Update app.py

app.py

@@ -7,70 +7,22 @@ import cv2
 import tensorflow as tf
 import h5py
 def analyse(img,plant_type):
-    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]
-    }
+    import json
+
+    # Load label_disease.json
+    with open('data/label_disease.json', 'r') as f:
+        label_disease = json.load(f)
+
+    # Load plant_label_disease.json
+    with open('data/plant_label_disease.json', 'r') as f:
+        plant_label_disease = json.load(f)
+
     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)
+    modelArchitecturePath ='model/model_architecture.h5'
+    modelWeightsPath = 'model/model_weights.h5'
+    dnn_model = keras.models.load_model(modelArchitecturePath,compile=False)
+    dnn_model.load_weights(modelWeightsPath)
     
     process_img = cv2.resize(img, (HEIGHT, WIDTH),interpolation = cv2.INTER_LINEAR)
     process_img = process_img/(255)