Added all dependencies

app.py

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+from tensorflow.keras.models import load_model
+import streamlit as st
+import cv2
+import numpy as np
+from PIL import Image
+# Load the ensemble model using tf.keras.models.load_model()
+loaded_ensemble_model = load_model('ensemble_model.h5')
+
+st.markdown('<h1 style="color:red;">Ensemble Image classification model for Alzheimer</h1>', unsafe_allow_html=True)
+st.markdown('<h2 style="color:gray;">The image classification model classifies brain scan image into following categories:</h2>', unsafe_allow_html=True)
+st.markdown('<h3 style="color:gray;"> Moderate,Mild,Very Mild, NonDemented</h3>', unsafe_allow_html=True)
+
+upload= st.file_uploader('Insert image for classification', type=['png','jpg'])
+c1, c2= st.columns(2)
+if upload is not None:
+    im= Image.open(upload)
+    im = im.convert('RGB')
+    img= np.asarray(im)
+    image= cv2.resize(img,(150, 150))
+    img_array = image.reshape(1,150,150,3)
+    c1.header('Input Image')
+    c1.image(im)
+
+    loaded_ensemble_model = load_model('ensemble_model.h5')
+    pred = loaded_ensemble_model.predict([img_array, img_array])
+    labels = {0:'MildDemented',1:'ModerateDemented',2:'NonDemented',3:'VeryMildDemented'}
+    output_array = pred
+    normalized_array = output_array / np.sum(output_array)
+    percentage_array = normalized_array * 100
+    c2.header('Output')
+    c2.subheader('Predicted class :')
+    c2.write(labels[pred.argmax()])
+    c2.subheader('With :')
+    c2.write(f'{int(np.max(percentage_array))}% assurity')

ensemble_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b63610a2561116e46a83ad420065ee8e5c679d59b49ddb28e82b1cdf293174dc
+size 193319800

requirements.txt

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+tensorflow
+opencv-python