Create app.py

app.py

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+import streamlit as st
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.image import load_img, img_to_array
+import numpy as np
+import os
+
+# Set page config
+st.set_page_config(
+    page_title="Brain Tumor Detection",
+    page_icon="🧠",
+    layout="centered"
+)
+
+# Load the trained model
+try:
+    MODEL_PATH = 'model.h5'
+    if not os.path.exists(MODEL_PATH):
+        st.error("Model file not found. Please ensure model.h5 exists in the 'models' directory")
+        st.stop()
+    model = load_model(MODEL_PATH)
+except Exception as e:
+    st.error(f"Error loading model: {str(e)}")
+    st.stop()
+
+# Class labels
+class_labels = ['pituitary', 'glioma', 'notumor', 'meningioma']
+
+# Helper function to predict tumor type
+def predict_tumor(image):
+    IMAGE_SIZE = 128
+    img = load_img(image, target_size=(IMAGE_SIZE, IMAGE_SIZE))
+    img_array = img_to_array(img) / 255.0  # Normalize pixel values
+    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+
+    predictions = model.predict(img_array)
+    predicted_class_index = np.argmax(predictions, axis=1)[0]
+    confidence_score = np.max(predictions, axis=1)[0]
+
+    if class_labels[predicted_class_index] == 'notumor':
+        return "No Tumor", confidence_score
+    else:
+        return f"Tumor: {class_labels[predicted_class_index]}", confidence_score
+
+# Main UI
+st.title("Brain Tumor Detection")
+st.write("Upload an MRI scan to detect the presence and type of brain tumor")
+
+# File uploader
+uploaded_file = st.file_uploader("Choose an MRI image file", type=['jpg', 'jpeg', 'png'])
+
+if uploaded_file is not None:
+    # Display the uploaded image
+    st.image(uploaded_file, caption="Uploaded MRI Scan", use_container_width=True)
+    
+    # Add a predict button
+    if st.button("Predict"):
+        with st.spinner("Analyzing image..."):
+            # Make prediction
+            result, confidence = predict_tumor(uploaded_file)
+            
+            # Display results
+            st.success("Analysis Complete!")
+            st.write(f"**Prediction:** {result}")
+            st.write(f"**Confidence:** {confidence*100:.2f}%")
+            
+            # Display additional information based on the result
+            if "No Tumor" not in result:
+                st.warning("Please consult with a healthcare professional for proper medical advice.")