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Skin Cancer/skin-cancer.py

@@ -0,0 +1,33 @@
+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")

Skin Cancer/skin_cancer.keras

@@ -1,3 +1,3 @@
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