File size: 2,742 Bytes
cf86c3a dd8c606 155de20 cf86c3a 3ec40df dd8c606 3ec40df dd8c606 3ec40df dd8c606 cf86c3a 3ec40df 155de20 3ec40df 235d85c 3ec40df 235d85c 3ec40df 235d85c 3ec40df 235d85c 3ec40df 235d85c 3ec40df 235d85c 3ec40df 5d337dd 3ec40df 5d337dd 3ec40df 5d337dd 3ec40df 5d337dd 3ec40df dd8c606 3ec40df |
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 81 82 83 |
import gradio as gr
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
import uuid
import joblib
# Load the model
model = joblib.load("anomaly_detector_rf_model.pkl")
# Define the features expected by the model
expected_features = ["amount"] # Update this list as per your trained model
def detect_anomalies(df):
df = df.copy()
df['is_anomalous'] = model.predict(df[expected_features])
anomalies = df[df['is_anomalous'] == 1]
# Save anomalies to temporary CSV file
csv_filename = f"/tmp/anomalies_{uuid.uuid4().hex}.csv"
anomalies.to_csv(csv_filename, index=False)
return (
anomalies[["transaction_id", "merchant", "location", "amount", "is_anomalous"]],
csv_filename
)
def generate_summary(df):
total_transactions = len(df)
total_anomalies = df['is_anomalous'].sum()
percent_anomalies = round((total_anomalies / total_transactions) * 100, 2)
return f"Total Transactions: {total_transactions}\nTotal Anomalies: {total_anomalies}\nAnomaly Rate: {percent_anomalies}%"
def generate_charts(df):
fig, ax = plt.subplots(1, 2, figsize=(12, 5))
# Distribution of Amounts
sns.histplot(df['amount'], bins=30, ax=ax[0], kde=True)
ax[0].set_title('Transaction Amount Distribution')
# Anomalies by Merchant
anomaly_counts = df[df['is_anomalous'] == 1]['merchant'].value_counts().nlargest(10)
sns.barplot(x=anomaly_counts.values, y=anomaly_counts.index, ax=ax[1])
ax[1].set_title('Top 10 Merchants with Anomalies')
plt.tight_layout()
chart_path = f"/tmp/chart_{uuid.uuid4().hex}.png"
plt.savefig(chart_path)
plt.close()
return chart_path
def app_interface(file):
df = pd.read_csv(file.name)
anomalies, csv_path = detect_anomalies(df)
summary = generate_summary(df)
chart_path = generate_charts(df)
return anomalies, summary, chart_path, csv_path
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.Markdown("""
# 🔍 Elder Financial Abuse Detection Tool
Upload a transaction dataset to identify potential financial abuse patterns in elderly individuals.
""")
with gr.Row():
file_input = gr.File(label="📂 Upload Transaction CSV")
analyze_btn = gr.Button("🚀 Analyze")
with gr.Row():
anomalies_output = gr.Dataframe(label="⚠️ Detected Anomalies", wrap=True)
summary_output = gr.Textbox(label="📊 Summary")
chart_output = gr.Image(label="📈 Analysis Charts")
csv_download = gr.File(label="📁 Download Anomalies CSV")
analyze_btn.click(
fn=app_interface,
inputs=[file_input],
outputs=[anomalies_output, summary_output, chart_output, csv_download]
)
demo.launch() |