File size: 1,486 Bytes
3ca6b64 0271264 3ca6b64 c68c1d5 3ca6b64 0271264 3ca6b64 |
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 |
import gradio as gr
from PIL import Image
import numpy as np
from tensorflow.keras.preprocessing import image as keras_image
from tensorflow.keras.applications.resnet50 import preprocess_input
from tensorflow.keras.models import load_model
# Load your trained model
model = load_model('model.h5')
def predict_alzheimer(img):
img = Image.fromarray(img.astype('uint8'), 'RGB') # Ensure the image is in RGB
img = img.resize((224, 224)) # Resize the image properly using PIL
img_array = keras_image.img_to_array(img) # Convert the image to an array
img_array = np.expand_dims(img_array, axis=0) # Expand dimensions to fit model input
img_array = preprocess_input(img_array) # Preprocess the input as expected by ResNet50
prediction = model.predict(img_array) # Predict using the model
classes = ['Mild Demented', 'Non Demented', 'Very Mild Demented' ] # Specific names
return {classes[i]: float(prediction[0][i]) for i in range(3)} # Return the prediction
# Define Gradio interface
interface = gr.Interface(fn=predict_alzheimer,
inputs="image", # Simplified input type
outputs="label", # Simplified output type
title="Alzheimer Classifier",
description="Upload an image of a CT of a brain and the classifier will predict between a mild demented, non demetend and very mild demtened alzheimer")
# Launch the interface
interface.launch()
|