app.py

import streamlit as st
import tensorflow as tf
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
import numpy as np
from PIL import Image

# Set up Streamlit app
st.title("Skin Cancer Detection")
st.write("Upload an image to detect the type of skin lesion.")

# Load the trained model
model_path = "Model.h5"  
model = load_model(model_path)

# Define class names
class_names = [
    "actinic keratosis", 
    "basal cell carcinoma", 
    "dermatofibroma", 
    "melanoma", 
    "nevus", 
    "pigmented benign keratosis", 
    "seborrheic keratosis", 
    "squamous cell carcinoma", 
    "vascular lesion"
]

# Image preprocessing function
def preprocess_image(uploaded_file, img_height=224, img_width=224):
    img = Image.open(uploaded_file).convert("RGB")
    img = img.resize((img_width, img_height))
    img_array = np.array(img) / 255.0  # Normalize the image
    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
    return img_array

# File uploader for image
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])

if uploaded_file is not None:
    # Display uploaded image
    st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
    
    # Preprocess the image
    img_array = preprocess_image(uploaded_file)
    
    # Predict using the model
    predictions = model.predict(img_array)
    predicted_class_index = np.argmax(predictions[0])
    predicted_class = class_names[predicted_class_index]
    confidence = predictions[0][predicted_class_index]
    
    # Display the prediction
    st.write(f"### Predicted Class: {predicted_class}")
    st.write(f"### Confidence: {confidence:.2%}")

    # Display probabilities for all classes
    st.write("### Class Probabilities:")
    for i, class_name in enumerate(class_names): 
        st.write(f"{class_name}: {predictions[0][i]:.2%}")