File size: 1,769 Bytes
2a9d41f 5e090bf 2a9d41f 45b79a1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | import streamlit as st
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
import numpy as np
# Load the model and weights
model_path = "Pneumonia_detection_using_CNN.h5"
weights_path = "Pneumonia_detection_using_CNN.weights.h5"
model = load_model(model_path)
model.load_weights(weights_path)
# Streamlit app
st.title('Pneumonia Detection App')
# File uploader for image input
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "webp"])
if uploaded_file is not None:
# Display the uploaded image
st.image(uploaded_file, caption='Uploaded Image', use_column_width=True)
if st.button('Predict', key='predict_button'):
# Load and preprocess the image
img = image.load_img(uploaded_file, target_size=(224, 224))
img_array = image.img_to_array(img)
img_array = np.expand_dims(img_array, axis=0)
# Make a prediction
prediction = model.predict(img_array)
# Display the prediction with confidence level in large highlighted text
class_names = ['Normal', 'Pneumonia']
predicted_class = class_names[np.argmax(prediction)]
confidence_level = np.max(prediction) * 100 # Convert probability to percentage
# Set text color based on prediction
if predicted_class == 'Normal':
text_color = 'green'
else:
text_color = 'red'
# Display prediction and confidence level in large highlighted text
st.markdown(f'<p style="font-size:32px; color:{text_color};">Prediction: {predicted_class}</p>', unsafe_allow_html=True)
st.markdown(f'<p style="font-size:32px; color:{text_color};">Confidence: {confidence_level:.2f}%</p>', unsafe_allow_html=True) |