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app.py

@@ -33,15 +33,32 @@ customer_data = {
     '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:
+#         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")
 
-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:
+
+# Single prediction section
+if st.button("Predict"):
+    try:
+        print(customer_data)
+        response = requests.post(
+            "https://MainiSandeep1987-BackEndFlaskAPITelecomChurnPrediction.hf.space/v1/customer",
+            json=input_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}.")
-    else:
-        st.error("Error in API request")
+    except requests.exceptions.RequestException as e:
+        st.error(f"Error making prediction: {e}")
+        
 
 # Batch Prediction
 st.subheader("Batch Prediction")