Skin Cancer/skin-cancer.py

import numpy as np
import os
import cv2
import tensorflow as tf
import streamlit as st
from tensorflow.keras.models import load_model

st.subheader("Skin Cancer Detection CNN")
print(os.getcwd())
@st.cache_resource
def load_cached_models():
    model = load_model("Skin Cancer/skin_cancer.keras")    
    return model

model= load_cached_models()

uploaded_file = st.file_uploader("Upload an X-ray image (JPEG/PNG)", type=["jpeg", "jpg", "png"])

if uploaded_file is not None:
    file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
    img = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  
    img_resized = cv2.resize(img, (224, 224)).astype('float32') / 255.0
    img_expanded = np.expand_dims(img_resized, axis=0)

    pred = model.predict(img_expanded)
    pred = pred.flatten()[np.argmax(pred)]
    st.image(img, caption="Uploaded X-ray", use_container_width=True)
    st.subheader("Prediction")
    final_pred = round(float(pred) * 100, 2)
    st.progress(int(round(pred * 100)))
    st.write(f"### Percentage: {final_pred}%")
    st.write("#### Cancer Detected" if pred > 0.5 else "Your Image seems normal")