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import streamlit as st
from tensorflow.keras.models import load_model
from PIL import Image
import numpy as np
# Load the model
model = load_model('cnn_model.h5', compile=False)
# Function to process the uploaded image
def process_image(img):
img = img.resize((64, 64))
img = np.array(img)
img = img / 255.0
img = np.expand_dims(img, axis=0)
return img
# Custom CSS for better design
st.markdown("""
<style>
.main {
background-color: #f0f4f7;
padding: 2rem;
border-radius: 10px;
box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
}
h1 {
color: #4caf50;
font-size: 36px;
font-weight: bold;
text-align: center;
}
.description {
font-size: 18px;
color: #555;
text-align: center;
margin-bottom: 2rem;
}
.stButton>button {
background-color: #4caf50;
color: white;
font-size: 18px;
border-radius: 5px;
padding: 10px 20px;
margin-top: 20px;
border: none;
box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
}
.stButton>button:hover {
background-color: #45a049;
}
</style>
""", unsafe_allow_html=True)
# Title of the application
st.title('Glass Detection from Image 📸')
# Brief description
st.markdown('<p class="description">Upload a photo, and the model will predict whether there is a glass or not.</p>', unsafe_allow_html=True)
# File uploader for the user to upload an image
file = st.file_uploader('Select an image (jpg, jpeg, png)', type=['jpg', 'jpeg', 'png'])
if file is not None:
# Displaying the uploaded image
img = Image.open(file)
st.image(img, caption='Uploaded Image')
# Processing the image
image = process_image(img)
prediction = model.predict(image)
# Get the predicted class index
predicted_class = np.argmax(prediction, axis=-1)
# Displaying prediction result
st.subheader("Prediction Result:")
if predicted_class == 1:
prediction_text = '✅ There is a glass'
else:
prediction_text = '❌ There is no a glass'
st.write(prediction_text)
else:
st.write("Please upload an image to get started.")
# Footer
st.markdown("<p style='text-align: center; font-size: 12px; color: #888;'>Built with 💚 by Senasu Demir</p>", unsafe_allow_html=True)
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