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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) | |