app.py

import numpy as np
import streamlit as st
import tensorflow as tf

from PIL import Image

LABELS = [0, 1, 2, 3, 4]

st.title("Surrey 2023 - Diabetes Grading")

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


# Load your TensorFlow model
model = tf.keras.models.load_model("model/kd_model")


def preprocess_image(image, target_size=(224, 224)):
    image = image.resize(target_size)
    image_array = np.array(image) / 255.0
    return np.expand_dims(image_array, axis=0)




col1, col2 = st.columns(2)

if uploaded_file is not None:
    image = Image.open(uploaded_file).convert('RGB')
    col1.image(image, caption="Uploaded Image", use_column_width=True)

    if st.button("Classify"):
        preprocessed_image = preprocess_image(image)
        predictions = model.predict(preprocessed_image)
        top_prediction = np.argmax(predictions[0])

        predicted_class = LABELS[top_prediction]

        col2.write(f"Predicted class: {predicted_class}")
else:
    col1.write("Upload an image to see the classification")
    col2.write("Prediction will appear here")