app.py

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.")