added

app.py

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+from keras.models import load_model
+from keras.preprocessing import image
+from keras.applications.vgg16 import preprocess_input # import the model`s skeleton
+import numpy as np
+import tensorflow as tf
+
+model=load_model('chest_xray.h5')
+
+#load the test image
+#tf.keras.utils.load_img
+img=tf.keras.utils.load_img('image.jpeg',target_size=(224,224))
+
+x=tf.keras.preprocessing.image.img_to_array(img) # image as a numpy array
+
+x=np.expand_dims(x, axis=0)
+
+img_data=preprocess_input(x) # organize for the prediction
+
+classes=model.predict(img_data)
+
+result=int(classes[0][0])
+
+if result== 0:
+    print("Person is Affected By PNEUMONIA")
+else:
+    print("Result is Normal")

chest_xray.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5292242f3784a552b74a9c9c4c2b7409b91e710a1695ea7e80ac3408b67a2d3a
+size 59545056

requirements.txt

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+streamlit
+tensorflow
+keras
+numpy

streamlit.py

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+import streamlit as st
+from keras.models import load_model
+from keras.preprocessing import image
+from keras.applications.vgg16 import preprocess_input
+import numpy as np
+import tensorflow as tf
+
+# Set the title of the web app
+st.title('Pneumonia Detection Using VGG16')
+
+st.text("Coded by Manith Jayaba")
+
+# Load the Keras model
+model = load_model('chest_xray.h5')
+
+# Create a file uploader for the test image
+uploaded_file = st.file_uploader("Choose an image...", type="jpg")
+
+# Perform the prediction when an image is uploaded
+if uploaded_file is not None:
+    img = tf.keras.utils.load_img(uploaded_file, target_size=(224, 224))
+    x = tf.keras.preprocessing.image.img_to_array(img)
+    x = np.expand_dims(x, axis=0)
+    img_data = preprocess_input(x)
+    classes = model.predict(img_data)
+    result = int(classes[0][0])
+    if result == 0:
+        st.write("Person is Affected By PNEUMONIA")
+    else:
+        st.write("Result is Normal")
+    st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)