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# Imporiting Necessary Libraries
import tensorflow as tf
import numpy as np
from PIL import Image
# Cleanig image
def clean_image(image):
image = np.array(image)
# Resizing the image
image = np.array(Image.fromarray(
image).resize((512, 512), Image.ANTIALIAS))
# Adding batch dimensions to the image
# YOu are seeting :3, that's becuase sometimes user upload 4 channel image,
image = image[np.newaxis, :, :, :3]
# So we just take first 3 channels
return image
def get_prediction(model, image):
datagen = tf.keras.preprocessing.image.ImageDataGenerator(
rescale=1./255)
# Inputting the image to keras generators
test = datagen.flow(image)
# Predict from the image
predictions = model.predict(test)
predictions_arr = np.array(np.argmax(predictions))
return predictions, predictions_arr
# Making the final results
def make_results(predictions, predictions_arr):
result = {}
if int(predictions_arr) == 0:
result = {"status": " is Healthy ",
"prediction": f"{int(predictions[0][0].round(2)*100)}%"}
if int(predictions_arr) == 1:
result = {"status": ' has Multiple Diseases ',
"prediction": f"{int(predictions[0][1].round(2)*100)}%"}
if int(predictions_arr) == 2:
result = {"status": ' has Rust ',
"prediction": f"{int(predictions[0][2].round(2)*100)}%"}
if int(predictions_arr) == 3:
result = {"status": ' has Scab ',
"prediction": f"{int(predictions[0][3].round(2)*100)}%"}
return result
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