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import streamlit as st
import pandas as pd
import plotly.express as px
def main():
st.title("📚 Project Documentation")
# Custom CSS for better styling
st.markdown("""
<style>
.question-card {
background-color: #f8f9fa;
padding: 20px;
border-radius: 10px;
border-left: 5px solid #1f77b4;
margin: 20px 0;
}
.question {
color: #1f77b4;
font-size: 1.2em;
font-weight: bold;
margin-bottom: 15px;
}
.answer {
color: #2c3e50;
line-height: 1.6;
}
</style>
""", unsafe_allow_html=True)
# Q1: Development Timeline
st.markdown("""
<div class="question-card">
<div class="question">⏱️ Q1: How long did it take to solve the problem?</div>
<div class="answer">
The solution was developed in approximately <b>5 hours</b> (excluding data collection and model training phases).
</div>
</div>
""", unsafe_allow_html=True)
# Q2: Solution Explanation
st.markdown("""
<div class="question-card">
<div class="question">🔍 Q2: Can you explain your solution approach?</div>
<div class="answer">
The solution implements a multi-stage document classification pipeline:
<br><br>
<b>1. Direct URL Text Approach:</b>
<ul>
<li>Initially considered direct URL text extraction</li>
<li>Found limitations in accuracy and reliability</li>
</ul>
<br>
<b>2. Baseline Approach (ML Model):</b>
<ul>
<li>Implemented TF-IDF vectorization</li>
<li>Used Logistic Regression for classification</li>
<li>Provided quick and efficient results</li>
</ul>
<br>
<b>3. (DL Model):</b>
<ul>
<li>Utilized BERT-based model architecture</li>
<li>Fine-tuned on construction document dataset</li>
<li>Achieved superior accuracy and context understanding</li>
</ul>
</div>
</div>
""", unsafe_allow_html=True)
# Q3: Model Selection
st.markdown("""
<div class="question-card">
<div class="question">🤖 Q3: Which models did you use and why?</div>
<div class="answer">
Implemented baseline using TF-IDF and Logistic Regression and then used BERT-based model:
<br><br>
<b>Baseline Model:</b>
<ul>
<li>TF-IDF + Logistic Regression</li>
<li>Quick inference time</li>
<li>Resource-efficient</li>
</ul>
<br>
<b>BERT Model:</b>
<ul>
<li>Fine-tuned on 1800 samples text</li>
<li>Better context understanding</li>
<li>Better handling of complex documents</li>
</ul>
</div>
</div>
""", unsafe_allow_html=True)
# Q4: Limitations and Improvements
st.markdown("""
<div class="question-card">
<div class="question">⚠️ Q4: What are the current limitations and potential improvements?</div>
<div class="answer">
<b>Current Implementation & Limitations:</b>
<ul>
<li>~25% of dataset URLs were inaccessible</li>
<li>Used Threadpooling for parallel downloading of train and test documents</li>
</ul>
<br>
<b>Proposed Improvements:</b>
<ul>
<li>Use latest LLMs like GPT-4o, Claude 3.5 Sonnet etc with few shot prompting to speed up the development process</li>
<li>Optimize inference pipeline for faster processing using distilled models like DistilBERT, or the last BERT based model - ModernBERT to compare the performance</li>
<li>Add support for more document formats</li>
</ul>
</div>
</div>
""", unsafe_allow_html=True)
# Q5: Model Performance
st.markdown("""
<div class="question-card">
<div class="question">📊 Q5: What is the model's performance on test data?</div>
<div class="answer">
<b>BERT Model Performance:</b>
<br><br>
<div style="overflow-x: auto;">
<table style="
width: 100%;
border-collapse: collapse;
margin: 20px 0;
font-size: 0.9em;
font-family: sans-serif;
box-shadow: 0 0 20px rgba(0, 0, 0, 0.15);
border-radius: 5px;
">
<thead>
<tr style="
background-color: #1f77b4;
color: white;
text-align: left;
">
<th style="padding: 12px 15px;">Category</th>
<th style="padding: 12px 15px;">Precision</th>
<th style="padding: 12px 15px;">Recall</th>
<th style="padding: 12px 15px;">F1-Score</th>
<th style="padding: 12px 15px;">Support</th>
