Update app.py

app.py

@@ -1,3 +1,51 @@
+import gradio as gr
+import PIL import Image
+
+js = """
+function createGradioAnimation() {
+    var container = document.createElement('div');
+    container.id = 'gradio-animation';
+    container.style.fontSize = '2em';
+    container.style.fontWeight = 'bold';
+    container.style.textAlign = 'center';
+    container.style.marginBottom = '20px';
+
+    var text = 'Stress Prediction Model';
+    for (var i = 0; i < text.length; i++) {
+        (function(i){
+            setTimeout(function(){
+                var letter = document.createElement('span');
+                letter.style.opacity = '0';
+                letter.style.transition = 'opacity 0.5s';
+                letter.innerText = text[i];
+
+                container.appendChild(letter);
+
+                setTimeout(function() {
+                    letter.style.opacity = '1';
+                }, 50);
+            }, i * 250);
+        })(i);
+    }
+
+    var gradioContainer = document.querySelector('.gradio-container');
+    gradioContainer.insertBefore(container, gradioContainer.firstChild);
+
+    return 'Animation created';
+}
+"""
+
+background = Image.open('quote.jpg')
+with gr.Blocks(js=js,theme=gr.themes.Soft()) as demo:
+    gr.Image(background, height = '400px',interactive = False)
+    gr.Markdown(
+        '''
+        Eye disease could lead to vision impairment and complete blindness that affect one's ability in daily life. 
+        Early detection and timely treatment are crucial to prevent the worsening of eye disease. 
+        A Deep Learning Model are potential to assist ophthalmologists in giving first screening of suspected eye disease through retinal fundus images.
+        '''
+    )
+
 class_names = ['cataracts', 'diabetic retinopathy', 'glaucoma', 'normal']
 
 def predict_input_image(img):
@@ -11,7 +59,7 @@ def predict_input_image(img):
     # Add a batch dimension.
     image_tensor = tf.expand_dims(image_tensor, 0)
     # Normalize the data.
-    image_tensor = image_tensor / 255.0
+    image_tensor = image_tensor #/ 255.0
 
     from keras.models import load_model
     model = load_model('vgg16_model.keras')
@@ -20,11 +68,9 @@ def predict_input_image(img):
     
     return {class_names[i]: float(prediction[i]) for i in range(4)}
 
-import gradio as gr
-
 title=(
  '''<style>h1 {text-align: center;}</style>
-     <h1> A Eye: Eye Disease Classifier </h1>'''
+     <h1> A-Eye: Eye Disease Classifier </h1>'''
 )
                 
 demo = gr.Interface(fn = predict_input_image,