Update app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# Load your model
+model = tf.keras.models.load_model("ElYazisiRakamlariTahmin.h5")
+
+def preprocess_image(image):
+    # Implement your image preprocessing here
+    # This is just a placeholder example
+    image = image.convert("L")  # Convert to grayscale
+    image = image.resize((28, 28))  # Resize to match your model's input size
+    image = np.array(image) / 255.0  # Normalize
+    return image.reshape(1, 28, 28, 1)  # Reshape for model input
+
+def predict_digit(image):
+    preprocessed = preprocess_image(image)
+    prediction = model.predict(preprocessed)
+    digit = np.argmax(prediction)
+    confidence = np.max(prediction)
+    return f"Predicted Digit: {digit}, Confidence: {confidence:.2f}"
+
+iface = gr.Interface(
+    fn=predict_digit,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Handwritten Digit Recognition",
+    description="Upload an image of a handwritten digit (0-9) to get a prediction."
+)
+
+iface.launch()