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import streamlit as st
from PIL import Image
from tensorflow.keras.models import load_model
import tensorflow as tf
import numpy as np
from keras.preprocessing.image import img_to_array
import os
@st.cache_resource
def load_model():
model_path = "model.keras" # Update with the absolute file path
return tf.keras.models.load_model(model_path)
model = load_model()
def prepare_image(img):
img = img.resize((220, 220))
img_array = img_to_array(img)
img_array = np.expand_dims(img_array, axis=0)
prediction = model.predict(img_array)
predicted_class = "Smoking" if prediction > 0.5 else "Not Smoking"
return predicted_class, prediction[0]
def run():
st.title("Smoking or Not Smoking Detection")
img_file = st.file_uploader("Choose an Image", type=["jpg", "png"])
if img_file is not None:
img = Image.open(img_file).resize((250, 250))
st.image(img, use_column_width=False)
# Create the directory if it doesn't exist
upload_dir = './upload_images/'
os.makedirs(upload_dir, exist_ok=True)
save_image_path = os.path.join(upload_dir, img_file.name)
with open(save_image_path, "wb") as f:
f.write(img_file.getbuffer())
predicted_class, score = prepare_image(img)
st.success(f"**Predicted : {predicted_class}, Score: {score}**")
run()
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