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app.py

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+import os
+import joblib
+import pandas as pd
+from imblearn.over_sampling import SMOTE
+from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
+import gradio as gr
+
+# Load models and preprocessor
+model_dir = 'models'
+data_dir = 'datasets'
+
+preprocessor_path = os.path.join(model_dir, 'churn_preprocessor.joblib')
+loaded_preprocessor = joblib.load(preprocessor_path)
+
+model_names = [
+    'Ada Boost Classifier',
+    'Extra Trees Classifier',
+    'Gradient Boosting Classifier',
+    'LGBM Classifier', 
+    'LogisticRegression',
+    'RandomForestClassifier'
+    'XGBoost Classifier', 
+]
+model_paths = {name: os.path.join(model_dir, f"{name.replace(' ', '')}.joblib") for name in model_names}
+
+# Load models safely
+models = {}
+for name, path in model_paths.items():
+    try:
+        models[name] = joblib.load(path)
+    except Exception as e:
+        print(f"Error loading model {name} from {path}: {str(e)}")
+
+# Load dataset
+data_path = os.path.join(data_dir, 'cleaned_IT_customer_churn.csv')
+df = pd.read_csv(data_path)
+
+# Prepare features and target
+X = df.drop(columns=['Churn'])
+y = df['Churn']
+
+# Predefined input choices
+input_choices = {
+    'gender': ['Female', 'Male'],
+    'internet_service': ['DSL', 'Fiber optic', 'No'],
+    'contract': ['Month-to-month', 'One year', 'Two year'],
+    'payment_method': ['Electronic check', 'Mailed check', 'Bank transfer (automatic)', 'Credit card (automatic)'],
+    'others' : ['No', 'Yes']
+}
+
+# Pre-computed statistics for default values
+stats = df[['tenure', 'MonthlyCharges', 'TotalCharges']].agg(['mean', 'max']).reset_index()
+means = stats.loc[0]
+maxs = stats.loc[1]
+
+# Metrics calculation function
+def calculate_metrics(y_true, y_pred):
+    return {
+        'Accuracy': accuracy_score(y_true, y_pred) * 100,
+        'Recall': recall_score(y_true, y_pred) * 100,
+        'F1 Score': f1_score(y_true, y_pred) * 100,
+        'Precision': precision_score(y_true, y_pred) * 100,
+    }
+
+# Prediction and metrics evaluation function
+def load_and_predict(
+        gender, internet_service, contract, payment_method, tenure, monthly_charges, total_charges, 
+        senior_citizen, partner, dependents, phone_service, multiple_lines, online_security, online_backup, 
+        device_protection, tech_support, streaming_tv, streaming_movies, paperless_billing):
+    
+    # Ensure inputs are not None
+    try:
+        sample = {
+            'gender': int(gender == 'Male'),
+            'SeniorCitizen': int(senior_citizen == 'Yes'),
+            'Partner': int(partner == 'Yes'),
+            'Dependents': int(dependents == 'Yes'),
+            'tenure': int(tenure),
+            'PhoneService': int(phone_service == 'Yes'),
+            'MultipleLines': int(multiple_lines == 'Yes'),
+            'InternetService': str(internet_service),
+            'OnlineSecurity': int(online_security == 'Yes'),
+            'OnlineBackup': int(online_backup == 'Yes'),
+            'DeviceProtection': int(device_protection == 'Yes'),
+            'TechSupport': int(tech_support == 'Yes'),
+            'StreamingTV': int(streaming_tv == 'Yes'),
+            'StreamingMovies': int(streaming_movies == 'Yes'),
+            'Contract': str(contract),
+            'PaperlessBilling': int(paperless_billing == 'Yes'),
+            'PaymentMethod': str(payment_method),
+            'MonthlyCharges': float(monthly_charges),
+            'TotalCharges': float(total_charges)
+        }
+        
+        sample_df = pd.DataFrame([sample])
+        sample_trans = loaded_preprocessor.transform(sample_df)
+        X_trans = loaded_preprocessor.transform(X)
+
+        # Using SMOTE to handle class imbalance
+        X_resampled, y_resampled = SMOTE(random_state=42).fit_resample(X_trans, y)
+
+        results = []
+        for name, model in models.items():
+            churn_pred = model.predict(sample_trans)
+            y_resampled_pred = model.predict(X_resampled)
+            metrics = calculate_metrics(y_resampled, y_resampled_pred)
+
+            results.append({
+                'Model': name,
+                'Predicted Churn': 'Yes' if churn_pred[0] == 1 else 'No',
+                **metrics,
+            })
+
+        return pd.DataFrame(results).sort_values(by='Accuracy', ascending=False).reset_index(drop=True)
+
+    except Exception as e:
+        return f"An error occurred during model loading or prediction: {str(e)}"
+    
+# Gradio Interface setup
+input_components = [
+    gr.Radio(label="Gender", choices=input_choices['gender'], value=input_choices['gender'][0]),
+    gr.Dropdown(label="Internet Service", choices=input_choices['internet_service'], value=input_choices['internet_service'][0]),
+    gr.Dropdown(label="Contract", choices=input_choices['contract'], value=input_choices['contract'][0]),
+    gr.Dropdown(label="Payment Method", choices=input_choices['payment_method'], value=input_choices['payment_method'][0]),
+    gr.Slider(label="Tenure (Months)", minimum=0, maximum=int(maxs['tenure'] * 1.5), value=int(means['tenure'])),
+    gr.Number(label="Monthly Charges", minimum=0.0, maximum=float(maxs['MonthlyCharges'] * 1.5), value=float(means['MonthlyCharges'])),
+    gr.Number(label="Total Charges", minimum=0.0, maximum=float(maxs['TotalCharges'] * 1.5), value=float(means['TotalCharges'])),
+    gr.Radio(label="Senior Citizen", choices=input_choices['others'], value=input_choices['others'][0]),
+    gr.Radio(label="Partner", choices=input_choices['others'], value=input_choices['others'][0]),
+    gr.Radio(label="Dependents", choices=input_choices['others'], value=input_choices['others'][0]),
+    gr.Radio(label="Phone Service", choices=input_choices['others'], value=input_choices['others'][0]),
+    gr.Radio(label="Multiple Lines", choices=input_choices['others'], value=input_choices['others'][0]),
+    gr.Radio(label="Online Security", choices=input_choices['others'], value=input_choices['others'][0]),
+    gr.Radio(label="Online Backup", choices=input_choices['others'], value=input_choices['others'][0]),
+    gr.Radio(label="Device Protection", choices=input_choices['others'], value=input_choices['others'][0]),
+    gr.Radio(label="Tech Support", choices=input_choices['others'], value=input_choices['others'][0]),
+    gr.Radio(label="Streaming TV", choices=input_choices['others'], value=input_choices['others'][0]),
+    gr.Radio(label="Streaming Movies", choices=input_choices['others'], value=input_choices['others'][0]),
+    gr.Radio(label="Paperless Billing", choices=input_choices['others'], value=input_choices['others'][0]),
+]
+
+output_component = gr.DataFrame()
+
+# Launching the Gradio Interface
+gr.Interface(
+    fn=load_and_predict, 
+    inputs=input_components,
+    outputs=output_component,
+    title="♻️ Customer Churn Prediction",
+    description="Enter the following information to predict customer churn.",
+    flagging_mode="never"  # Replacing allow_flagging with flagging_mode
+).launch()

requirements.txt

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+pandas
+numpy
+joblib
+scikit-learn
+imbalanced-learn
+lightgbm 
+xgboost
+catboost