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import numpy as np
import gradio as gr
import tensorflow as tf #version 2.13.0
import keras #version
import numpy as np
import cv2
import tensorflow as tf
import h5py
def sepia(img):
label_disease = {
0 : 'Apple___Apple_scab',
1 : 'Apple___Black_rot',
2 : 'Apple___Cedar_apple_rust',
3 : 'Apple___healthy',
4 : 'Background_without_leaves',
5 : 'Blueberry___healthy',
6 : 'Cherry___Powdery_mildew',
7 : 'Cherry___healthy',
8 : 'Corn___Cercospora_leaf_spot_Gray_leaf_spot',
9 : 'Corn___Common_rust',
10: 'Corn___Northern_Leaf_Blight',
11: 'Corn___healthy',
12: 'Grape___Black_rot',
13: 'Grape___Esca_(Black_Measles)',
14: 'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)',
15: 'Grape___healthy',
16: 'Orange___Haunglongbing_Citrus_greening',
17: 'Peach___Bacterial_spot',
18: 'Peach___healthy',
19: 'Pepper_bell___Bacterial_spot',
20: 'Pepper_bell___healthy',
21: 'Potato___Early_blight',
22: 'Potato___Late_blight',
23: 'Potato___healthy',
24: 'Raspberry___healthy',
25: 'Soybean___healthy',
26: 'Squash___Powdery_mildew',
27: 'Strawberry___Leaf_scorch',
28: 'Strawberry___healthy',
29: 'Tomato___Bacterial_spot',
30: 'Tomato___Early_blight',
31: 'Tomato___Late_blight',
32: 'Tomato___Leaf_Mold',
33: 'Tomato___Septoria_leaf_spot',
34: 'Tomato___Spider_mites_Two-,spotted_spider_mite',
35: 'Tomato___Target_Spot',
36: 'Tomato___Tomato_Yellow_Leaf_Curl_Virus',
37: 'Tomato___Tomato_mosaic_virus',
38: 'Tomato___healthy',
}
plant_label_disease={
"apple":[0,1,2,3],
"background_without_leaves":[4],
"blueberry" : [5],
"cherry" : [6,7],
"corn" : [8,9,10,11],
"grape" : [12,13,14,15],
"orange" : [16] ,
"peach" : [17,18],
"pepper" : [19,20],
"potato" : [21,22,23],
"raspberry" : [24],
"soybean" : [25],
"squash" : [26],
"strawberry" : [27,28],
"tomato" : [29,30,31,32,33,34,35,36,37,38]
}
HEIGHT = 256
WIDTH = 256
dnn_model = keras.models.load_model('untrained_model.h5',compile=False)
weights_path = 'keras_savedmodel_weights.h5'
dnn_model.load_weights(weights_path)
# dnn_model = tf.saved_model.load(model_path)
process_img = cv2.resize(img, (HEIGHT, WIDTH),interpolation = cv2.INTER_LINEAR)
process_img = process_img/(255)
process_img = np.expand_dims(process_img, axis=0)
y_pred = dnn_model.predict(process_img)
print("y pred",y_pred)
indx = np.argmax(y_pred)
print(label_disease[indx])
return label_disease[indx]
demo = gr.Interface(sepia, gr.Image(), "text")
demo.launch(share=True)
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