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import tensorflow as tf
import gradio as gr
import numpy as npd
from PIL import Image
loaded_model = tf.keras.models.load_model('digit_recognition_model.h5')
def recognize_digit(image):
# Preprocess the input image
image = Image.fromarray(image).convert('L') # Convert to grayscale
image = image.resize((28, 28)) # Resize to 28x28
image = np.array(image)
image = image / 255.0 # Normalize the pixel values
# Make a prediction using the loaded model
image = np.expand_dims(image, axis=0) # Add a batch dimension
prediction = loaded_model.predict(image)
predicted_digit = np.argmax(prediction)
return str(predicted_digit)
iface = gr.Interface(fn=recognize_digit, inputs="image", outputs="text")
iface.launch(share = True)
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