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app.py

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+import pandas as pd
+import gradio as gr
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+from sklearn.ensemble import AdaBoostClassifier
+from PIL import Image
+import pickle
+# Load the dataset
+df = pd.read_csv("Telco_Customer.csv")
+
+# Separate the features and target variable
+X = df.drop("Churn", axis=1)
+y = df["Churn"]
+
+# Encode the categorical variables
+categorical_vars = X.select_dtypes(include="object").columns.tolist()
+encoders = {}
+for var in categorical_vars:
+    encoder = LabelEncoder()
+    X[var] = encoder.fit_transform(X[var])
+    encoders[var] = encoder
+
+# Split the data into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Define and train the AdaBoost classifier
+adaboost = AdaBoostClassifier()
+adaboost.fit(X_train, y_train)
+
+# Save the trained model using pickle
+model_filename = "adaboost_model.pkl"
+with open(model_filename, "wb") as file:
+    pickle.dump(adaboost, file)
+
+# Define prediction function using the loaded model
+def predict_churn(*data):
+    # Load the saved model
+    with open(model_filename, "rb") as file:
+        loaded_model = pickle.load(file)
+
+    # Encode the inputs
+    encoded_data = []
+    for i, var in enumerate(categorical_vars):
+        encoder = encoders[var]
+        encoded_value = encoder.transform([data[i]])[0]
+        encoded_data.append(encoded_value)
+
+    # Make predictions on the encoded data
+    encoded_df = pd.DataFrame([encoded_data], columns=X.columns)
+    prediction = loaded_model.predict(encoded_df)
+
+    # Save inputs and output to CSV
+    input_data = pd.DataFrame([data], columns=X.columns)
+    input_data["Churn Prediction"] = prediction[0]
+    input_data.to_csv("history.csv", index=False)
+
+    return "Churn Prediction: " + prediction[0]
+
+# Create the dropdown choices using raw data
+dropdown_choices = {}
+for var in categorical_vars:
+    dropdown_choices[var] = list(df[var].unique())
+    
+
+# Create the input interfaces using Gradio
+input_interfaces = [gr.inputs.Dropdown(choices=dropdown_choices[col], label=col) for col in X.columns]
+output_interface = gr.outputs.Textbox()
+
+# Create the Gradio interface
+iface = gr.Interface(fn=predict_churn, inputs=input_interfaces, outputs=output_interface, title = "Customer Churn Prediction App")
+
+
+# Run the Gradio interface
+iface.launch(share =True)