Update app.py

app.py

@@ -3,6 +3,7 @@ import tensorflow as tf
 import cv2
 import numpy as np
 from PIL import Image, ImageOps
+import imageio.v3 as iio
 #from io import BytesIO
 
 @st.cache_resource()
@@ -19,19 +20,19 @@ st.write("""
 
 file = st.file_uploader("Upload an X-ray image", type= ['png', 'jpg'])
 
+# def model_prediction(image, model):
+#     resize = tf.image.resize(image, (256,256))
+#     yhat = model.predict(np.expand_dims(resize/255, 0))
+#     if(yhat>0.5):
+#         result = "Prediction is loose"
+#     else:
+#         result = "Prediction is control"
+#     return result
 
 def model_prediction(image, model):
-    resize = tf.image.resize(image, (256,256))
-    yhat = model.predict(np.expand_dims(resize/255, 0))
-    if(yhat>0.5):
-        result = "Prediction is loose"
-    else:
-        result = "Prediction is control"
-    return result
-
-def model_prediction(image, model):
-    resize = tf.image.resize(image, (256,256))
-    yhat = model.predict(np.expand_dims(resize/255, 0))
+    
+    item = cv2.resize(image,dsize=(256,256), interpolation=cv2.INTER_CUBIC)
+    yhat = model.predict(np.expand_dims(item/255, 0))
     if(yhat>0.5):
         result = "Prediction is loose"
     else:
@@ -45,7 +46,8 @@ def on_click():
     else:
         image = Image.open(file)
         st.image(image, use_column_width=True)
-        predictions = model_prediction(image, model)
+        img = iio.imread(file)
+        predictions = model_prediction(img, model)
         st.write(prediction)
         print(
             "This image most likely belongs to {}."