TruthCheck / templates /home.html
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{% extends "base.html" %}
{% block title %}Home - Detect Fake News{% endblock %}
{% block content %}
<section class="home-hero-section">
<div class="container home-content-wrapper">
<div class="row align-items-center">
<div class="col-lg-6 home-text-area">
<h1 class="home-title">
Detect Fake News with Confidence
<!-- <span class="fake-news-highlight">Fake News</span> with Confidence -->
</h1>
<p class="home-subtitle">
Our AI-powered platform helps you identify misinformation and verify the authenticity of news articles and information online.
</p>
<div class="d-flex gap-3">
<a href="{{ url_for('index') }}" class="btn btn-primary btn-lg" style="background-color: dodgerblue; border-color:dodgerblue">
<!-- "background-color: #4f46e5; border-color: #4f46e5;" -->
Verify News Now</a>
<a href="{{ url_for('about') }}" class="btn btn-outline-secondary btn-lg" style="border-color: #dee2e6; color: #343a40; background-color: #dee2e6;">Learn More</a>
</div>
</div>
<div class="col-lg-6 d-flex justify-content-center justify-content-lg-end mt-5 mt-lg-0">
<div class="home-image-box">
<img src="https://www.keele.ac.uk/research/researchnews/2025/january/artificial-intelligence/fake-news-detector-960.jpg" alt="AI Powered Platform Graphic" style="max-width: 450px;">
</div>
</div>
</div>
</div>
</section>
<div class="app-box-2">
<div class="container mt-5 mb-5">
<h4 class="text-center mb-5 home-title" style="color:dodgerblue;">How TruthCheck Works: The AI Verification Process</h4>
<div class="row" >
<div class="col-lg-4 mb-4">
<div class="card shadow-sm h-100">
<div class="card-body">
<h5 class="card-title fw-bold">1. Text Embedding (Vectorization)</h5>
<p class="card-text">
The core of our technology is the AI model, "all-MiniLM-L6-v2".
It converts the news headline you enter into a "numerical vector" (embedding). This vector mathematically represents the semantic meaning of the text, allowing for objective comparison.
</p>
</div>
</div>
</div>
<div class="col-lg-4 mb-4">
<div class="card shadow-sm h-100">
<div class="card-body">
<h5 class="card-title fw-bold">2. Data Sourcing & Comparison</h5>
<p class="card-text">
The detector first checks for an extremely high match (similarity > 0.9) against a database of "pre-verified facts".
If no direct match is found, it queries the web using the <b>SerpAPI</b> (Google Search API) to retrieve the top 5 most relevant articles from trusted online sources in real-time.
</p>
</div>
</div>
</div>
<div class="col-lg-4 mb-4">
<div class="card shadow-sm h-100">
<div class="card-body">
<h5 class="card-title fw-bold">3. Verdict Generation</h5>
<p class="card-text">
Beyond just similarity, our AI uses a <b>Cross-Encoder</b> to check for logical contradictions.
Even if words match, if the meaning is opposite (like 'Won' vs 'Lost'), the <b>T5 Model</b> identifies the error and provides a detailed explanation.
</p>
</div>
</div>
</div>
</div>
</div>
</div>
<div class="app-box-2">
<h3 class="mt-5 mb-5 text-center home-title" style="color:dodgerblue">Technology Summary</h3>
<ul class="list-group list-group-flush mx-auto" style="max-width: 800px;">
<li class="list-group-item"><b>Core NLP Model:</b> all-MiniLM-L6-v2 Sentence Transformer</li>
<li class="list-group-item"><b>Real-time Data:</b> Google Search results via serpapi</li>
<li class="list-group-item"><b>Backend Framework:</b> Flask (Python)</li>
<li class="list-group-item"><b>Success Threshold:</b> Semantic similarity score of *0.7*</li>
</ul>
</div>
{% endblock %}