diff --git a/glaucoma_app.py b/glaucoma_app.py
index 540cd5d..b523b57 100644
--- a/glaucoma_app.py
+++ b/glaucoma_app.py
@@ -3,44 +3,50 @@
 from PIL import Image, ImageOps
 import numpy as np
 
-st.set_option('deprecation.showfileUploaderEncoding', False)
+# Set Streamlit option to suppress deprecation warning
+# st.set_option('deprecation.showfileUploaderEncoding', False)
 
-# @st.cache(suppress_st_warning=True,allow_output_mutation=True)
+# Function to preprocess image and make predictions
 def import_and_predict(image_data, model):
-    image = ImageOps.fit(image_data, (100,100),Image.ANTIALIAS)
+    # Resize image to the required input size of the model
+    image = ImageOps.fit(image_data, (100, 100), Image.Resampling.LANCZOS)
     image = image.convert('RGB')
     image = np.asarray(image)
     st.image(image, channels='RGB')
-    image = (image.astype(np.float32) / 255.0)
-    img_reshape = image[np.newaxis,...]
+    
+    # Normalize the image
+    image = image.astype(np.float32) / 255.0
+    img_reshape = np.expand_dims(image, axis=0)
+    
+    # Prediction
     prediction = model.predict(img_reshape)
     return prediction
 
+# Load the model (ensure 'my_model2.h5' is compatible with the current TensorFlow version)
 model = tf.keras.models.load_model('my_model2.h5')
 
+# Streamlit interface
 st.write("""
-         # ***Glaucoma detector***
-         """
-         )
+         # ***Glaucoma Detector***
+         This is a simple image classification web app to predict glaucoma through fundus image of the eye.
+         """)
 
-st.write("This is a simple image classification web app to predict glaucoma through fundus image of eye")
-
-file = st.file_uploader("Please upload an image(jpg) file", type=["jpg"])
+file = st.file_uploader("Please upload an image (jpg) file", type=["jpg"])
 
+# Check if a file has been uploaded
 if file is None:
-    st.text("You haven't uploaded a jpg image file")
+    st.text("You haven't uploaded an image file.")
 else:
+    # Open image
     imageI = Image.open(file)
+    
+    # Make predictions
     prediction = import_and_predict(imageI, model)
     pred = prediction[0][0]
-    if(pred > 0.5):
-        st.write("""
-                 ## **Prediction:** You eye is Healthy. Great!!
-                 """
-                 )
+    
+    # Display results
+    if pred > 0.5:
+        st.write("## **Prediction:** Your eye appears healthy. Great!")
         st.balloons()
     else:
-        st.write("""
-                 ## **Prediction:** You are affected by Glaucoma. Please consult an ophthalmologist as soon as possible.
-                 """
-                 )
+        st.write("## **Prediction:** You may be affected by Glaucoma. Please consult an ophthalmologist.")
diff --git a/requirements.txt b/requirements.txt
index 33c0f99..b8ae026 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-tensorflow==2.3.0
+streamlit==0.81.1
+tensorflow==2.17.0
 pillow==8.3.2
 numpy==1.16.5
-streamlit==0.81.1