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import streamlit as st
import tensorflow as tf
import numpy as np
from PIL import Image
import io
# Set seed for reproducibility
np.random.seed(42)
tf.random.set_seed(42)
# Define the correct class labels mapping
class_labels = ['akiec', 'bcc', 'bkl', 'df', 'nv', 'vasc', 'mel']
# Load the trained model
@st.cache_resource
def load_model():
return tf.keras.models.load_model('final_model.h5')
model = load_model()
# Load mean and std from training (you'll need to provide these)
@st.cache_resource
def load_mean_std():
mean = np.load('mean.npy')
std = np.load('std.npy')
return mean, std
mean, std = load_mean_std()
# Define the image preprocessing function
def preprocess_image(image):
image = image.resize((28, 28))
image = np.asarray(image)
image = (image - mean) / std
image = np.expand_dims(image, axis=0)
return image
# Streamlit app
st.title('Skin Lesion Predictor')
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
image = Image.open(io.BytesIO(uploaded_file.read())).convert('RGB')
st.image(image, caption='Uploaded Image', use_container_width=True)
# Preprocess the image
processed_image = preprocess_image(image)
# Make prediction
predictions = model.predict(processed_image)
# Get predicted class index and name
predicted_class_idx = np.argmax(predictions, axis=1)[0]
predicted_class = class_labels[predicted_class_idx]
confidence = predictions[0][predicted_class_idx]
# Display results
st.write(f"Predicted Class: {predicted_class}")
st.write(f"Confidence: {confidence:.2f}")
# Display bar chart of all predictions
st.bar_chart(dict(zip(class_labels, predictions[0])))
st.write("Note: This is a demo application and should not be used for medical diagnosis. Always consult with a healthcare professional.")
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