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