Create app.py

app.py

@@ -0,0 +1,47 @@
+import streamlit as st
+import numpy as np
+from keras.preprocessing import image
+from keras.models import load_model
+
+
+
+model = load_model('braintumordetectmodel.h5')
+
+
+
+st.title('Classification of Brain Tumor using CNN')
+
+st.text("")
+
+upload_brain_photo = st.file_uploader('Please upload the photo of Brain MRI Image', type=['jpg', 'png'])
+
+
+if upload_brain_photo is not None:
+
+    brain_photo_uploaded = image.load_img(upload_brain_photo, target_size=(180, 180, 3))
+
+    st.text("")
+
+    col1, col2, col3 = st.columns (3)
+
+    with col1:
+        st.write (' ')
+    with col2:
+        st.image (brain_photo_uploaded, caption='Preview of the uploaded Brain MRI Image', width=250)
+    with col3:
+        st.write (' ')
+
+    brain_photo_uploaded_to_arr = image.img_to_array(brain_photo_uploaded)
+
+    brain_photo_uploaded_to_arr = brain_photo_uploaded_to_arr / 255
+
+    brain_photo_uploaded_to_arr_expand = np.expand_dims(brain_photo_uploaded_to_arr, axis=0)
+
+    prediction = (model.predict(brain_photo_uploaded_to_arr_expand) > 0.5).astype('int32')
+
+    st.text("")
+
+    if prediction[0][0] == 0:
+        st.markdown("<div style='background-color: green; padding: 8px; border-radius: 10px; text-align: center; color: white; font-size: large'>The Brain MRI imaging that you uploaded shows no signs of any tumor.</div>", unsafe_allow_html=True)
+    else:
+        st.markdown("<div style='background-color: red; padding: 8px; border-radius: 10px; text-align: center; color: white; font-size: large'> The brain imaging that you uploaded indeed has signs of a tumor.</div>", unsafe_allow_html=True)