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gradio_app.py

@@ -4,6 +4,8 @@ import pandas as pd
 import numpy as np
 from sklearn.metrics.pairwise import euclidean_distances
 import random
+import plotly.graph_objects as go
+import plotly.express as px
 
 model = joblib.load("churn_model.pkl")
 model_features = joblib.load("model_features.pkl")
@@ -160,14 +162,46 @@ def predict_churn(
     input_df = pd.DataFrame([[input_dict[col] for col in model_features]], columns=model_features)
     prediction = model.predict_proba(input_df)[0][1]
     score = round(prediction * 100, 2)
+    
+    # Create gauge chart for churn risk
+    fig_gauge = go.Figure(go.Indicator(
+        mode = "gauge+number",
+        value = score,
+        domain = {'x': [0, 1], 'y': [0, 1]},
+        title = {'text': "Churn Riski"},
+        gauge = {
+            'axis': {'range': [0, 100]},
+            'bar': {'color': "darkblue"},
+            'steps': [
+                {'range': [0, 30], 'color': "lightgreen"},
+                {'range': [30, 70], 'color': "yellow"},
+                {'range': [70, 100], 'color': "red"}
+            ],
+            'threshold': {
+                'line': {'color': "red", 'width': 4},
+                'thickness': 0.75,
+                'value': 50
+            }
+        }
+    ))
+    
+    # Create pie chart for probability distribution
+    fig_pie = px.pie(
+        values=[score, 100-score],
+        names=['Churn Riski', 'Kalma Olasılığı'],
+        title='Müşteri Durumu Dağılımı',
+        color_discrete_sequence=['red', 'green']
+    )
+    
     if score >= 50:
         comment = "Müşteri Kaybedilebilir."
     else:
         comment = "Müşteri Kayıp Riski Taşımıyor."
     result = f"Churn Riski: %{score} — {comment}"
+    
     # Vector similarity
     similar_customers = find_similar_customers_vector(input_df.values[0], n=5)
-    return result, similar_customers
+    return result, fig_gauge, fig_pie, similar_customers
 
 # Define options for dropdowns (Turkish values)
 phone_service_options = ["Evet", "Hayır"]
@@ -214,10 +248,19 @@ with gr.Blocks() as demo:
     with gr.Row():
         Contract = gr.Dropdown(contract_options, label="Sözleşme (Contract)")
         PaymentMethod = gr.Dropdown(payment_method_options, label="Ödeme Yöntemi (PaymentMethod)")
+    
     autofill_btn = gr.Button("Rastgele Müşteri ile Doldur")
     submit_btn = gr.Button("Tahmin Et")
-    output = gr.Textbox(label="Sonuç")
+    
+    with gr.Row():
+        output = gr.Textbox(label="Sonuç")
+    
+    with gr.Row():
+        gauge_plot = gr.Plot(label="Churn Risk Gauge")
+        pie_plot = gr.Plot(label="Probability Distribution")
+    
     similar_customers_table = gr.Dataframe(label="Benzer Müşteriler (İlk 5)")
+    
     autofill_btn.click(
         autofill_random_customer,
         inputs=[],
@@ -230,7 +273,7 @@ with gr.Blocks() as demo:
         inputs=[tenure, monthly, total, PhoneService, gender, SeniorCitizen, Partner, Dependents, PaperlessBilling,
                 MultipleLines, InternetService, OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport,
                 StreamingTV, StreamingMovies, Contract, PaymentMethod],
-        outputs=[output, similar_customers_table]
+        outputs=[output, gauge_plot, pie_plot, similar_customers_table]
     )
 
 if __name__ == "__main__":