Gradio Frontend

frontend.py

@@ -1,2 +1,107 @@
 import gradio as gr
-from main import 
+import main
+import numpy as np
+import pandas as pd
+from main import clf_rf, clf_log, accuracy_score_rf, accuracy_score_lr, breier_score_rf, breier_score_lr, roc_rf, roc_lr
+
+def eda(Graphs):
+    match Graphs:
+        case "Customer Churn":
+            return gr.Image("graphs/EDAGraphs/Churn.png")
+        case "Contract":
+            return gr.Image("graphs/EDAGraphs/Contract.png")
+        case "Dependents":
+            return gr.Image("graphs/EDAGraphs/Dependents.png")
+        case "Device Protection":
+            return gr.Image("graphs/EDAGraphs/DeviceProtection.png")
+        case "Heatmap":
+            return gr.Image("graphs/EDAGraphs/Heatmap.png")
+        case "Monthly Charges":
+            return gr.Image("graphs/EDAGraphs/MonthlyCharges.png")
+        case "Online Backup":
+            return gr.Image("graphs/EDAGraphs/OnlineBackup.png")
+        case "Online Security":
+            return gr.Image("graphs/EDAGraphs/OnlineSecurity.png")
+        case "Paperless Billing":
+            return gr.Image("graphs/EDAGraphs/PaperlessBilling.png")
+        case "Partner":
+            return gr.Image("graphs/EDAGraphs/Partner.png")
+        case "Payment Method":
+            return gr.Image("graphs/EDAGraphs/PaymentMethod.png")
+        case "Senior Citizen":
+            return gr.Image("graphs/EDAGraphs/SeniorCitizen.png")
+        case "Tech Support":
+            return gr.Image("graphs/EDAGraphs/TechSupport.png")
+        case "Tenure":
+            return gr.Image("graphs/EDAGraphs/tenure.png")
+
+def result(Graphs):
+    match Graphs:
+        case "Correlation":
+            inf = "Enter inference here"
+            return [gr.Image("graphs/EDAGraphs/Correlation.png"), inf]
+        case "Confusion Matrix for Random Forest":
+            inf = "Enter inference here"
+            return [gr.Image("graphs/OutputGraphs/ConfusionMatrixRandomForest.png"), inf]
+        case "Confusion Matrix for Logistic regression":
+            inf = "Enter inference here"
+            return [gr.Image("graphs/OutputGraphs/ConfusionMatrixLogistic.png"), inf]
+        case "SHAP analysis for Random Forest":
+            inf = "Enter inference here"
+            return [gr.Image("graphs/OutputGraphs/SHAP_RandomForest_Summary.png"), inf]
+        case "SHAP analysis for Logistic Regression":
+            inf = "Enter inference here"
+            return [gr.Image("graphs/OutputGraphs/SHAP_Logistic_Summary.png"), inf]
+
+def metrics(Algorithms):
+    match Algorithms:
+        case "Random Forest":
+            df = pd.DataFrame(clf_rf)
+            df = df.drop(columns = ['accuracy', 'macro avg', 'weighted avg']).T
+            df = df.reset_index().rename(columns={'index': 'class'})
+            df_clf = gr.DataFrame(
+                value = df
+            )
+            df_acc = gr.DataFrame(
+                headers = ['Accuracy Score', 'Breier Score', 'ROC Score'],
+                value = [list([accuracy_score_rf, breier_score_rf, roc_rf])],
+            )
+            return df_clf, df_acc
+        
+        case "Logistic Regression":
+            df = pd.DataFrame(clf_log)
+            df = df.drop(columns = ['accuracy', 'macro avg', 'weighted avg']).T
+            df = df.reset_index().rename(columns={'index': 'class'})
+            df_clf = gr.DataFrame(
+                value = df
+            )
+            df_acc = gr.DataFrame(
+                headers = ['Accuracy Score', 'Breier Score', 'ROC Score'],
+                value = [list([accuracy_score_lr, breier_score_lr, roc_lr])],
+            )
+            return df_clf, df_acc
+
+with gr.Blocks() as Output:
+    gr.Markdown("View Exploratory data Analysis and Output")
+    with gr.Tab("EDA Graphs"):
+        eda_input = gr.Radio(["Customer Churn", "Contract", "Dependents", "Device Protection", "Heatmap", "Monthly Charges", "Online Backup", "Online Security", "Paperless Billing", "Partner", "Payment Method", "Senior Citizen", "Tech Support", "Tenure"], show_label= False)
+        eda_output = gr.Image()
+
+        eda_input.change(fn = eda, inputs= eda_input, outputs= eda_output)
+
+    with gr.Tab("Output Graphs"):
+        result_input = gr.Radio(["Correlation", "Confusion Matrix for Random Forest", "Confusion Matrix for Logistic regression", "SHAP analysis for Random Forest", "SHAP analysis for Logistic Regression"], show_label = False)
+
+        result_output = [gr.Image(), gr.Label()]
+
+        result_input.change(fn = result, inputs=result_input, outputs = result_output)
+
+    with gr.Tab("Performance Metrics"):
+        algorithm = gr.Radio(["Random Forest", "Logistic Regression"], show_label= False)
+
+        metrics_output = [gr.DataFrame(), gr.DataFrame()]
+
+        algorithm.change(fn = metrics, inputs = algorithm, outputs = metrics_output)
+
+
+Output.launch()

