File size: 2,218 Bytes
355b229 |
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 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 |
from keras.models import load_model
from keras.preprocessing import image
import numpy as np
import os
import gradio as gr
loaded_model = load_model('diabetic_retinopathy_model.h5')
import gradio as gr
import numpy as np
from tensorflow.keras.preprocessing import image
# Class mapping
class_mapping = {
0: 'No DR',
1: 'Mild',
2: 'Moderate',
3: 'Severe',
4: 'Proliferative DR'
}
# URL of the fixed example image to display
example_image_url = "1.jpg" # Replace with the actual URL
def predict_diabetic_retinopathy(test_image, loaded_model, height=512, width=512):
# Always return the example image
try:
if test_image is None:
return "No image uploaded. Please upload an image.", example_image_url
# Ensure the image is in the correct format
img = image.img_to_array(test_image)
# Resize the image while maintaining the aspect ratio
img = np.array(image.smart_resize(img, (height, width)))
img_array = np.expand_dims(img, axis=0)
img_array /= 255.0 # Normalize the image array
# Make predictions
predictions = loaded_model.predict(img_array)
# Convert predictions to the corresponding class
predicted_class = np.argmax(predictions)
# Return the predicted class and the example image URL
return f"**Predicted Diabetic Retinopathy Stage:** {class_mapping[predicted_class]}", example_image_url
except Exception as e:
return f"An error occurred: {str(e)}", example_image_url
# Create the Gradio interface with fixed example images
example_images = [
"No_DR.png",
"Mild.png",
"Moderate.png",
"Proliferate_DR.png"
]
iface = gr.Interface(
fn=lambda img: predict_diabetic_retinopathy(img, loaded_model),
inputs=gr.Image(type="numpy", label="Upload Retina Image"),
outputs=[gr.Markdown(label="Prediction Result"), gr.Image(value=example_image_url, label="Example Image")],
title="Diabetic Retinopathy Prediction",
description="Upload an image of the retina to predict the stage of diabetic retinopathy.",
theme="default",
examples=example_images
)
# Launch the interface
iface.launch() |