Delete streamlit.py

streamlit.py

@@ -1,31 +0,0 @@
-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)