Create test.py

test.py

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+import imutils
+import pickle
+from tensorflow.keras.models import load_model
+def buka(title, image):
+  image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+  plt.imshow(image)
+  plt.title(title)
+  plt.grid(False)
+  plt.axis("off")
+  plt.show()
+  
+print("loading model....")
+model = load_model('/content/bananafreshness/pisang.model')
+mlb = pickle.loads(open('/content/bananafreshness/pisang.pickle', "rb").read())
+
+# load the image
+image = cv2.imread("/content/pisang-busuk-sebagian_20171004_085005.jpg")
+output = imutils.resize(image, width=400)
+ 
+# pre-process the image for classification
+image = cv2.resize(image, (94, 94))
+image = image.astype("float") / 255.0
+image = img_to_array(image)
+image = np.expand_dims(image, axis=0)
+proba = model.predict(image)[0]
+idxs = np.argsort(proba)[::-1][:2]
+
+
+
+for (i, j) in enumerate(idxs):
+	label = "{}: {:.2f}%".format(mlb.classes_[j], proba[j] * 100)
+	cv2.putText(output, label, (10, (i * 30) + 25), 
+		cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
+ 
+
+for (label, p) in zip(mlb.classes_, proba):
+	print("{}: {:.2f}%".format(label, p * 100))
+ 
+
+buka("Output", output)