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import streamlit as st
import numpy as np
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
# Load the pre-trained Keras model for lung cancer classification
model = load_model("./model/lung_cancer_detection_model.h5", compile=False)
# Then, compile your model using the optimizer
model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
# Function to preprocess the uploaded image
def predict_single_image(image_path, model, target_size=(128, 128)):
# Load and preprocess the image
img = image.load_img(image_path, target_size=target_size, color_mode="grayscale")
img = image.img_to_array(img)
img = np.expand_dims(img, axis=0)
img /= 255.0 # Rescale the image
# Predict the class probabilities
probabilities = model.predict(img)
# display(probabilities)
# Determine the predicted class label
predicted_class = "POSITIVE" if probabilities[0][0] > 0.5 else "NEGATIVE"
return predicted_class, probabilities[0][0]
# Function to classify the uploaded image
def classify_lung_cancer(img):
# Call the function to predict the class label for the single image
predicted_label, confidence = predict_single_image(
img, model, target_size=(512, 512)
)
# Print the prediction
# print('Predicted Label:', predicted_label)
# print('Confidence:', confidence)
if confidence < 0.5:
confidence = abs(1 - confidence)
return (predicted_label, confidence)
# Streamlit app
st.title("Lung Cancer Classification")
st.write(
"Upload an image and click 'Classify' to predict if it's positive or negative for lung cancer."
)
# Display the uploaded image
uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
# Classify the uploaded image
if st.button("Classify"):
if uploaded_image is not None:
predicted_label, confidence = classify_lung_cancer(uploaded_image)
# Style the prediction text
if predicted_label == "POSITIVE":
prediction_style = "color: red; font-size: 24px; text-transform: uppercase; border: 2px solid red;"
else:
prediction_style = "color: green; font-size: 24px; text-transform: uppercase; border: 2px solid green;"
st.markdown(
f"<p style='{prediction_style}'>Prediction: {predicted_label}</p>",
unsafe_allow_html=True,
)
st.write(f"Confidence: {confidence:.2f}")
st.progress(int(confidence * 100))
st.image(uploaded_image, caption="Uploaded Image.", use_column_width=True)
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