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app.py

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+import streamlit as st
+from keras.models import load_model
+from PIL import Image, ImageOps
+import numpy as np
+# Disable scientific notation for clarity
+np.set_printoptions(suppress=True)
+
+# Load the model
+model = load_model("braintumour_model.h5", compile=False)
+
+# Load the labels
+class_names = open("labels.txt", "r").readlines()
+
+# Function to preprocess the image
+def preprocess_image(image):
+    # resizing the image to be at least 224x224 and then cropping from the center
+    size = (224, 224)
+    image = ImageOps.fit(image, size, Image.Resampling.LANCZOS)
+
+    # turn the image into a numpy array
+    image_array = np.asarray(image)
+
+    # Normalize the image
+    normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1
+
+    # Return the preprocessed image
+    return normalized_image_array
+
+# Function to predict the image class
+def predict_image_class(image):
+    # Create the array of the right shape to feed into the keras model
+    data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
+    
+    # Load the image into the array
+    data[0] = image
+    
+    # Predict the model
+    prediction = model.predict(data)
+    index = np.argmax(prediction)
+    class_name = class_names[index]
+    confidence_score = prediction[0][index]
+
+    return class_name[2:], round(confidence_score, 2)*100
+
+# Streamlit app
+def main():
+    # Set page title and icon
+    st.set_page_config(page_title="Brain Tumour Classifier", page_icon="🧠")
+
+    st.title("Brain Tumour Classifier")
+
+    # Upload image file
+    uploaded_file = st.file_uploader("Upload the MRI", type=["jpg", "jpeg", "png"])
+
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file).convert("RGB")
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+        
+        preprocessed_image = preprocess_image(image)
+        
+        # Predict the image class
+        class_name, confidence_score = predict_image_class(preprocessed_image)
+
+        # Display the prediction
+        st.subheader("RESULT")
+        st.write("Class:", "**" + class_name + "**")
+        st.write("Prediction Probability:", "**" + str(confidence_score) + "%**")
+
+
+# Run the app
+if __name__ == "__main__":
+    main()

braintumour_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:378e3392f9db5be6a8055a7a691a670c28275b5d09f6402d2b1b59ee3c205c37
+size 2453432

labels.txt

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+0 Glioma Tumour
+1 Meningioma Tumour
+2 No Tumor
+3 Pituitary Tumour