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| import gradio as gr | |
| import requests | |
| import json | |
| import plotly | |
| def predict_fraud(selected_model, selected_interpretability_method, step, transaction_type, amount, oldbalanceOrg): | |
| # Validation checks | |
| if not selected_model: | |
| return "Model Selection is required.", None, None, None, None, None, None, None, None, None, None | |
| if not selected_interpretability_method: | |
| return "Interpretability Technique is required.", None, None, None, None, None, None, None, None, None, None | |
| if step == 0: # Assuming step is a numerical value, check for None explicitly | |
| return "Step (Transaction Time) is required.", None, None, None, None, None, None, None, None, None, None | |
| if not transaction_type: | |
| return "Transaction Type is required.", None, None, None, None, None, None, None, None, None, None | |
| if amount == 0: # Assuming amount is a numerical value, check for None explicitly | |
| return "Transaction Amount is required.", None, None, None, None, None, None, None, None, None, None | |
| if oldbalanceOrg is None: # Assuming oldbalanceOrg is a numerical value, check for None explicitly | |
| return "Old Balance Org is required.", None, None, None, None, None, None, None, None, None, None | |
| #url = "https://fraud-sense-16a8ed5f96b5.herokuapp.com/predict_and_explain" | |
| #url = "https://fraudsense-02168c9829aa.herokuapp.com/predict_and_explain" | |
| url = "https://xaifraudsense-48ebac2f952e.herokuapp.com/predict_and_explain" | |
| data = { | |
| 'selected_model': selected_model, | |
| 'selected_interpretability_method': selected_interpretability_method, | |
| 'step': step, | |
| 'transaction_type': transaction_type, | |
| 'amount': amount, | |
| 'oldbalanceOrg': oldbalanceOrg | |
| } | |
| response = requests.post(url, json=data) | |
| if response.status_code == 200: | |
| result = response.json() | |
| # Directly use the base64-encoded image string for the network graph | |
| network_graph = result['network_graph'] | |
| # Ensure other data is handled correctly | |
| prediction_text = result['prediction_text'] | |
| model_explanation = result['model_explanation'] | |
| mod_plot_json = result['mod_plot'] | |
| # Parse the JSON strings back into Plotly figures | |
| mod_plot = plotly.graph_objs.Figure(json.loads(mod_plot_json)) | |
| features_influence = result['features_influence'] | |
| network_graph_json = result['network_graph'] #graph_objects | |
| # Parse the JSON strings back into Plotly figures | |
| network_graph = plotly.graph_objs.Figure(json.loads(network_graph_json)) | |
| network_explainer = result['network_explainer'] | |
| top_main_effect = result['top_main_effect'] | |
| top_interaction = result['top_interaction'] | |
| # 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.get('narrative', "") | |
| # Return the results | |
| return prediction_text, model_explanation, mod_plot, features_influence, network_graph, network_explainer, top_main_effect, top_interaction,radial_plot, bar_chart, narrative | |
| else: | |
| # Handle error scenario by returning placeholders for each expected output | |
| return "Error: " + response.text, None, None, None, None,None, None, None, None, None, None | |
| # Define your Gradio interface here | |
| with gr.Blocks() as app: | |
| 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") | |
| gr.Markdown("<p style='text-align: center;'>This app utilizes financial synthetic dataset from kaggle, and it is structured to adapt to the nature of the dataset .</p>", elem_id="description") | |
| error_message = gr.Textbox(label="Error Message", visible=False, lines=2, interactive=False) | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown("#### INPUT PARAMETERS: All fields are required") | |
| model_selection = gr.Dropdown(choices=['Random Forest', 'Gradient Boost', 'Neural Network'], label="Model Selection") | |
| interpretability_selection = gr.Dropdown(choices=['LIME', 'SHAP'], label="Interpretability Technique") | |
| step = gr.Number(label="Step(Transaction Time)") | |
| transaction_type = gr.Dropdown(choices=['Transfer', 'Payment', 'Cash Out', 'Cash In'], label="Transaction Type") | |
| transaction_amount = gr.Number(label="Transaction Amount:") | |
| old_balance_org = gr.Number(label="Old Balance: total account balance prior to transaction initiation ") | |
| submit_btn = gr.Button("Analyze") | |
| # Define outputs | |
| gr.Markdown("#### PREDICTION RESULT") | |
| prediction_text = gr.Textbox(label="Prediction", lines=7) | |
| with gr.Column(): | |
| gr.Markdown("#### MODEL INTERPRETATIONS") | |
| model_explanation = gr.Textbox(label="Model Explanation", lines=7) | |
| mod_plot = gr.Plot(label="Model Plot") | |
| features_influence = gr.Textbox(label="Features Influence", lines=7) | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown("#### FEATURE INTERACTIONS: Note that this function only supports SHAP. LIME & Neural Network are not supported") | |
| network_graph = gr.Plot(label="Network Graph") | |
| network_explainer = gr.Text(label="Network Graph Explanation") | |
| top_main_effect = gr.Text(label="Top Main Effect", lines=7) | |
| top_interaction = gr.Text(label="Top Interaction", lines=7) | |
| with gr.Column(): | |
| gr.Markdown("#### COUNTERFACTUAL EXPLANATIONS") | |
| radial_plot = gr.Plot(label="Radial Plot") | |
| bar_chart = gr.Plot(label="Bar Chart") | |
| narrative = gr.Textbox(label="Narrative") | |
| def update_error_message(error_text, *rest): | |
| if error_text and not error_text.startswith("Error: "): | |
| error_message.update(value=error_text, visible=True) | |
| return (None,) * len(rest) # Update to match the number of outputs minus the error message | |
| else: | |
| error_message.update(visible=False) | |
| return (error_text,) + rest | |
| submit_btn.click( | |
| predict_fraud, | |
| inputs=[model_selection, interpretability_selection, step, transaction_type, transaction_amount, old_balance_org], | |
| outputs=[prediction_text, model_explanation, mod_plot, features_influence, network_graph, network_explainer, top_main_effect, top_interaction,radial_plot, bar_chart, narrative] | |
| ) | |
| app.launch(share=True) | |