Create app.py

app.py

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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)