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app.py

@@ -2,7 +2,6 @@ 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.")
@@ -20,8 +19,8 @@ tenure = st.number_input("Tenure (Months with the company)", min_value=0, value=
 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 user input into a DataFrame
-customer_data = pd.DataFrame([{
+# Convert categorical inputs to match model training
+customer_data = {
     'SeniorCitizen': 1 if SeniorCitizen == "Yes" else 0,
     'Partner':Partner,
     'Dependents': Dependents,
@@ -32,10 +31,9 @@ customer_data = pd.DataFrame([{
     'PaymentMethod': PaymentMethod,
     'MonthlyCharges': MonthlyCharges,
     'TotalCharges': TotalCharges
-}])
+}
 
 
-# Single prediction section
 if st.button("Predict", type='primary'):
     response = requests.post("https://MainiSandeep1987-BackEndFlaskAPITelecomChurnPrediction.hf.space/v1/customer", json=customer_data)    # enter user name and space name before running the cell
     if response.status_code == 200:
@@ -45,69 +43,6 @@ if st.button("Predict", type='primary'):
     else:
         st.error("Error in API request")
 
-
-# Single prediction section
-# if st.button("Predict"):
-#     try:
-#         print(customer_data)
-#         response = requests.post(
-#             "https://MainiSandeep1987-BackEndFlaskAPITelecomChurnPrediction.hf.space/v1/customer",
-#             json=customer_data.to_dict(orient='records')[0]
-#         )  # Send data to Flask API
-#         response.raise_for_status()  # Raise an error if the request fails
-#         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}.")
-#     except requests.exceptions.RequestException as e:
-#         st.error(f"Error making prediction: {e}")
-        
-
-
-# # Function for handling retries with exponential backoff
-# def make_request_with_backoff(url, payload, max_retries=5):
-#     retry_delay = 2  # Initial retry delay (in seconds)
-
-#     for attempt in range(max_retries):
-#         try:
-#             response = requests.post(url, json=payload)
-#             response.raise_for_status()  # Ensure successful response
-
-#             return response.json()  # Return data if request succeeds
-
-#         except requests.exceptions.RequestException as e:
-#             if response.status_code == 429:  # Too many requests
-#                 st.warning(f"Rate limit exceeded. Retrying in {retry_delay} seconds...")
-#                 time.sleep(retry_delay)  # Wait before retrying
-#                 retry_delay *= 2  # Exponential backoff (2s, 4s, 8s, etc.)
-#             else:
-#                 st.error(f"Request failed: {e}")
-#                 return None
-
-#     st.error("Max retries reached. Try again later.")
-#     return None
-
-# # Single prediction section with retry mechanism
-# if st.button("Predict"):
-#     try:
-#         print(customer_data)  # Display input data for debugging
-        
-#         result = make_request_with_backoff(
-#             "https://MainiSandeep1987-BackEndFlaskAPITelecomChurnPrediction.hf.space/v1/customer",
-#             customer_data.to_dict(orient='records')[0]
-#         )
-
-#         if result:
-#             churn_prediction = result.get("Prediction", "Unknown")  # Safe key lookup
-#             customer_id = customer_data.get("CustomerID", "Unknown")  # Prevent undefined errors
-#             st.write(f"Based on the information provided, the customer with ID {customer_id} is likely to {churn_prediction}.")
-        
-#     except KeyError as e:
-#         st.error(f"Unexpected response format: Missing key {e}")
-#     except Exception as e:
-#         st.error(f"Unexpected error: {e}")
-
-
-
 # Batch Prediction
 st.subheader("Batch Prediction")
 

requirements.txt

@@ -1,3 +1,3 @@
 pandas==2.2.2
 requests==2.28.1
-streamlit==1.43.2
+streamlit==1.43.2