Update app.py

app.py

@@ -1,5 +1,11 @@
 import gradio as gr
 from PIL import Image
+import gradio as gr
+from gradio import components
+import tensorflow as tf
+from keras.models import load_model
+import numpy as np
+import cv2
 
 js = """
 function createGradioAnimation() {
@@ -48,15 +54,15 @@ with gr.Blocks(js=js,theme=gr.themes.Soft()) as demo:
         '''
     )
     
-    from keras.models import load_model
-    import tensorflow as tf
-
+    
     class_names = ['cataracts', 'diabetic retinopathy', 'glaucoma', 'normal']
+    model = load_model('densenet121_model.keras')
     
     def predict_input_image(img):
+        img = cv2.resize(img, (224, 224))
         img = img.reshape((-1, 224, 224, 3))
-        img = tf.keras.applications.densenet121.preprocess_input(img)
-
+        img = tf.keras.applications.densenet.preprocess_input(img)
+        
         #image_tensor = tf.convert_to_tensor(img)
         # Resize the image to 224x224.
         #image_tensor = tf.image.resize(image_tensor, (224, 224))
@@ -67,8 +73,8 @@ with gr.Blocks(js=js,theme=gr.themes.Soft()) as demo:
         # Normalize the data.
         #image_tensor = image_tensor / 255.0
         
-        model = load_model('densenet121_model.keras')
-        prediction = model.predict(img)[0]
+        
+        prediction = model.predict(img).flatten()
        
         predicted_class = {class_names[i]: float(prediction[i]) for i in range(4)}
         return predicted_class