main.py

@@ -62,6 +62,7 @@ y_rf_pred = rf_clf.predict(X_test)
 # Evaluating Random Forest 
 print("\n\t\t\tRandom Forest Classifier:\n")
 print("Classification Report for Random Forest:")
+clf_rf = classification_report(y_test, y_rf_pred, output_dict= True)
 print(classification_report(y_test, y_rf_pred))
 
 cm = confusion_matrix(y_test, y_rf_pred)
@@ -70,12 +71,16 @@ display = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=rf_clf.clas
 display.plot(cmap=plt.cm.Blues)
 plt.savefig("graphs/OutputGraphs/ConfusionMatrixRandomForest.png")
 
+breier_score_rf = brier_score_loss(y_test, y_rf_pred)
+accuracy_score_rf = accuracy_score(y_test, y_rf_pred)
+roc_rf = roc_auc_score(y_test, y_rf_pred)
+
 print("Brier Score Loss:")
-print(brier_score_loss(y_test, y_rf_pred))
+print(breier_score_rf)
 print("Accuracy Score:")
-print(accuracy_score(y_test, y_rf_pred))
+print(accuracy_score_rf)
 print("ROC AUC Score:")
-print(roc_auc_score(y_test, y_rf_pred))
+print(roc_rf)
 
 
 explainer = shap.TreeExplainer(rf_clf)
@@ -94,6 +99,7 @@ y_log_pred = logistic.predict(X_test)
 # Evaluating logistic
 print("\t\t\tLogistic Regression:\n")
 print("Classification Report for Logistic Regression:")
+clf_log = classification_report(y_test, y_log_pred, output_dict= True)
 print(classification_report(y_test, y_log_pred))
 
 cm = confusion_matrix(y_test, y_log_pred)
@@ -102,12 +108,16 @@ display = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=logistic.cl
 display.plot(cmap=plt.cm.Blues)
 plt.savefig("graphs/OutputGraphs/ConfusionMatrixLogistic.png")
 
+breier_score_lr = brier_score_loss(y_test, y_log_pred)
+accuracy_score_lr = accuracy_score(y_test, y_log_pred)
+roc_lr = roc_auc_score(y_test, y_log_pred)
+
 print("Brier Score Loss:")
-print(brier_score_loss(y_test, y_log_pred))
+print(breier_score_lr)
 print("Accuracy Score:")
-print(accuracy_score(y_test, y_log_pred))
+print(accuracy_score_lr)
 print("ROC AUC Score:")
-print(roc_auc_score(y_test, y_log_pred))
+print(roc_lr)
 
 # SHAP Analysis for logistic regression
 explainer = shap.LinearExplainer(logistic, X_train)