File size: 1,773 Bytes
7b9bad7 9ef0568 7b9bad7 9ef0568 c4d58dc 7b9bad7 9ef0568 7b9bad7 9ef0568 7b9bad7 9ef0568 4c7b492 9ef0568 249e99c 4c7b492 7b9bad7 9ef0568 7b9bad7 9ef0568 7b9bad7 9ef0568 | 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 | import gradio as gr
from tensorflow.keras.models import load_model
from PIL import Image
import numpy as np
# Load the model
model = load_model("shap efficient B0 multi.h5")
# Optionally: Recompile the model to avoid the warning (if needed for evaluation)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
# Preprocessing function
def preprocess_image(image):
# Resize image to the model's input size
image = image.resize((400, 400)) # Adjust based on your model's input size
# Normalize the image (if required by your model)
image = np.array(image) / 255.0 # Normalize pixel values to [0, 1]
return np.expand_dims(image, axis=0) # Add batch dimension
# Prediction function
def predict(image):
# Preprocess the input image
preprocessed = preprocess_image(image)
# Make predictions
predictions = model.predict(preprocessed)
# Define the class labels (you can update these with your actual class names)
class_labels = ['No DR', 'Mild', 'Moderate', 'Severe', 'Proliferative DR']
# Create a dictionary with class labels as keys and probabilities as values
prediction_dict = {class_labels[i]: float(predictions[0][i]) for i in range(len(class_labels))}
# Get the predicted class with the highest probability
predicted_class = class_labels[np.argmax(predictions, axis=-1)[0]]
return predicted_class, prediction_dict
# Gradio interface
interface = gr.Interface(
fn=predict, # The function to call for predictions
inputs=gr.Image(type="pil"), # The input type: image
outputs=[gr.Text(), gr.Label()] # The output type: predicted class and probability
)
# Launch the Gradio app
interface.launch(share=True) # Share the app with a public link
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