Create app.py

app.py

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+import streamlit as st
+import tensorflow as tf
+import cv2
+import numpy as np
+from PIL import Image
+
+# Load the trained model
+model = tf.keras.models.load_model('braintumor.h5')
+
+# Define labels for the classes
+labels = ['glioma_tumor', 'meningioma_tumor', 'no_tumor', 'pituitary_tumor']
+
+# Define a function to make predictions
+def predict_tumor_type(image):
+    # Preprocess the image
+    img = cv2.resize(image, (150, 150))
+    img_array = np.array(img)
+    img_array = img_array.reshape(1, 150, 150, 3)
+    
+    # Make prediction
+    prediction = model.predict(img_array)
+    predicted_class = labels[np.argmax(prediction)]
+    confidence = np.max(prediction)
+    
+    return predicted_class, confidence
+
+# Streamlit UI
+st.title("Brain Tumor Classification")
+st.write("Upload an image for brain tumor classification.")
+
+uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_image is not None:
+    # Display the uploaded image
+    image = Image.open(uploaded_image)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+    
+    # Make prediction
+    prediction, confidence = predict_tumor_type(np.array(image))
+    
+    st.write("Prediction:", prediction)
+    st.write(f"Confidence: {confidence * 100:.2f}%")
+
+st.write("DISCLAIMER:")
+st.write("0 - glioma_tumor")
+st.write("1 - meningioma_tumor")
+st.write("2 - No_tumor")
+st.write("3 - pituitary_tumor")