Create app.py

app.py

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+import streamlit as st
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+import numpy as np
+from PIL import Image
+
+# Function to load and preprocess the uploaded image
+def load_and_preprocess_image(uploaded_image, target_size=(224, 224)):
+    img = Image.open(uploaded_image)  # Open the uploaded image
+    img = img.resize(target_size)     # Resize to match model's input shape
+    img_array = np.array(img)         # Convert to numpy array
+    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+    img_array = img_array / 255.0     # Normalize pixel values
+    return img_array
+
+# Load your pre-trained model (assuming it's in HDF5 format)
+@st.cache(allow_output_mutation=True)  # Cache the model to avoid reloading it each time
+def load_cnn_model():
+    model = load_model('brain_tumor_model.h5')  # Replace with your model path
+    return model
+
+# Streamlit App UI
+st.title("Brain Tumor using CNN")
+st.write("Upload a brain scan (JPG format), and the model will predict its class.")
+
+# Upload image button
+uploaded_file = st.file_uploader("Choose a JPG image...", type="jpg")
+
+# If an image is uploaded, process it
+if uploaded_file is not None:
+    st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
+    st.write("Classifying...")
+
+    # Preprocess the uploaded image
+    processed_image = load_and_preprocess_image(uploaded_file, target_size=(224, 224))
+
+    # Load the model
+    model = load_cnn_model()
+
+    # Make predictions
+    predictions = model.predict(processed_image)
+    predicted_class = np.argmax(predictions, axis=1)
+
+    # Map the class index to the actual class names
+    class_names = ['glioma', 'pituitary', 'meningioma', 'healthy']
+    result = class_names[predicted_class[0]]
+
+    # Display the prediction
+    st.write(f"Predicted Class: **{result}**")
+else:
+    st.write("Please upload an image to classify.")
+