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import gradio as gr |
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import tensorflow as tf |
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import cv2 |
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import matplotlib.pyplot as plt |
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import numpy as np |
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model = tf.keras.models.load_model('plant_model_v5-beta.h5') |
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class_names = { |
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0: 'Apple___Apple_scab', |
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1: 'Apple___Black_rot', |
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2: 'Apple___Cedar_apple_rust', |
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3: 'Apple___healthy', |
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4: 'Not a plant', |
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5: 'Blueberry___healthy', |
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6: 'Cherry___Powdery_mildew', |
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7: 'Cherry___healthy', |
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8: 'Corn___Cercospora_leaf_spot Gray_leaf_spot', |
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9: 'Corn___Common_rust', |
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10: 'Corn___Northern_Leaf_Blight', |
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11: 'Corn___healthy', |
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12: 'Grape___Black_rot', |
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13: 'Grape___Esca_(Black_Measles)', |
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14: 'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)', |
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15: 'Grape___healthy', |
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16: 'Orange___Haunglongbing_(Citrus_greening)', |
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17: 'Peach___Bacterial_spot', |
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18: 'Peach___healthy', |
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19: 'Pepper,_bell___Bacterial_spot', |
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20: 'Pepper,_bell___healthy', |
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21: 'Potato___Early_blight', |
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22: 'Potato___Late_blight', |
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23: 'Potato___healthy', |
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24: 'Raspberry___healthy', |
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25: 'Soybean___healthy', |
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26: 'Squash___Powdery_mildew', |
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27: 'Strawberry___Leaf_scorch', |
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28: 'Strawberry___healthy', |
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29: 'Tomato___Bacterial_spot', |
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30: 'Tomato___Early_blight', |
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31: 'Tomato___Late_blight', |
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32: 'Tomato___Leaf_Mold', |
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33: 'Tomato___Septoria_leaf_spot', |
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34: 'Tomato___Spider_mites Two-spotted_spider_mite', |
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35: 'Tomato___Target_Spot', |
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36: 'Tomato___Tomato_Yellow_Leaf_Curl_Virus', |
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37: 'Tomato___Tomato_mosaic_virus', |
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38: 'Tomato___healthy' |
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} |
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def edge_and_cut(img, threshold1, threshold2): |
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emb_img = img.copy() |
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edges = cv2.Canny(img, threshold1, threshold2) |
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edge_coors = [] |
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for i in range(edges.shape[0]): |
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for j in range(edges.shape[1]): |
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if edges[i][j] != 0: |
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edge_coors.append((i, j)) |
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if len(edge_coors) == 0: |
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return emb_img |
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row_min = edge_coors[np.argsort([coor[0] for coor in edge_coors])[0]][0] |
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row_max = edge_coors[np.argsort([coor[0] for coor in edge_coors])[-1]][0] |
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col_min = edge_coors[np.argsort([coor[1] for coor in edge_coors])[0]][1] |
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col_max = edge_coors[np.argsort([coor[1] for coor in edge_coors])[-1]][1] |
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new_img = img[row_min:row_max, col_min:col_max] |
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emb_color = np.array([255], dtype=np.uint8) |
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emb_img[row_min-10:row_min+10, col_min:col_max] = emb_color |
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emb_img[row_max-10:row_max+10, col_min:col_max] = emb_color |
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emb_img[row_min:row_max, col_min-10:col_min+10] = emb_color |
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emb_img[row_min:row_max, col_max-10:col_max+10] = emb_color |
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return emb_img |
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def classify_and_visualize(image): |
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img_array = tf.image.resize(image, [256, 256]) |
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img_array = tf.expand_dims(img_array, 0) / 255.0 |
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prediction = model.predict(img_array) |
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predicted_class = tf.argmax(prediction[0], axis=-1) |
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confidence = np.max(prediction[0]) |
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if confidence < 0.60: |
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class_name = "The image you uploaded might not be in the dataset. Try making your leaf background white." |
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bounded_image = image |
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else: |
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class_name = class_names[predicted_class.numpy()] |
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bounded_image = edge_and_cut(image, 200, 400) |
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return class_name, confidence, bounded_image |
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iface = gr.Interface( |
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fn=classify_and_visualize, |
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inputs="image", |
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outputs=["text", "number", "image"], |
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interpretation="default", |
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examples=[ |
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['examples/grot.jpg'], |
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['examples/corn.jpg'], |
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]) |
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iface.launch() |
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