First Push

.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model_tuned.hdf5 filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,41 @@
+# Import library
+import streamlit as st
+import numpy as np
+from tensorflow import keras
+from PIL import Image
+
+
+# Load model
+model = keras.models.load_model('model_tuned.hdf5')
+
+st.header('Lung X-Ray Prediction')
+st.write('Please Upload Your X-Ray Image')
+
+# Upload Image
+uploaded_file = st.file_uploader("", type=["jpg", "jpeg", "png"])
+
+# Show Uploaded Image
+if uploaded_file is not None:
+    image = Image.open(uploaded_file).convert('RGB')
+    st.subheader('This Is Your X-Ray Image')
+    st.image(image, width=200)
+
+    st.write('Push This Button To Predict')
+    if st.button('Predict'):
+
+        # resize uploaded image
+        inf = image.resize((224,224))
+        # make into array
+        inf = np.asarray(inf)
+        # expands dims to tensor
+        inf = np.expand_dims(inf, axis = 0)
+
+        # define label
+        label = ['Covid', 'Normal', 'Pneumonia']
+
+        # Make the prediction
+        prediction = model.predict(inf)
+        prediction = label[np.argmax(prediction)]
+    
+        st.subheader(f"This X-Ray Images Has {prediction} Condition")
+

model_tuned.hdf5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:434b16ae2f2b2119bc538790eb94153b8d4ce26c9d75a1075e205ce979f455c1
+size 2569264