Create app.py

app.py

@@ -0,0 +1,72 @@
+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)