Upload folder using huggingface_hub

app.py

@@ -0,0 +1,71 @@
+import joblib
+import pandas as pd
+from flask import Flask, request, jsonify
+
+# Initialize Flask app with a name
+cust_churn_predictor_api = Flask ("Customer Churn Predictor Week1")
+
+# Load the trained churn prediction model
+model = joblib.load ("churn_prediction_model_v2_0.joblib")
+
+# Define a route for the home page
+@cust_churn_predictor_api.get ('/')
+def home ():
+    return "Welcome to the Customer Churn Prediction Week1 API!"
+
+# Define an endpoint to predict churn for a single customer
+@cust_churn_predictor_api.post ('/v1/customer')
+def predict_churn ():
+    # Get JSON data from the request
+    customer_data = request.get_json ()
+
+    # Extract relevant customer features from the input data
+    sample = {
+        'SeniorCitizen'   : customer_data ['SeniorCitizen'],
+        'tenure'          : customer_data ['tenure'],
+        'MonthlyCharges'  : customer_data ['MonthlyCharges'],
+        'TotalCharges'    : customer_data ['TotalCharges'],
+        'Partner'         : customer_data ['Partner'],
+        'Dependents'      : customer_data ['Dependents'],
+        'PhoneService'    : customer_data ['PhoneService'],
+        'InternetService' : customer_data ['InternetService'],
+        'Contract'        : customer_data ['Contract'],
+        'PaymentMethod'   : customer_data ['PaymentMethod']
+    }
+
+    # Convert the extracted data into a DataFrame
+    input_data = pd.DataFrame ([sample])
+
+    # Make a churn prediction using the trained model
+    prediction = model.predict (input_data).tolist ()[0]
+
+    # Map prediction result to a human-readable label
+    prediction_label = "churn" if prediction == 1 else "not churn"
+
+    # Return the prediction as a JSON response
+    return jsonify ({'Prediction': prediction_label})
+
+# Define an endpoint to predict churn for a batch of customers
+@cust_churn_predictor_api.post ('/v1/customerbatch')
+def predict_churn_batch ():
+    # Get the uploaded CSV file from the request
+    file = request.files ['file']
+
+    # Read the file into a DataFrame
+    input_data = pd.read_csv (file)
+
+    # Make predictions for the batch data and convert raw predictions into a readable format
+    predictions = [
+        'Churn' if x == 1
+        else "Not Churn"
+        for x in model.predict (input_data.drop ("customerID",axis=1)).tolist ()
+    ]
+
+    cust_id_list = input_data.CustomerId.values.tolist ()
+    output_dict = dict(zip (cust_id_list, predictions))
+
+    return output_dict  
+
+# Run the Flask app in debug mode
+if __name__ == '__main__':
+    app.run(debug=True)