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import streamlit as st
# Custom CSS for better styling
st.markdown("""
<style>
.main-title {
font-size: 36px;
color: #4A90E2;
font-weight: bold;
text-align: center;
}
.sub-title {
font-size: 24px;
color: #4A90E2;
margin-top: 20px;
}
.section {
background-color: #f9f9f9;
padding: 15px;
border-radius: 10px;
margin-top: 20px;
}
.section h2 {
font-size: 22px;
color: #4A90E2;
}
.section p, .section ul {
color: #666666;
}
.link {
color: #4A90E2;
text-decoration: none;
}
.benchmark-table {
width: 100%;
border-collapse: collapse;
margin-top: 20px;
}
.benchmark-table th, .benchmark-table td {
border: 1px solid #ddd;
padding: 8px;
text-align: left;
}
.benchmark-table th {
background-color: #4A90E2;
color: white;
}
.benchmark-table td {
background-color: #f2f2f2;
}
</style>
""", unsafe_allow_html=True)
# Main Title
st.markdown('<div class="main-title">Image Classification with Swin</div>', unsafe_allow_html=True)
# Description
st.markdown("""
<div class="section">
<p><strong>Swin Transformer</strong> is a cutting-edge image classification model introduced in the paper "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" by Liu et al. The model image_classifier_swin_base_patch4_window7_224 is a Swin model, adapted from Hugging Face and curated for scalability and production-readiness using Spark NLP.</p>
</div>
""", unsafe_allow_html=True)
# Image Classification Overview
st.markdown('<div class="sub-title">What is Image Classification?</div>', unsafe_allow_html=True)
st.markdown("""
<div class="section">
<p><strong>Image Classification</strong> is a computer vision task where an algorithm is trained to recognize and classify objects within images. This process involves assigning a label or category to an image based on its visual content.</p>
<h2>How It Works</h2>
<p>Image classification typically involves the following steps:</p>
<ul>
<li><strong>Data Collection</strong>: Gather a dataset of labeled images.</li>
<li><strong>Preprocessing</strong>: Normalize and resize images to prepare them for the model.</li>
<li><strong>Model Training</strong>: Use a machine learning model, such as Swin, to learn patterns and features from the images.</li>
<li><strong>Inference</strong>: Apply the trained model to new images to predict their labels.</li>
</ul>
<h2>Why Use Image Classification?</h2>
<p>Image classification can automate and streamline many tasks, such as:</p>
<ul>
<li>Identifying objects in photos for content tagging.</li>
<li>Enhancing search functionality by categorizing images.</li>
<li>Supporting autonomous systems like self-driving cars.</li>
</ul>
<h2>Where to Use It</h2>
<p>Applications of image classification span across various industries:</p>
<ul>
<li><strong>Healthcare</strong>: Diagnosing diseases from medical images.</li>
<li><strong>Retail</strong>: Sorting and tagging product images.</li>
<li><strong>Security</strong>: Facial recognition for authentication.</li>
</ul>
<h2>Importance</h2>
<p>Image classification is crucial because it enables machines to interpret visual data, which is essential for creating intelligent systems capable of understanding and interacting with the world in a more human-like manner.</p>
<p>The <strong>Swin Transformer</strong> model used in this example is a state-of-the-art approach for image classification, offering advanced performance and scalability. It utilizes a hierarchical transformer architecture to capture intricate patterns and relationships within images, enhancing classification accuracy and efficiency.</p>
</div>
""", unsafe_allow_html=True)
# How to Use
st.markdown('<div class="sub-title">How to Use the Model</div>', unsafe_allow_html=True)
st.code('''
import sparknlp
from sparknlp.base import *
from sparknlp.annotator import *
