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#import library
import pandas as pd
import numpy as np
import streamlit as st
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tensorflow_hub.keras_layer import KerasLayer
import tensorflow as tf
from tensorflow.keras.models import load_model
#import pickle
import pickle
#load model
def run():
file = st.file_uploader("Upload an image", type=["jpg", "png"])
model = load_model('src/my_model.keras', custom_objects={'KerasLayer': KerasLayer})
target_size=(224, 224)
def import_and_predict(image_data, model):
image = load_img(image_data, target_size=(224, 224))
img_array = img_to_array(image)
img_array = tf.expand_dims(img_array, 0) # Create a batch
# Normalize the image
img_array = img_array / 255.0
# Make prediction
predictions = model.predict(img_array)
# Get the class with the highest probability
idx = np.where(predictions >= 0.5, 1, 0).item()
# predicted_class = np.argmax(predictions)
jenis = ['Brain Tumor', 'Healthy']
result = f"Prediction: {jenis[idx]}"
return result
if file is None:
st.text("Please upload an image file")
else:
result = import_and_predict(file, model)
st.image(file)
st.write(result)
if __name__ == "__main__":
run() |