Upload test.py

test.py

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+import sys
+import numpy as np
+from PIL import Image
+from numpy import asarray
+import tensorflow as tf
+from tensorflow import keras
+
+from tensorflow.keras.preprocessing import image_dataset_from_directory
+
+
+input = sys.argv[1]
+
+
+
+path = r"archive/Train/Train"
+train = image_dataset_from_directory(path, batch_size=32,
+                                    image_size=(256,256),shuffle=True)
+
+#pathx=r"C:\Projects\Junk\model.keras"
+
+class_labels = train.class_names
+
+#model = keras.models.load_model("model.h5")
+model = keras.models.load_model('model.h5', custom_objects=None, compile=True, safe_mode=True)
+#print(model.summary())
+
+#imgs = Image.open('basil.jpg')
+
+def calling(img_path):
+    imgs = Image.open('basil.jpg')
+    predicted_class, confidence = Prediction(model, asarray(imgs))
+    return predicted_class
+
+
+#print('hello')
+def Prediction(model, img):
+    img_array = tf.keras.preprocessing.image.img_to_array((img))
+    img_array = tf.expand_dims(img_array, 0)  # create a batch
+
+    predictions = model.predict(img_array)
+
+    predicted_class = class_labels[np.argmax(predictions[0])]
+    confidence = round(100 * (np.max(predictions[0])), 2)
+
+    return predicted_class, confidence
+    #return predicted_class
+    #return predictions
+
+op = calling('basil.jpg')
+print(op)
+
+sys.stdout.flush()
+#predicted_class , confidence = Prediction(model,asarray(imgs))
+#pred = Prediction(model,asarray(imgs))
+#print(predicted_class)
+
+
+
+#print('hello2')