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import streamlit as st
from keras.models import load_model
from keras.applications.vgg16 import preprocess_input
import numpy as np
from PIL import Image
import tensorflow as tf
# Load your pre-trained model
model = load_model('chest_xray.h5')
st.title('Pneumonia Detection from Chest X-Ray Images')
# Create a file uploader to upload images
uploaded_file = st.file_uploader("Choose an X-ray image...", type=["jpg", "jpeg", "png"])
def predict(image):
# Preprocess the image to get it into the right format for the model
img = image.resize((224,224))
x = tf.keras.preprocessing.image.img_to_array(img)
x = np.expand_dims(x, axis=0)
img_data = preprocess_input(x)
# Make the prediction
classes = model.predict(img_data)
return int(classes[0][0])
if uploaded_file is not None:
# Display the uploaded image
st.image(uploaded_file, caption='Uploaded X-ray Image', use_column_width=True)
st.write("")
st.write("Classifying...")
image = Image.open(uploaded_file)
# Predict and display the results
result = predict(image)
if result == 0:
st.write("Person is Affected By PNEUMONIA")
else:
st.write("Result is Normal") |