app.py

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()