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import streamlit as st
import numpy as np
import tensorflow as tf
from PIL import Image
import cv2 as cv
import io
# Load trained model
model = tf.keras.models.load_model('model.keras')
# Define the image preprocessing function
def preprocess_image(image):
# Convert to numpy array
image_array = np.array(image)
# Apply Gaussian Blur
image_array = cv.GaussianBlur(image_array, (9, 9), 0)
# Apply CLAHE
clahe = cv.createCLAHE(clipLimit=3, tileGridSize=(10, 10))
clahe_image = clahe.apply(image_array)
# Convert CLAHE image to RGB
clahe_image = cv.cvtColor(clahe_image, cv.COLOR_GRAY2RGB)
# Normalize image to [0, 1]
clahe_image = (clahe_image - clahe_image.min()) / (clahe_image.max() - clahe_image.min())
# Resize the image to 224x224
image_resized = cv.resize(clahe_image, (224, 224))
# Add batch dimension
image_array = np.expand_dims(image_resized, axis=0).astype(np.float32)
return image_array
# Define the class labels
class_labels = ['Atelectasis', 'Cardiomegaly', 'Consolidation', 'Edema', 'Effusion',
'Emphysema', 'Fibrosis', 'Infiltration', 'Mass',
'Nodule', 'Pleural_Thickening', 'Pneumothorax']
# Streamlit app
st.title("Chest X-ray Classification")
# Upload image
uploaded_file = st.file_uploader("Upload a Chest X-ray image...", type=["jpg", "jpeg", "png"])
# Create two columns
col1, col2 = st.columns(2)
if uploaded_file is not None:
# Read and display the image
image = Image.open(uploaded_file)
with col1:
st.image(image, caption='Uploaded Image', use_column_width=True)
# Preprocess the image
preprocessed_image = preprocess_image(image)
# Make predictions
predictions = model.predict(preprocessed_image)[0]
# Get top 3 predictions with probability greater than 0.5
top_predictions = [(label, prob) for label, prob in zip(class_labels, predictions) if prob > 0.5]
top_predictions = sorted(top_predictions, key=lambda x: x[1], reverse=True)[:3]
with col2:
# Display results
if not top_predictions:
st.write("No diseases found with probability greater than 50%.")
else:
st.write("Predicted Disease(s):")
for label, prob in top_predictions:
st.write(f"{label}: {prob*100:.2f}%")
percentage = int(prob * 100)
st.progress(percentage) |