Delete app (1).py

app (1).py

@@ -1,160 +0,0 @@
-import pandas as pd
-import numpy as np
-import joblib
-import gradio as gr
-
-
-# Load the preprocessing steps and the model
-label_encoders = joblib.load("label_encoders.pkl")
-one_hot_encoder = joblib.load("one_hot_encoder.pkl")
-min_max_scaler = joblib.load("min_max_scaler.pkl")
-model = joblib.load("logistic_regression_model.pkl")
-le_target = joblib.load("label_encoder_target.pkl")
-
-
-def preprocess_data(data):
-
-    df = pd.DataFrame([data])
-
-    label_encode_cols = [
-        "Partner",
-        "Dependents",
-        "PhoneService",
-        "PaperlessBilling",
-        "gender",
-    ]
-    one_hot_encode_cols = [
-        "MultipleLines",
-        "InternetService",
-        "OnlineSecurity",
-        "OnlineBackup",
-        "DeviceProtection",
-        "TechSupport",
-        "StreamingTV",
-        "StreamingMovies",
-        "Contract",
-        "PaymentMethod",
-    ]
-    min_max_scale_cols = ["tenure", "MonthlyCharges", "TotalCharges"]
-
-    # Strip leading and trailing spaces from string inputs
-    for col in label_encode_cols + one_hot_encode_cols:
-        df[col] = df[col].str.strip()
-
-    # Convert non-numeric values to NaN and fill them with the mean of the column
-    df[min_max_scale_cols] = df[min_max_scale_cols].replace(" ", np.nan).astype(float)
-
-    df[min_max_scale_cols] = df[min_max_scale_cols].fillna(
-        df[min_max_scale_cols].mean()
-    )
-
-    # Label encode specified columns
-    for col in label_encode_cols:
-        le = label_encoders[col]
-        df[col] = le.transform(df[col])
-
-    # One-hot encode specified columns
-    one_hot_encoded = one_hot_encoder.transform(df[one_hot_encode_cols])
-
-    # Min-max scale specified columns
-    scaled_numerical = min_max_scaler.transform(df[min_max_scale_cols])
-
-    # Combine processed columns into one DataFrame
-    X_processed = np.hstack(
-        (df[label_encode_cols].values, scaled_numerical, one_hot_encoded)
-    )
-
-    return X_processed
-
-
-def predict(
-    gender,
-    senior_citizen,
-    partner,
-    dependents,
-    tenure,
-    phone_service,
-    multiple_lines,
-    internet_service,
-    online_security,
-    online_backup,
-    device_protection,
-    tech_support,
-    streaming_tv,
-    streaming_movies,
-    contract,
-    paperless_billing,
-    payment_method,
-    monthly_charges,
-    total_charges,
-):
-
-    data = {
-        "gender": gender,
-        "SeniorCitizen": senior_citizen,
-        "Partner": partner,
-        "Dependents": dependents,
-        "tenure": tenure,
-        "PhoneService": phone_service,
-        "MultipleLines": multiple_lines,
-        "InternetService": internet_service,
-        "OnlineSecurity": online_security,
-        "OnlineBackup": online_backup,
-        "DeviceProtection": device_protection,
-        "TechSupport": tech_support,
-        "StreamingTV": streaming_tv,
-        "StreamingMovies": streaming_movies,
-        "Contract": contract,
-        "PaperlessBilling": paperless_billing,
-        "PaymentMethod": payment_method,
-        "MonthlyCharges": monthly_charges,
-        "TotalCharges": total_charges,
-    }
-
-    try:
-        X_new = preprocess_data(data)
-        prediction = model.predict(X_new)
-        prediction = le_target.inverse_transform(prediction)
-        return "Churn" if prediction[0] == "Yes" else "No Churn"
-    except Exception as e:
-        print("Error during prediction:", e)
-        return str(e)
-
-
-# Define the Gradio interface
-inputs = [
-    gr.Radio(label="Gender", choices=["Female", "Male"]),
-    gr.Number(label="Senior Citizen (0 or 1)"),
-    gr.Radio(label="Partner", choices=["Yes", "No"]),
-    gr.Radio(label="Dependents", choices=["Yes", "No"]),
-    gr.Number(label="Tenure (integer)"),
-    gr.Radio(label="Phone Service", choices=["Yes", "No"]),
-    gr.Radio(label="Multiple Lines", choices=["Yes", "No", "No phone service"]),
-    gr.Radio(label="Internet Service", choices=["DSL", "Fiber optic", "No"]),
-    gr.Radio(label="Online Security", choices=["Yes", "No", "No internet service"]),
-    gr.Radio(label="Online Backup", choices=["Yes", "No", "No internet service"]),
-    gr.Radio(label="Device Protection", choices=["Yes", "No", "No internet service"]),
-    gr.Radio(label="Tech Support", choices=["Yes", "No", "No internet service"]),
-    gr.Radio(label="Streaming TV", choices=["Yes", "No", "No internet service"]),
-    gr.Radio(label="Streaming Movies", choices=["Yes", "No", "No internet service"]),
-    gr.Radio(label="Contract", choices=["Month-to-month", "One year", "Two year"]),
-    gr.Radio(label="Paperless Billing", choices=["Yes", "No"]),
-    gr.Radio(
-        label="Payment Method",
-        choices=[
-            "Electronic check",
-            "Mailed check",
-            "Bank transfer (automatic)",
-            "Credit card (automatic)",
-        ],
-    ),
-    gr.Number(label="Monthly Charges (float)"),
-    gr.Number(label="Total Charges (float)"),
-]
-
-outputs = gr.Textbox(label="Prediction")
-
-# Create the Gradio interface
-gr.Interface(
-    fn=predict, inputs=inputs, outputs=outputs, title="Churn Prediction Model"
-).launch(share=True)