Update app.py

app.py

@@ -5,14 +5,19 @@ import matplotlib.pyplot as plt
 from PIL import Image
 import pickle
 from tensorflow.keras.models import load_model
+import os
+
+os.system('wget -O model.h5 "https://drive.google.com/file/d/1UqXYR2e3c0VW8Ax4nYLvef7Vmlx3AEGi/view?usp=share_link')
+os.system('wget -O model2.pkl "https://drive.google.com/file/d/16FuBwWEhG7oHH2YGm8EkEDTbv6GSUbyg/view?usp=share_link')
 
 # Load the RGB to hyperspectral conversion model
-converion_model = load_model('/kaggle/input/convmo/Conversion_model.h5')
+#converion_model = load_model('/kaggle/input/convmo/Conversion_model.h5')
+converion_model = tf.keras.models.load_model('model.h5')
 
 # Load the cancer classification model
 #cancer_model = pickle.load(open("/kaggle/input/classi/ClassRF (1).pkl", "rb"))
 
-cancer_model = pickle.load(open("/kaggle/input/logistic/LRclass.pkl", "rb"))
+cancer_model = pickle.load(open("model2.pkl", "rb"))
 
 def classify(rgb_image):
     img = Image.fromarray(rgb_image.astype('uint8'), 'RGB')