Update app.py

app.py

@@ -1,4 +1,4 @@
-from flask import Flask, render_template, request, redirect, url_for, flash, send_file
+from flask import Flask, render_template, request, redirect, url_for, print, send_file
 import os
 import pandas as pd
 from werkzeug.utils import secure_filename
@@ -142,12 +142,12 @@ def index():
 @app.route('/predict', methods=['POST'])
 def predict():
     if 'file' not in request.files:
-        flash('No file part', 'error')
+        print('No file part', 'error')
         return redirect(url_for('index'))
     
     file = request.files['file']
     if file.filename == '':
-        flash('No selected file', 'error')
+        print('No selected file', 'error')
         return redirect(url_for('index'))
     
     if file and allowed_file(file.filename):
@@ -162,13 +162,13 @@ def predict():
             else:
                 df = pd.read_excel(filepath)
         except Exception as e:
-            flash(f'Error reading file: {e}', 'error')
+            print(f'Error reading file: {e}', 'error')
             return redirect(url_for('index'))
         
         # Process the DataFrame and generate predictions and classification analysis.
         df_pred, dx_class = process_dataframe(df)
         if df_pred.empty:
-            flash("Processed prediction DataFrame is empty. Check the input file and processing logic.", "error")
+            print("Processed prediction DataFrame is empty. Check the input file and processing logic.", "error")
             return redirect(url_for('index'))
         
         # Save output files with a timestamp and unique id.
@@ -183,7 +183,7 @@ def predict():
         # Redirect to report view; default to prediction report, page 1.
         return redirect(url_for('report_view', report_type='pred', page=1))
     else:
-        flash('Invalid file type. Only CSV and Excel files are allowed.', 'error')
+        print('Invalid file type. Only CSV and Excel files are allowed.', 'error')
         return redirect(url_for('index'))
 
 def process_dataframe(df):
@@ -202,7 +202,7 @@ def process_dataframe(df):
             try:
                 df_pred[col] = loaded_label_encoder[col].transform(df_pred[col])
             except ValueError as e:
-                flash(f'Invalid value in column {col}: {e}', 'error')
+                print(f'Invalid value in column {col}: {e}', 'error')
                 return pd.DataFrame(), pd.DataFrame()
         
         # Update the classification DataFrame with the transformed prediction columns.
@@ -214,7 +214,7 @@ def process_dataframe(df):
             try:
                 df_class[col] = loaded_label_encoder[col].transform(df_class[col])
             except ValueError as e:
-                flash(f'Invalid value in column {col}: {e}', 'error')
+                print(f'Invalid value in column {col}: {e}', 'error')
                 return pd.DataFrame(), pd.DataFrame()
         
         # Convert both DataFrames to float.
@@ -287,7 +287,7 @@ def process_dataframe(df):
         # Final return with full data for pagination.
         return df_pred, dx.head(len(df_pred))
     except Exception as e:
-        flash(f'Error processing file: {e}', 'error')
+        print(f'Error processing file: {e}', 'error')
         return pd.DataFrame(), pd.DataFrame()
 
 # ------------------------------