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import gradio as gr
import pandas as pd
import matplotlib.pyplot as plt
from loan_model import predict_default
from categorizer import categorize_expenses
from portfolio import analyze_portfolio
# Custom CSS remains the same
css = """
:root {
--primary: #2563eb;
--secondary: #0ea5e9;
}
body { font-family: 'Segoe UI', sans-serif; }
.header {
background: linear-gradient(120deg, var(--primary), var(--secondary));
padding: 2rem;
border-radius: 8px;
color: white;
}
.card {
border: 1px solid #e2e8f0;
border-radius: 8px;
padding: 1.5rem;
box-shadow: 0 4px 6px -1px rgba(0,0,0,0.1);
}
"""
# Loan Default Predictor UI
def loan_ui():
with gr.Column():
gr.Markdown("## 🏦 Loan Risk Assessment")
with gr.Row():
income = gr.Number(label="Monthly Income ($)", value=5000)
loan_amount = gr.Number(label="Loan Amount ($)", value=25000)
with gr.Row():
credit_score = gr.Slider(300, 850, label="Credit Score", value=720)
employment = gr.Dropdown(["Employed", "Self-employed", "Unemployed"], label="Employment Status", value="Employed")
submit_btn = gr.Button("Assess Risk", variant="primary")
output = gr.Label(label="Risk Prediction")
submit_btn.click(
predict_default,
inputs=[income, loan_amount, credit_score, employment],
outputs=output
)
# Expense Categorization UI
def expense_ui():
with gr.Column():
gr.Markdown("## 🧾 Expense Categorization")
file_input = gr.File(label="Upload Bank Statement (CSV)")
with gr.Row():
example_btn = gr.Button("Load Example")
submit_btn = gr.Button("Categorize", variant="primary")
output_table = gr.Dataframe(interactive=False, wrap=True)
example_btn.click(
lambda: "assets/example_statement.csv",
outputs=file_input
)
submit_btn.click(
categorize_expenses,
inputs=file_input,
outputs=output_table
)
# Portfolio Analyzer UI
def portfolio_ui():
with gr.Column():
gr.Markdown("## 📊 Portfolio Analysis")
file_input = gr.File(label="Upload Holdings (CSV)")
risk_profile = gr.Radio(["Conservative", "Moderate", "Aggressive"], label="Risk Preference", value="Moderate")
with gr.Row():
example_btn = gr.Button("Load Example")
submit_btn = gr.Button("Analyze", variant="primary")
with gr.Row():
allocation_plot = gr.Plot()
recommendations = gr.Textbox(label="Optimization Suggestions")
example_btn.click(
lambda: "assets/example_portfolio.csv",
outputs=file_input
)
submit_btn.click(
analyze_portfolio,
inputs=[file_input, risk_profile],
outputs=[allocation_plot, recommendations]
)
# Main App Assembly
with gr.Blocks(css=css, title="FinTech Toolkit") as app:
gr.Markdown("""
<div class="header">
<h1>🚀 Finance Toolkit Pro</h1>
<p>AI-powered tools for loan risk, expense tracking, and portfolio optimization</p>
</div>
""")
with gr.Tabs():
with gr.TabItem("Loan Default Predictor", id=0):
loan_ui()
with gr.TabItem("Expense Categorization", id=1):
expense_ui()
with gr.TabItem("Portfolio Analysis", id=2):
portfolio_ui()
gr.Markdown("---\n*Built for FinTech professionals*")
if __name__ == "__main__":
app.launch() |