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import gradio as gr
import tensorflow as tf
print(tf.__version__)
import numpy as np
from PIL import Image
import os
model_path = "apple_model_transferlearning.keras"
model = tf.keras.models.load_model(model_path)
def predict_apple(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 pixels
image = np.array(image)
image = np.expand_dims(image, axis=0) # Add batch dimension
# Predict
prediction = model.predict(image)
# Convert the probabilities to rounded values
prediction = np.round(prediction, 2)
# Make sure the indices are correct according to your model's training
p_schorf = prediction[0][0] # Probability for "Schorf"
p_schwarzfaeule = prediction[0][1] # Probability for "Schwarzfaeule"
p_zederapfel = prediction[0][2] # Probability for "Zederapfel"
p_gesund = prediction[0][3] # Probability for "Gesund"
return {'Gesund': p_gesund, 'Schorf': p_schorf, 'Schwarzfaeule': p_schwarzfaeule, 'Zederapfel': p_zederapfel}
# Create the Gradio interface
input_image = gr.Image()
iface = gr.Interface(
fn=predict_apple,
inputs=input_image,
outputs=gr.Label(),
examples=["images/Gesund1.JPG",
"images/Gesund2.JPG",
"images/Gesund3.JPG",
"images/Schorf1.JPG",
"images/Schorf2.JPG",
"images/Schorf3.JPG",
"images/Schwarzfaeule1.JPG",
"images/Schwarzfaeule2.JPG",
"images/Schwarzfaeule3.JPG",
"images/Zederapfel1.JPG",
"images/Zederapfel2.JPG",
"images/Zederapfel3.JPG"],
description="Applikation zur Überwachung der Gesundheit von Apfelbäumen")
iface.launch()
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