mnist-model

app.py

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+import tensorflow as tf
+import gradio as gr
+import numpy as np
+from PIL import Image, ImageOps
+from huggingface_hub import hf_hub_download
+
+model_path = hf_hub_download(repo_id="ishaqezaz/mnist-cnn", filename="mnist_v4.keras")
+model = tf.keras.models.load_model(model_path)
+
+def preprocess_image(img):
+    if isinstance(img, np.ndarray):
+        img = Image.fromarray(img)
+
+    img = img.convert("L")
+    img = ImageOps.invert(img)
+    img = img.resize((28, 28))
+
+    img = np.array(img) / 255.0
+    img = img.reshape(1, 28, 28, 1)
+    return img
+
+def predict(img):
+    img = preprocess_image(img)
+    prediction = model.predict(img)
+    predicted_digit = np.argmax(prediction)
+    return f"Predicted Digit: {predicted_digit}"
+
+interface = gr.Interface(fn=predict, inputs="image", outputs="text")
+interface.launch(share=True)

requirements.txt

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+tensorflow
+gradio
+pillow
+numpy
+huggingface_hub