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import streamlit as st
from tensorflow.keras.models import load_model
import cv2
import numpy as np
from PIL import Image
# Load the model
model = load_model('best_model.keras')
def preprocess_image(image):
# Resize the image as required by the model
img = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
img = cv2.resize(img, (220, 220)) # Resize to match the model input
# Normalize the image
img = img.astype('float32') / 255.0
# Add a batch dimension
img = np.expand_dims(img, axis=0)
return img
def run():
# Create title
st.title('Detecting Fire in Forest Images')
# Create a form for image input
with st.form('form_forest_fire_detection'):
# Image upload
uploaded_image = st.file_uploader('Upload an image', type=['jpg', 'jpeg', 'png'])
# Submit button
submitted = st.form_submit_button('Detect Fire or No Fire')
if uploaded_image:
# Display the uploaded image
st.image(uploaded_image, caption='Uploaded Image', use_column_width=True)
if submitted:
# Preprocess the image
image = Image.open(uploaded_image)
preprocessed_image = preprocess_image(image)
# Predict using the model
prediction = model.predict(preprocessed_image)
# For example, if prediction > 0.5 classify as 'No Fire', otherwise 'Fire'
fire_probability = prediction[0][0]
result = 'No Fire' if fire_probability > 0.5 else 'Fire'
st.write('## Prediction: ', result)
st.write('## Raw Prediction Output: ', prediction)
if __name__ == '__main__':
run()
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