Spaces:
Running
Running
File size: 1,066 Bytes
d70585e 044b146 d70585e 044b146 d70585e 044b146 d70585e 3a813cf d70585e c2840e0 8d28437 3a813cf d70585e 8d28437 d70585e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 |
import gradio as gr
import tensorflow as tf
import numpy as np
from PIL import Image
# Load the trained model
model = tf.keras.models.load_model("denis_mnist_cnn_model.h5")
# Preprocessing function for images
def preprocess_image(image):
# Resize the image to 28x28 as expected by the model
image = np.array(image)
image = tf.image.resize(image, (28, 28)) # Resize to 28x28
image = tf.image.grayscale_to_rgb(image) # Convert grayscale to RGB (3 channels)
image = image / 255.0 # Normalize pixel values to [0, 1]
image = np.expand_dims(image, axis=0) # Add batch dimension
return image
# Function to make predictions
def predict(image):
image = preprocess_image(image)
prediction = model.predict(image) # Predict
predicted_class = np.argmax(prediction, axis=-1)[0] # Get the predicted class
return {"prediction": int(predicted_class)}
# Create a Gradio interface
interface = gr.Interface(fn=predict, inputs="image", outputs="json")
# Launch the Gradio interface
if __name__ == "__main__":
interface.launch()
|