Upload app.py

app.py

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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()