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import gradio as gr
import tensorflow as tf
import numpy as np
from PIL import Image
model_path = "xception_aerial.keras"
model = tf.keras.models.load_model(model_path)
# Define the core prediction function
def predict_aerial(image):
# Preprocess image
print(type(image))
image = Image.fromarray(image.astype('uint8')) # Convert numpy array to PIL image
image = image.resize((150, 150)) # Resize the image to 150x150
image = np.array(image)
image = np.expand_dims(image, axis=0) # Expand dimensions to match the model input shape
# Predict
prediction = model.predict(image)
# Print the shape of the prediction to debug
print(f"Prediction shape: {prediction.shape}")
# Assuming the output is already softmax probabilities
probabilities = prediction[0]
# Print the probabilities array to debug
print(f"Probabilities: {probabilities}")
# Assuming your model was trained with these class names
class_names = ['agriculture', 'airport', 'beach', 'city', 'forest'] # Replace 'another_pokemon' with your third class name
# Create a dictionary of class probabilities
result = {class_names[i]: float(probabilities[i]) for i in range(len(class_names))}
return result
# Create the Gradio interface
input_image = gr.Image()
iface = gr.Interface(
fn=predict_aerial,
inputs=input_image,
outputs=gr.Label(),
examples=["aerial_examples/agriculture1.jpg", "aerial_examples/agriculture2.jpg", "aerial_examples/agriculture3.jpg", "aerial_examples/airport1.jpg", "aerial_examples/airport2.jpg", "aerial_examples/airport3.jpg", "aerial_examples/beach1.jpg", "aerial_examples/beach2.jpg", "aerial_examples/beach3.jpg", "aerial_examples/forest1.jpg", "aerial_examples/forest2.jpg", "aerial_examples/forest3.jpg",
"aerial_examples/city1.jpg", "aerial_examples/city2.jpg", "aerial_examples/city3.jpg"],
description="A simple mlp classification model for image classification using the mnist dataset.")
iface.launch() |