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app.py

@@ -62,20 +62,20 @@ if st.button("Predict Customer Conversion"):
             "https://manasranjanpani-extraalearncustomerpredictionbackend.hf.space/v1/customers",
             json=input_data
         )
-        
+
         if response.status_code == 200:
             result = response.json()
             prediction = result['Predicted_Sales']
             st.success(f"Predicted Customer Conversion Score: {prediction:.2f}")
-            
+
             # Display additional info if available
             if 'input_received' in result:
                 st.info(f"Processed {len(result['input_received']['fields_processed'])} features successfully")
-                
+
         else:
             error_data = response.json()
             st.error(f"Error making prediction: {error_data.get('error', 'Unknown error')}")
-            
+
     except requests.exceptions.RequestException as e:
         st.error(f"Connection error: {e}")
     except Exception as e:
@@ -92,11 +92,11 @@ if uploaded_file is not None:
     st.write("Preview of uploaded data:")
     batch_data = pd.read_csv(uploaded_file)
     st.dataframe(batch_data.head())
-    
+
     # Check if required columns are present
     required_columns = list(input_data.keys())
     missing_columns = [col for col in required_columns if col not in batch_data.columns]
-    
+
     if missing_columns:
         st.warning(f"Missing required columns in CSV: {missing_columns}")
         st.info(f"Required columns: {required_columns}")
@@ -108,16 +108,16 @@ if uploaded_file is not None and st.button("Predict Batch"):
     try:
         # Reset file pointer to beginning
         uploaded_file.seek(0)
-        
+
         response = requests.post(
             "https://manasranjanpani-extraalearncustomerpredictionbackend.hf.space/v1/customersbatch",
             files={"file": uploaded_file}
         )
-        
+
         if response.status_code == 200:
             predictions = response.json()
             st.success("Batch predictions completed!")
-            
+
             # Display predictions in a nice format
             if 'predictions' in predictions:
                 st.write("Predictions:")
@@ -127,11 +127,11 @@ if uploaded_file is not None and st.button("Predict Batch"):
                 # If predictions are keyed by ID
                 predictions_df = pd.DataFrame(list(predictions.items()), columns=['Customer ID', 'Prediction'])
                 st.dataframe(predictions_df)
-                
+
         else:
             error_data = response.json()
             st.error(f"Error making batch prediction: {error_data.get('error', 'Unknown error')}")
-            
+
     except requests.exceptions.RequestException as e:
         st.error(f"Connection error: {e}")
     except Exception as e:
@@ -182,7 +182,7 @@ st.download_button(
 with st.expander("Required Fields Information"):
     st.write("""
     The following fields are required for prediction:
-    
+
     - **age**: Customer age (15-80)
     - **currentOccupation**: Student, Unemployed, Employed, Self-Employed, Professional
     - **firstInteraction**: How the customer first interacted with ExtraaLearn