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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://xaifraudsense-04ba19097287.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) | |