Update app.py

app.py

@@ -1,30 +1,30 @@
-import streamlit as st
-from tensorflow.keras.models import load_model
-from PIL import Image
-import numpy as np
-
-model = load_model('skin_canser_cnn_model.h5')
-
-
-def process_image(img):
-    img= img.resize((170,170))
-    img = np.array(img)
-    img = img/255.0 # normalize
-    img = np.expand_dims(img,axis=0)
-    return img
-
-
-st.title('Skin Canser Classification :cancer:')
-st.write('Upload Test Image')
-
-
-file = st.file_uploader('Enter Image', type=['jpg','png','jpeg'])
-
-if file is not None:
-    img=Image.open(file)
-    st.image(img,caption='Uploaded Image')
-    image = process_image(img)
-    prediction = model.predict(image)
-    predicted_class = np.argmax(prediction)
-    class_names = ['Canser !','Not Canser !']
+import streamlit as st
+from tensorflow.keras.models import load_model
+from PIL import Image
+import numpy as np
+
+model = load_model('skin_cancer_cnn_model.h5')
+
+
+def process_image(img):
+    img= img.resize((170,170))
+    img = np.array(img)
+    img = img/255.0 # normalize
+    img = np.expand_dims(img,axis=0)
+    return img
+
+
+st.title('Skin Cancer Classification :cancer:')
+st.write('Upload Test Image')
+
+
+file = st.file_uploader('Enter Image', type=['jpg','png','jpeg'])
+
+if file is not None:
+    img=Image.open(file)
+    st.image(img,caption='Uploaded Image')
+    image = process_image(img)
+    prediction = model.predict(image)
+    predicted_class = np.argmax(prediction)
+    class_names = ['Cancer !','Not Cancer !']
     st.write(class_names[predicted_class])