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import streamlit as st
import numpy as np
import tensorflow as tf
from PIL import Image
import io
# Load your pre-trained model
model = tf.keras.models.load_model('model.keras')
# Define the image preprocessing function
# Define the image preprocessing function
def preprocess_image(image):
# Convert image to RGB if it's grayscale
if image.mode != 'RGB':
image = image.convert('RGB')
# Resize and preprocess the image
image = image.resize((224, 224)) # Adjust the size as per your model's requirement
image_array = np.array(image) / 255.0 # Normalize the image
image_array = np.expand_dims(image_array, axis=0) # Add batch dimension
return image_array
# Define the class labels
class_labels = ['Atelectasis', 'Cardiomegaly', 'Consolidation', 'Edema', 'Effusion',
'Emphysema', 'Fibrosis', 'Infiltration', 'Mass',
'Nodule', 'Pleural_Thickening', 'Pneumothorax']
# class_labels = ["Class1", "Class2", "Class3", "Class4", "Class5"] # Update with actual class names
# Streamlit app
st.title("Image Classification with Grad-CAM")
st.write("Upload an image to get predictions:")
# Upload image
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
# Read and display the image
image = Image.open(uploaded_file)
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]
# Display results
st.write("Predictions with probability greater than 0.5:")
for label, prob in top_predictions:
st.write(f"{label}: {prob*100:.2f}%")
percentage = int(prob * 100)
# st.progress(prob)
st.progress(percentage)
if not top_predictions:
st.write("No predictions with probability greater than 0.5.") |