</tr>
</thead>
<tbody>
<tr style="border-bottom: 1px solid #dddddd;">
<td style="padding: 12px 15px;"><b>Cable</b></td>
<td style="padding: 12px 15px;">1.00</td>
<td style="padding: 12px 15px;">1.00</td>
<td style="padding: 12px 15px;">1.00</td>
<td style="padding: 12px 15px;">92</td>
</tr>
<tr style="border-bottom: 1px solid #dddddd; background-color: #f3f3f3;">
<td style="padding: 12px 15px;"><b>Fuses</b></td>
<td style="padding: 12px 15px;">0.95</td>
<td style="padding: 12px 15px;">1.00</td>
<td style="padding: 12px 15px;">0.98</td>
<td style="padding: 12px 15px;">42</td>
</tr>
<tr style="border-bottom: 1px solid #dddddd;">
<td style="padding: 12px 15px;"><b>Lighting</b></td>
<td style="padding: 12px 15px;">0.94</td>
<td style="padding: 12px 15px;">1.00</td>
<td style="padding: 12px 15px;">0.97</td>
<td style="padding: 12px 15px;">74</td>
</tr>
<tr style="border-bottom: 1px solid #dddddd; background-color: #f3f3f3;">
<td style="padding: 12px 15px;"><b>Others</b></td>
<td style="padding: 12px 15px;">1.00</td>
<td style="padding: 12px 15px;">0.92</td>
<td style="padding: 12px 15px;">0.96</td>
<td style="padding: 12px 15px;">83</td>
</tr>
</tbody>
<tfoot>
<tr style="background-color: #f8f9fa; font-weight: bold; border-top: 2px solid #dddddd;">
<td style="padding: 12px 15px;">Accuracy</td>
<td style="padding: 12px 15px;" colspan="3">0.98</td>
<td style="padding: 12px 15px;">291</td>
</tr>
<tr style="background-color: #f8f9fa; color: #666;">
<td style="padding: 12px 15px;">Macro Avg</td>
<td style="padding: 12px 15px;">0.97</td>
<td style="padding: 12px 15px;">0.98</td>
<td style="padding: 12px 15px;">0.98</td>
<td style="padding: 12px 15px;">291</td>
</tr>
<tr style="background-color: #f8f9fa; color: #666;">
<td style="padding: 12px 15px;">Weighted Avg</td>
<td style="padding: 12px 15px;">0.98</td>
<td style="padding: 12px 15px;">0.98</td>
<td style="padding: 12px 15px;">0.98</td>
<td style="padding: 12px 15px;">291</td>
</tr>
</tfoot>
</table>
</div>
</div>
</div>
""", unsafe_allow_html=True)
st.markdown("""
<div style='
background-color: #f8f9fa;
padding: 20px;
border-radius: 10px;
border-left: 5px solid #1f77b4;
margin: 20px 0;
'>
✨ Perfect performance (1.00) for Cable category<br>
📈 High recall (1.00) across most categories<br>
🎯 Overall accuracy of 98%<br>
⚖️ Balanced performance across all metrics
</div>
""", unsafe_allow_html=True)
# Q6: Metric Selection
st.markdown("""
<div class="question-card">
<div class="question">📈 Q6: Why did you choose these particular metrics?</div>
<div class="answer">
Our metric selection was driven by the dataset characteristics:
<br><br>
<b>Key Considerations:</b>
<ul>
<li>Dataset has mild class imbalance (Imbalance Ratio: 2.36)</li>
<li>Need for balanced evaluation across all classes</li>
</ul>
<br>
<b>Selected Metrics:</b>
<ul>
<li><b>Precision:</b> Critical for minimizing false positives</li>
<li><b>Recall:</b> Important for catching all instances of each class</li>
<li><b>F1-Score:</b> Provides balanced evaluation of both metrics</li>
<li><b>Weighted Average:</b> Accounts for class imbalance</li>
</ul>
</div>
</div>
""", unsafe_allow_html=True)
# Performance Visualization
st.markdown("### 📊 Model Performance Comparison")
metrics = {
'Metric': ['Accuracy', 'Precision', 'Recall', 'F1-Score'],
'Baseline': [0.85, 0.83, 0.84, 0.83],
'BERT': [0.98, 0.97, 0.98, 0.98]
}
df = pd.DataFrame(metrics)
fig = px.bar(
df,
x='Metric',
y=['Baseline', 'BERT'],
barmode='group',
title='Model Performance Comparison',
color_discrete_sequence=['#2ecc71', '#3498db'],
template='plotly_white'
)
fig.update_layout(
title_x=0.5,
title_font_size=20,
legend_title_text='Model Type',
xaxis_title="Evaluation Metric",
yaxis_title="Score",
bargap=0.2,
height=500
)
st.plotly_chart(fig, use_container_width=True)
main() |