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.gitattributes

@@ -38,3 +38,4 @@ Blood[[:space:]]Cancer/blood_cancer.keras filter=lfs diff=lfs merge=lfs -text
 Blood[[:space:]]Cancer/test_imgs/benign.jpg filter=lfs diff=lfs merge=lfs -text
 Brain[[:space:]]Tumor/brain_tumor.keras filter=lfs diff=lfs merge=lfs -text
 Skin[[:space:]]Cancer/skin_cancer.keras filter=lfs diff=lfs merge=lfs -text
+Brain[[:space:]]Tumor/brain_tumor_model.keras filter=lfs diff=lfs merge=lfs -text

Brain Tumor/brain-tumor.py

@@ -0,0 +1,34 @@
+import numpy as np
+import cv2
+import tensorflow as tf
+import streamlit as st
+from tensorflow.keras.models import load_model
+
+st.subheader("Brain Tumor Detection CNN")
+@st.cache_resource
+def load_cached_models():
+    model = load_model("Brain Tumor/brain_tumor_model.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).flatten()
+
+    pred_id = np.argmax(pred)
+    final_pred = pred[pred_id]
+
+    st.image(img, caption="Uploaded X-ray", use_container_width=True)
+    st.subheader("Prediction")
+    
+    st.progress(int(round(final_pred * 100)))
+    st.write(f"### Percentage: {int(round(final_pred * 100))}%")
+    st.write("#### Tumor Detected" if final_pred > 0.5 else "Your X-ray seems normal")

Brain Tumor/brain_tumor_model.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7fb7086ddebef47f5b573a01995fd86c97da06429e07f79140a624dda3b8f777
+size 75732883