from pyspark.ml import Pipeline
# Load image data
imageDF = spark.read \\
.format("image") \\
.option("dropInvalid", value = True) \\
.load("src/test/resources/image/")
# Define Image Assembler
imageAssembler: ImageAssembler = ImageAssembler() \\
.setInputCol("image") \\
.setOutputCol("image_assembler")
# Define Swin classifier
imageClassifier = SwinForImageClassification \\
.pretrained("image_classifier_swin_base_patch4_window7_224") \\
.setInputCols(["image_assembler"]) \\
.setOutputCol("class")
# Create pipeline
pipeline = Pipeline().setStages([imageAssembler, imageClassifier])
# Apply pipeline to image data
pipelineDF = pipeline.fit(imageDF).transform(imageDF)
# Show results
pipelineDF \\
.selectExpr("reverse(split(image.origin, '/'))[0] as image_name", "class.result") \\
.show(truncate=False)
''', language='python')
# Results
st.markdown('<div class="sub-title">Results</div>', unsafe_allow_html=True)
st.markdown("""
<div class="section">
<table class="benchmark-table">
<tr>
<th>Image Name</th>
<th>Result</th>
</tr>
<tr>
<td>dog.JPEG</td>
<td>[whippet]</td>
</tr>
<tr>
<td>cat.JPEG</td>
<td>[Siamese]</td>
</tr>
<tr>
<td>bird.JPEG</td>
<td>[peacock]</td>
</tr>
</table>
</div>
""", unsafe_allow_html=True)
# Model Information
st.markdown('<div class="sub-title">Model Information</div>', unsafe_allow_html=True)
st.markdown("""
<div class="section">
<table class="benchmark-table">
<tr>
<th>Attribute</th>
<th>Description</th>
</tr>
<tr>
<td><strong>Model Name</strong></td>
<td>image_classifier_swin_base_patch4_window7_224</td>
</tr>
<tr>
<td><strong>Compatibility</strong></td>
<td>Spark NLP 4.3.0+</td>
</tr>
<tr>
<td><strong>License</strong></td>
<td>Open Source</td>
</tr>
<tr>
<td><strong>Edition</strong></td>
<td>Official</td>
</tr>
<tr>
<td><strong>Input Labels</strong></td>
<td>[image_assembler]</td>
</tr>
<tr>
<td><strong>Output Labels</strong></td>
<td>[class]</td>
</tr>
<tr>
<td><strong>Language</strong></td>
<td>en</td>
</tr>
<tr>
<td><strong>Size</strong></td>
<td>108.0 MB</td>
</tr>
</table>
</div>
""", unsafe_allow_html=True)
# References
st.markdown('<div class="sub-title">References</div>', unsafe_allow_html=True)
st.markdown("""
<div class="section">
<ul>
<li><a class="link" href="https://sparknlp.org/2023/03/28/image_classifier_swin_base_patch4_window7_224_en.html" target="_blank" rel="noopener">Swin Model on Spark NLP</a></li>
<li><a class="link" href="https://huggingface.co/microsoft/swin-base-patch4-window7-224" target="_blank" rel="noopener">Swin Model on Hugging Face</a></li>
<li><a class="link" href="https://github.com/microsoft/Swin-Transformer" target="_blank" rel="noopener">Swin Transformer GitHub Repository</a></li>
<li><a class="link" href="https://arxiv.org/abs/2103.14030" target="_blank" rel="noopener">Swin Transformer Paper</a></li>
</ul>
</div>
""", unsafe_allow_html=True)
# Community & Support
st.markdown('<div class="sub-title">Community & Support</div>', unsafe_allow_html=True)
st.markdown("""
<div class="section">
<ul>
<li><a class="link" href="https://sparknlp.org/" target="_blank">Official Website</a>: Documentation and examples</li>
<li><a class="link" href="https://join.slack.com/t/spark-nlp/shared_invite/zt-198dipu77-L3UWNe_AJ8xqDk0ivmih5Q" target="_blank">Slack</a>: Live discussion with the community and team</li>
<li><a class="link" href="https://github.com/JohnSnowLabs/spark-nlp" target="_blank">GitHub</a>: Bug reports, feature requests, and contributions</li>
<li><a class="link" href="https://medium.com/spark-nlp" target="_blank">Medium</a>: Spark NLP articles</li>
<li><a class="link" href="https://www.youtube.com/channel/UCmFOjlpYEhxf_wJUDuz6xxQ/videos" target="_blank">YouTube</a>: Video tutorials</li>
</ul>
</div>
""", unsafe_allow_html=True)
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