app.py

import streamlit as st
import pandas as pd
import numpy as np
import tensorflow as tf
from PIL import Image
from tensorflow.keras.preprocessing.image import load_img, img_to_array

# Header
st.header('Fire Image Detection')

# Input user
image_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])

# Create a button for prediction
predict_button = st.button("Predict")

if predict_button and image_file is not None:
    # Preprocess input image
    def preprocess_image(img_path):
        img = load_img(img_path, target_size=(256, 256))
        img = img_to_array(img)
        img = np.expand_dims(img, axis=0)
        return img

    preprocessed_image = preprocess_image(image_file)

    # Load model
    model = tf.keras.models.load_model('./model.hdf5')

    if preprocessed_image is not None:
        # Make prediction
        prediction = model.predict(preprocessed_image)
        # Result
        st.subheader("Prediction:")
        if prediction > 0.95:
            st.write("Fire Detected")
        else:
            st.write("No Fire Detected")
        
        st.image(image_file, caption='Uploaded Image.', use_column_width=True, width=100)