app.py

import streamlit as st
import numpy as np
import joblib
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.vgg16 import preprocess_input
from sklearn.svm import SVC

# import requests

# url = 'https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5'
# response = requests.get(url)

# # Save the file locally
# with open('vgg16_weights.h5', 'wb') as f:
#     f.write(response.content)

# print("Download complete.")

# Load the model (SVM and VGG16)
@st.cache_resource
def load_model():
    # Load the VGG16 base model (without top)
    vgg16_base = VGG16(weights='vgg16_weights.h5', include_top=False, input_shape=(224, 224, 3))
    
    # Load your SVM model
    svm_vgg16 = joblib.load('svm_model_vgg16.joblib')
    return vgg16_base, svm_vgg16

# Feature extraction function
def extract_features(model, img):
    img = np.expand_dims(img, axis=0)  # Add batch dimension
    img = preprocess_input(img)  # Preprocess image for VGG16
    features = model.predict(img)  # Extract features using VGG16
    features = features.flatten()  # Flatten the features for SVM input
    return features

# Prediction function
def predict_label(model, svm, img):
    features = extract_features(model, img)  # Extract features
    prediction = svm.predict([features])  # Get prediction (which is an integer index)
    labels = ['Mild_Demented', 'Moderate_Demented', 'Non_Demented', 'Very_Mild_Demented']
    return labels[int(prediction[0])]

# Streamlit interface
st.title('Dementia Severity Prediction')
st.write('Upload an MRI scan image to predict the dementia severity.')

# Upload image
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png"])

if uploaded_file is not None:
    # Read image using Keras preprocessing utility (resize automatically)
    img = np.array(image.load_img(uploaded_file, target_size=(224, 224)))
    
    # Load model
    vgg16_base, svm_vgg16 = load_model()

    # Make prediction
    prediction = predict_label(vgg16_base, svm_vgg16, img)

    # Show the result
    st.image(uploaded_file, caption='Uploaded Image', use_column_width=True)
    st.write(f"Prediction: {prediction}")