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app.py

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+import streamlit as st
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.preprocessing import image
+from PIL import Image
+
+# ------------------------------
+# Load the trained model
+# ------------------------------
+@st.cache_resource()
+def load_model():
+    return tf.keras.models.load_model("model.h5")
+
+model = load_model()
+
+# Class labels from your notebook
+CLASS_LABELS = ["Glioma", "Meningioma", "No Tumor", "Pituitary Tumor"]
+
+# ------------------------------
+# Image Preprocessing
+# ------------------------------
+def preprocess_image(img):
+    """Preprocess the image to match the model's input requirements."""
+    img = img.resize((224, 224))  # Resize to model input size
+    img = img.convert("RGB")  # Ensure RGB mode
+    img_array = np.array(img) / 255.0  # Normalize pixel values
+    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+    return img_array
+
+# ------------------------------
+# Streamlit UI
+# ------------------------------
+st.title("🧠 Brain Tumor Detection with Deep Learning")
+st.write("Upload an MRI scan (JPG, PNG) to check for a brain tumor.")
+
+uploaded_file = st.file_uploader("Choose an MRI scan...", type=["jpg", "png", "jpeg"])
+
+if uploaded_file is not None:
+    # Display the uploaded image
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded MRI Scan", use_column_width=True)
+
+    # Preprocess the image
+    img_array = preprocess_image(image)
+
+    # Make prediction
+    prediction = model.predict(img_array)
+    predicted_class = np.argmax(prediction, axis=1)[0]
+    confidence = np.max(prediction)
+
+    # Display result
+    st.subheader("🩺 Prediction Results")
+    st.write(f"**Predicted Class:** {CLASS_LABELS[predicted_class]}")
+    st.write(f"**Confidence Score:** {confidence:.2f}")
+
+    # Provide interpretation
+    if predicted_class == 2:
+        st.success("✅ No Brain Tumor Detected.")
+    else:
+        st.error(f"🚨 Brain Tumor Detected: **{CLASS_LABELS[predicted_class]}**. Consult a doctor.")
+

requirements.txt

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+streamlit
+tensorflow
+numpy
+pillow

saved_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:63172564e417d6ee4208b1c5d1503a70cd3ab8a997f0d4be1ce170e8a0888bb3
+size 213127952