Add application file

app.py

@@ -0,0 +1,59 @@
+import streamlit as st
+import tensorflow as tf
+import streamlit as st
+import cv2
+from PIL import Image, ImageOps
+import numpy as np
+
+
+@st.cache(allow_output_mutation=True)
+
+def load_model():
+  model=tf.keras.models.load_model('/Users/muhammadhugo/Documents/Skripsi/Project-porto/MachineLearning-ClearVis-main-2/Saved_model/cnnsvm_retinoblastoma_model.h5')
+  return model
+
+def predict(image_data, model):
+    
+        size = (224,224)    
+        image = ImageOps.fit(image_data, size, Image.ANTIALIAS)
+        image = np.asarray(image)
+        img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        #img_resize = (cv2.resize(img, dsize=(75, 75),    interpolation=cv2.INTER_CUBIC))/255.
+        
+        img_reshape = img[np.newaxis,...]
+    
+        prediction = model.predict(img_reshape)
+        
+        return prediction
+
+
+with st.spinner('Model is being loaded..'):
+  model=load_model()
+
+st.write("""
+         # Retinoblastoma Classification App
+         """
+         )
+
+file = st.file_uploader("Please upload a cropped eye image file", type=["jpg", "png", "jpeg"])
+
+st.set_option('deprecation.showfileUploaderEncoding', False)
+
+if file is None:
+    st.text("Please upload an image file")
+
+else:
+    image = Image.open(file)
+    st.image(image, use_column_width=True)
+    predictions = predict(image, model)
+
+    if round(float(predictions[0][0])) == 1:
+        results = 'normal'
+        print("user's eyes is normal")
+        print(predictions)
+        st.write("user's eyes is normal")
+    elif round(float(predictions[0][1])) == 1:
+        results = 'retinoblastoma'
+        print("user's eyes is retinoblastoma")
+        print(predictions)
+        st.write("user's eyes is suspected with retinoblastoma")