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import streamlit as st
import cv2
import pandas as pd
import numpy as np
from tensorflow.keras.models import load_model
from PIL import Image
import datetime
# Load File
model_imp = load_model('./src/model.keras')
def run():
# Title
st.title('Safe and Unsafe Working Condition')
# Sub Header
st.subheader('Safety Prediction of Working Condition Image ')
# Image
image = Image.open('./src/image12.jpg')
st.image(image)
# Create form
with st.form(key='safety-prediction'):
st.markdown('Data ID')
date = st.date_input("Select a date", help='Input the time the photo was taken or obtained')
time = st.time_input("Select a time")
timestamp = datetime.datetime.combine(date, time)
name_id = st.text_input('ID', value='---name/id---', help='Input your name or identity')
uploaded_img = st.file_uploader("Upload an image", type=['jpg', 'jpeg', 'png'])
submitted = st.form_submit_button('Predict')
# Data inference
data_inf_input = {
'timestamp': timestamp,
'name_id': name_id,
}
# Data frame
st.markdown('Data Summary:')
data_inference = pd.DataFrame([data_inf_input])
st.dataframe(data_inference)
# Preprocessing
if uploaded_img is not None:
img_pil = Image.open(uploaded_img).convert('RGB')
img_arr = np.array(img_pil)
# Resize as input model (img_height × img_width)
img_resized = cv2.resize(img_arr, (200, 200))
# Scaling
img_array = np.array(img_resized) / 255.
# Change dimension
img_dims = np.expand_dims(img_array, axis=0)
st.image(uploaded_img)
else:
st.warning("Please upload an image to continue.")
st.markdown('Result:')
# Prediction
if submitted:
prob = model_imp.predict(img_dims)
# Convert to class (0 = safe, 1 = unsafe)
pred = 1 if prob >= 0.5 else 0
class_names = ['SAFE', 'UNSAFE']
label = class_names[pred]
percentage = prob[0][0] * 100
# Result
st.write(f"#### Prediction: this image consider {label} working condition.")
st.write(f"##### Probability: {percentage:.2f} % ")
if __name__ == '__main__':
run() |