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6f49f65 a1d6114 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | import dash
from dash import html, dcc
import dash_bootstrap_components as dbc
dash.register_page(__name__, path='/', name="Control Room")
layout = html.Div([
# --- Paper Header Section ---
html.Div([
html.H1("Deep Learning for Financial Statement Fraud Detection",
className="text-white fw-bold",
style={"fontFamily": "serif", "letterSpacing": "1px", "fontSize": "3rem"}),
html.P("A Comparative Analysis of Transformers-based Architectures in SEC Filing Audits",
className="text-info text-uppercase fw-light mb-4",
style={"letterSpacing": "3px", "fontSize": "0.9rem"}),
# Abstract-style Introduction
html.Div([
html.P([
html.B("Abstract: "),
"This intelligence system leverages Bidirectional Encoder Representations from Transformers (BERT) "
"to identify anomalous patterns in corporate filings. By integrating FinBERT and DeBERTa-v3 architectures "
"with Parameter-Efficient Fine-Tuning (PEFT), the engine achieves high-precision risk scoring, "
"transforming qualitative financial narratives into quantitative fraud probability metrics."
], className="text-muted mx-auto", style={"maxWidth": "800px", "textAlign": "justify", "lineHeight": "1.6"})
], className="mb-5")
], className="text-center mt-5 mb-5"),
# --- Quantitative Performance Metrics (The "Results" Row) ---
dbc.Row([
dbc.Col(
html.Div([
html.Small("DATABASE SCOPE", className="text-muted fw-bold"),
html.H2("1,284", className="text-white fw-bold mt-2"),
html.Div(style={"height": "2px", "width": "40px", "background": "#00d2ff", "margin": "10px 0"}),
html.P("Processed Document Samples", className="text-muted small")
], className="modern-card p-4", style={"borderLeft": "1px solid #00d2ff"}), width=4
),
dbc.Col(
html.Div([
html.Small("ANOMALY DETECTION", className="text-muted fw-bold"),
html.H2("12", className="text-white fw-bold mt-2"),
html.Div(style={"height": "2px", "width": "40px", "background": "#dc3545", "margin": "10px 0"}),
html.P("Validated Risk Sigils", className="text-muted small")
], className="modern-card p-4", style={"borderLeft": "1px solid #dc3545"}), width=4
),
dbc.Col(
html.Div([
html.Small("SOTA BENCHMARK (F1)", className="text-muted fw-bold"),
html.H2("93.4%", className="text-white fw-bold mt-2"),
html.Div(style={"height": "2px", "width": "40px", "background": "#9d50bb", "margin": "10px 0"}),
html.P("DeBERTa-v3 Engine Performance", className="text-muted small")
], className="modern-card p-4", style={"borderLeft": "1px solid #9d50bb"}), width=4
),
], className="mb-5 g-4"),
# --- Methodology & Execution ---
dbc.Row([
dbc.Col([
html.Div([
html.H4("Experimental Protocol", className="text-white mb-3", style={"fontFamily": "serif"}),
html.P([
"The detection engine utilizes cross-attention mechanisms to parse semantic irregularities. "
"Select 'Launch Detection' to initiate the inference pipeline on raw unstructured text data."
], className="text-muted mb-4"),
dbc.Button("Launch Inference Engine", href="/detection",
className="px-5 py-2",
style={
"background": "transparent",
"border": "1px solid #00d2ff",
"color": "#00d2ff",
"borderRadius": "0",
"textTransform": "uppercase",
"letterSpacing": "2px"
})
], className="modern-card p-5", style={"border": "1px solid rgba(255,255,255,0.05)"})
], width=12)
])
]) |