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app.py

@@ -1,58 +1,73 @@
-import streamlit as st
-import pandas as pd
-import requests
-
-# Streamlit UI for Customer Churn Prediction
-st.title("Telecom Customer Churn Prediction App")
-st.write("This tool predicts customer churn risk based on their details. Enter the required information below.")
-
-# Collect user input based on dataset columns
-CustomerID = st.number_input("Customer ID", min_value=10000000, max_value=99999999)
-SeniorCitizen = st.selectbox("Senior citizen", ["Yes", "No"])
-Partner = st.selectbox("Does the customer have a partner?", ["Yes", "No"])
-Dependents = st.selectbox("Does the customer have dependents?", ["Yes", "No"])
-PhoneService = st.selectbox("Does the customer have phone service?", ["Yes", "No"])
-InternetService = st.selectbox("Type of Internet Service", ["DSL", "Fiber optic", "No"])
-Contract = st.selectbox("Type of Contract", ["Month-to-month", "One year", "Two year"])
-PaymentMethod = st.selectbox("Payment Method", ["Electronic check", "Mailed check", "Bank transfer", "Credit card"])
-tenure = st.number_input("Tenure (Months with the company)", min_value=0, value=12)
-MonthlyCharges = st.number_input("Monthly Charges", min_value=0.0, value=50.0)
-TotalCharges = st.number_input("Total Charges", min_value=0.0, value=600.0)
-
-# Convert categorical inputs to match model training
-customer_data = {
-    'SeniorCitizen': 1 if SeniorCitizen == "Yes" else 0,
-    'Partner':Partner,
-    'Dependents': Dependents,
-    'tenure': tenure,
-    'PhoneService': PhoneService,
-    'InternetService': InternetService,
-    'Contract': Contract,
-    'PaymentMethod': PaymentMethod,
-    'MonthlyCharges': MonthlyCharges,
-    'TotalCharges': TotalCharges
-}
-
-
-if st.button("Predict", type='primary'):
-    response = requests.post("https://MainiSandeep1987-FLASK_API_Telecom_Churn_Prediction.hf.space/v1/customer", json=customer_data)    # enter user name and space name before running the cell
-    if response.status_code == 200:
-        result = response.json()
-        churn_prediction = result["Prediction"]  # Extract only the value
-        st.write(f"Based on the information provided, the customer with ID {CustomerID} is likely to {churn_prediction}.")
-    else:
-        st.error("Error in API request")
-
-# Batch Prediction
-st.subheader("Batch Prediction")
-
-file = st.file_uploader("Upload CSV file", type=["csv"])
-if file is not None:
-    if st.button("Predict for Batch", type='primary'):
-        response = requests.post("https://MainiSandeep1987-FLASK_API_Telecom_Churn_Prediction.hf.space/v1/customerbatch", files={"file": file})    # enter user name and space name before running the cell
-        if response.status_code == 200:
-            result = response.json()
-            st.header("Batch Prediction Results")
-            st.write(result)
-        else:
-            st.error("Error in API request")
+import joblib
+import pandas as pd  ## To Read around the CSV Files.
+from flask import Flask, request, jsonify  ## Fask to create the backwend API using FLASK, request to use and cater the influx requests,\
+                                           ## and jsonify to convert the python object to JSON String,as Python 
+                                           ## object CANNOT be transmitted over HTTP CALLS.  
+
+# Initialize Flask app with a name
+app = Flask("Telecom Customer Churn Predictor")
+
+# Load the trained churn prediction model
+model = joblib.load("churn_prediction_model_v1_0.joblib")
+
+# Define a route for the home page
+@app.get('/')
+def home():
+    return "Welcome to the Telecom Customer Churn Prediction API"
+
+# Define an endpoint to predict churn for a single customer
+@app.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'],
+        'Partner': customer_data['Partner'],
+        'Dependents': customer_data['Dependents'],
+        'tenure': customer_data['tenure'],
+        'PhoneService': customer_data['PhoneService'],
+        'InternetService': customer_data['InternetService'],
+        'Contract': customer_data['Contract'],
+        'PaymentMethod': customer_data['PaymentMethod'],
+        'MonthlyCharges': customer_data['MonthlyCharges'],
+        'TotalCharges': customer_data['TotalCharges']
+    }
+
+    # 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
+@app.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)

requirements.txt

@@ -1,3 +1,10 @@
 pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+joblib==1.4.2
+Werkzeug==2.2.2
+flask==2.2.2
+gunicorn==20.1.0
 requests==2.28.1
-streamlit==1.43.2
+uvicorn[standard]