initial commit

app.py

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+import streamlit as st
+import tensorflow as tf
+from io import BytesIO
+import numpy as np
+import cv2
+import base64
+
+def set_page_background(png_file):
+    @st.cache_data(show_spinner=False)
+    def get_base64_of_bin_file(bin_file):
+        with open(bin_file, 'rb') as f:
+            data = f.read()
+        return base64.b64encode(data).decode()
+
+    bin_str = get_base64_of_bin_file(png_file)
+    custom_css = f'''
+        <style>
+            .stApp {{
+                background-image: url("data:image/png;base64,{bin_str}");
+                background-size: cover;
+                background-repeat: no-repeat;
+                background-attachment: scroll;
+            }}
+            
+            #MainMenu {{visibility: hidden;}}
+            footer {{visibility: hidden;}}
+            icon {{color: white;}}
+            nav-link {{--hover-color: grey; }}
+            nav-link-selected {{background-color: #4ABF7E;}}
+        </style>
+    '''
+    st.markdown(custom_css, unsafe_allow_html=True)
+
+set_page_background("./technology-network-background-connection-cyber-space-ai-generative.jpg")
+st.title("Stages of Alzheimer's Disease Prediction")
+
+st.markdown("[Dataset Source](https://www.kaggle.com/datasets/tourist55/alzheimers-dataset-4-class-of-images)")
+
+model = tf.keras.models.load_model('./model/model_1.h5')
+model.load_weights('./model/best_model_custom_1.h5')
+
+uploaded_file = st.file_uploader("Upload a brain MRI image here", type=["jpg", "png", "jpeg"], label_visibility="hidden")
+predict_button = st.button("ㅤㅤPredictㅤㅤ")
+
+if uploaded_file is not None:
+    file_bytes = BytesIO(uploaded_file.read())
+    st.image(file_bytes,use_column_width=True,clamp = True) 
+    
+    img = cv2.imdecode(np.frombuffer(file_bytes.read(), np.uint8), 0)
+    #img=np.array(file_bytes)   
+  
+    if len(img.shape) == 2:
+        img=cv2.cvtColor(img,cv2.COLOR_GRAY2RGB)
+    
+    img=cv2.resize(img,(176, 176))
+    if img.max() > 1:
+        img = img / 255.0
+    
+    img = np.expand_dims(img, axis=0)
+    pred=model.predict(img)
+    predict_val = np.argmax(pred, axis=1)
+   
+    if predict_val == 0:
+        probability = pred[predict_val]
+        st.write(f"Mildly Demented with prediction probability of {probability}") 
+                 
+    elif predict_val == 1:
+        probability = pred[predict_val]
+        st.write(f"Moderately Demented with prediction probability of {probability}") 
+                 
+    elif predict_val == 2:
+        probability = pred[predict_val]
+        st.write(f"Not Demented with prediction probability of {probability}") 
+                
+    elif predict_val == 3:
+        probability = pred[predict_val]
+        st.write(f"Very Mildly Demented with prediction probability of {probability}") 
+                
+    else:
+       st.write("Error!")
+
+
+

requirements.txt

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+numpy==1.25.2
+tensorflow
+keras
+opencv-python-headless==4.5.4.60
+streamlit==1.26.0
+streamlit_option_menu==0.3.6