axis=1)[0]
+    
+    return str(pred_label)
+
+with gr.Blocks() as interface:
+    gr.Markdown(
+        """
+        ## ✍️ Digit Classification using Convolutional Neural Network
+        Draw a digit in the sketchpad below (0 to 9), then click **Submit** to see the prediction.
+        """
+    )
+    with gr.Row():
+        sketchpad = gr.Sketchpad(image_mode='L')
+        output = gr.Label()
+    gr.Button("Submit").click(cnn_predict_digit, inputs=sketchpad, outputs=output)
+    gr.ClearButton([sketchpad, output])
+
+interface.launch()

mnist_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2c435ee80c60965b62fc5bd5b47fb5ede1ea23ada102062cc46237904c67eb41
+size 1168216

requirements.txt

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+tensorflow
+gradio
+opencv-python
+numpy
+Pillow