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import streamlit as st
from tensorflow.keras.models import load_model
from PIL import Image
import numpy as np
import pandas as pd
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
model = load_model('cnn_model.h5')
def process_image(img):
img = img.resize((32, 32))
img = np.array(img)
img = img / 255.0
img = np.expand_dims(img, axis=0)
return img
st.markdown("""
<style>
.title {
font-size: 50px;
color: #A8D5FF; # Pastel Blue color
font-weight: bold;
text-align: center;
}
.description {
font-size: 16px;
color: #555555;
text-align: center;
}
.uploaded-image {
display: block;
margin-left: auto;
margin-right: auto;
padding: 20px;
}
.prediction {
font-size: 20px;
font-weight: bold;
text-align: center;
}
.confidence {
font-size: 18px;
text-align: center;
color: #4CAF50;
}
.bar-chart {
text-align: center;
}
</style>
""", unsafe_allow_html=True)
st.markdown('<div class="title">CIFAR-10 Image Classification 🔎</div>', unsafe_allow_html=True)
st.markdown('<div class="description">Upload an image to classify it into one of the CIFAR-10 categories.</div>', unsafe_allow_html=True)
file = st.file_uploader('Upload an image', type=['jpg', 'jpeg', 'png'])
if file is not None:
img = Image.open(file)
st.markdown('<div class="uploaded-image">', unsafe_allow_html=True)
st.image(img, caption='Uploaded Image', use_container_width=True, output_format='PNG')
st.markdown('</div>', unsafe_allow_html=True)
with st.spinner('Processing...'):
image = process_image(img)
predictions = model.predict(image)
predicted_class = np.argmax(predictions)
class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
confidence = np.max(predictions) * 100 # Confidence score
st.markdown(f'<div class="prediction">Prediction: {class_names[predicted_class]}</div>', unsafe_allow_html=True)
st.markdown(f'<div class="confidence">Confidence: {confidence:.2f}%</div>', unsafe_allow_html=True)
st.markdown('<div class="bar-chart">Class Probabilities:</div>', unsafe_allow_html=True)
prob_df = pd.DataFrame(predictions[0], index=class_names, columns=["Probability"])
st.bar_chart(prob_df)
st.markdown('---')
st.markdown("<div style='text-align: center;'>Powered by Streamlit and TensorFlow</div>", unsafe_allow_html=True)
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