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import gradio as gr
import requests
import json
import plotly
def predict_fraud(selected_model, step, transaction_type, amount, oldbalanceOrg):
# URL of the Flask API deployed on Heroku
url = "https://xai-fraud-sense-7f7f48d380fe.herokuapp.com/predict_and_explain"
# Prepare the data in the format expected by the Flask API
data = {
'selected_model': selected_model,
'step': step,
'transaction_type': transaction_type,
'amount': amount,
'oldbalanceOrg': oldbalanceOrg
}
# Send a POST request to the Flask API
response = requests.post(url, json=data)
if response.status_code == 200:
# Extract the response data
result = response.json()
prediction_text = result['prediction_text']
lime_explanation = result['lime_explanation']
# Parse the JSON strings back into Plotly figures
radial_plot_json = result['radial_plot']
bar_chart_json = result['bar_chart']
radial_plot = plotly.graph_objs.Figure(json.loads(radial_plot_json))
bar_chart = plotly.graph_objs.Figure(json.loads(bar_chart_json))
narrative = result['narrative']
# Return the results
return prediction_text, radial_plot, bar_chart, lime_explanation, narrative
else:
return "Error: " + response.text, None, None, None, None
# Organizing inputs and outputs with enhanced styling
with gr.Blocks() as iface:
gr.Markdown("<h2 style='text-align: center; font-weight: bold;'>FraudSenseXAI - Advanced Fraud Detection</h2>")
gr.Markdown("<p style='text-align: center;'>Predict and analyze fraudulent transactions.</p>", elem_id="description")
with gr.Row():
with gr.Column():
gr.Markdown("#### Input Parameters")
model_selection = gr.Dropdown(['Random Forest', 'Gradient Boost', 'Neural Network'], label="Model Selection")
step = gr.Number(value=1, label="Step")
transaction_type = gr.Dropdown(['Transfer', 'Payment', 'Cash Out', 'Cash In'], label="Transaction Type")
transaction_amount = gr.Number(label="Transaction Amount")
old_balance_org = gr.Number(label="Old Balance Org")
submit_button = gr.Button("Submit", variant="primary")
prediction_text = gr.Text(label="Prediction")
lime_explanation_text = gr.Text(label="LIME Explanation")
with gr.Column():
gr.Markdown("#### Visualization")
radial_plot = gr.Plot(label="Radial Plot")
bar_chart = gr.Plot(label="Bar Chart")
narrative_text = gr.Text(label="Narrative") # Placed in the same column
submit_button.click(
predict_fraud,
inputs=[model_selection, step, transaction_type, transaction_amount, old_balance_org],
outputs=[prediction_text, radial_plot, bar_chart, lime_explanation_text, narrative_text]
)
iface.launch(share=True)
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