Rename app2.py to app.py

app2.py → app.py

@@ -1,283 +1,284 @@
-from flask import Flask, render_template, request, redirect, url_for, flash, send_file
-import os
-import pandas as pd
-from werkzeug.utils import secure_filename
-from joblib import load
-import numpy as np
-from sklearn.preprocessing import OneHotEncoder, LabelEncoder
-from sklearn.model_selection import train_test_split
-from sklearn.preprocessing import StandardScaler
-from sklearn.decomposition import PCA
-from sklearn.pipeline import Pipeline
-from sklearn.tree import DecisionTreeRegressor
-from sklearn.ensemble import RandomForestRegressor
-from sklearn.linear_model import LinearRegression
-from xgboost import XGBRegressor
-from sklearn.neighbors import KNeighborsRegressor
-from sklearn.model_selection import cross_val_score
-from sklearn.metrics import mean_squared_error
-from sklearn import metrics
-from sklearn.metrics.pairwise import cosine_similarity
-from time import time
-
-app = Flask(__name__)
-
-# Set the secret key for session management
-app.secret_key = os.urandom(24)
-
-# Configurations
-UPLOAD_FOLDER = "uploads/"
-DATA_FOLDER = "data/"
-
-# Define the model directory (ensuring correct path formatting)
-MODEL_DIR = r'.\Model'
-LABEL_ENOCDER_DIR = r'.\Label_encoders'
-
-# Define the output file path
-PRED_OUTPUT_FILE = "data/pred_output.csv"
-CLASS_OUTPUT_FILE = "data/class_output.csv"
-
-ALLOWED_EXTENSIONS = {'csv', 'xlsx'}
-
-app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
-
-# Ensure the upload folder exists
-os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
-
-# Load models using os.path.join for better cross-platform compatibility
-
-# linear_regression_model
-gia_model = load(os.path.join(MODEL_DIR, 'linear_regression_model_gia_price.joblib'))
-grade_model = load(os.path.join(MODEL_DIR, 'linear_regression_model_grade_price.joblib'))
-bygrade_model = load(os.path.join(MODEL_DIR, 'linear_regression_model_bygrade_price.joblib'))
-makable_model = load(os.path.join(MODEL_DIR, 'linear_regression_model_makable_price.joblib'))
-
-# classifier_model
-col_model = load(os.path.join(MODEL_DIR, 'classification_LogisticRegression_col.joblib'))
-cts_model = load(os.path.join(MODEL_DIR, 'classification_LogisticRegression_cts.joblib'))
-cut_model = load(os.path.join(MODEL_DIR, 'classification_LogisticRegression_cut.joblib'))
-qua_model = load(os.path.join(MODEL_DIR, 'classification_LogisticRegression_qua.joblib'))
-shp_model = load(os.path.join(MODEL_DIR, 'classification_LogisticRegression_shp.joblib'))
-
-# print("===================================models==================================")
-# print(gia_model)
-# print(grade_model)  
-# print(bygrade_model)
-# print(makable_model)
-
-# Load label encoders
-encoder_list = ['Tag', 'EngShp', 'EngQua', 'EngCol', 'EngCut', 'EngPol', 'EngSym', 'EngFlo', 'EngNts', 'EngMikly', 'EngLab',
-                'Change_cts_value', 'Change_shape_value', 'Change_quality_value', 'Change_color_value', 'Change_cut_value']
-#loaded_label_encoder = {val: load(f"./Label_encoders/label_encoder_{val}.joblib") for val in encoder_list}
-loaded_label_encoder = {}
-for val in encoder_list:
-    #encoder_path = f"H:/DEV PATEL/2025/AI_In_Diamond_Industry/Label_encoders/label_encoder_{val}.joblib"
-    encoder_path = os.path.join(LABEL_ENOCDER_DIR, f"label_encoder_{val}.joblib")
-    loaded_label_encoder[val] = load(encoder_path)
-    
-# print(loaded_label_encoder)
-
-# Ensure upload folder exists
-os.makedirs(UPLOAD_FOLDER, exist_ok=True)
-
-def allowed_file(filename):
-    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
-
-@app.route('/')
-def index():
-    return render_template('index.html')
-
-@app.route('/predict', methods=['POST'])
-def predict():
-    if 'file' not in request.files:
-        flash('No file part', 'error')
-        return redirect(request.url)
-    
-    file = request.files['file']
-    if file.filename == '':
-        flash('No selected file', 'error')
-        return redirect(request.url)
-    
-    if file and allowed_file(file.filename):
-        filename = secure_filename(file.filename)
-        filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
-        file.save(filepath)
-        
-        # Convert to DataFrame
-        if filename.endswith('.csv'):
-            df = pd.read_csv(filepath)
-        else:
-            df = pd.read_excel(filepath)
-        
-        # Preprocess DataFrame
-        print("===================================process_dataframe=0==================================")
-        df,dx = process_dataframe(df)
-        print("===================================process_dataframe=5==================================")
-        return render_template('output.html', df=df.to_html(), dx=dx.to_html())
-    else:
-        flash('Invalid file type. Only CSV and Excel files are allowed.', 'error')
-        print('Invalid file type. Only CSV and Excel files are allowed.')
-        return redirect(request.url)
-
-def process_dataframe(df):
-    try:
-        print("===================================process_dataframe=1==================================")
-        # 'EngLab' is not in the required columns
-        required_columns = ['Tag', 'EngCts', 'EngShp', 'EngQua', 'EngCol', 'EngCut', 'EngPol',
-                            'EngSym', 'EngFlo', 'EngNts', 'EngMikly', 'EngAmt']
-        
-        # for prediction
-        df = df[required_columns]
-        df = df.copy()
-        # for classification
-        
-        
-        # df[col] = df[col].map(lambda x: loaded_label_encoder[col].transform([x])[0] if x in loaded_label_encoder[col].classes_ else np.nan)
-
-        # Transform categorical features using loaded label encoders
-        df["Tag"] = loaded_label_encoder['Tag'].transform(df["Tag"])
-        df["EngShp"] = loaded_label_encoder['EngShp'].transform(df["EngShp"]) 
-        df["EngQua"] = loaded_label_encoder['EngQua'].transform(df["EngQua"])
-        df["EngCol"] = loaded_label_encoder['EngCol'].transform(df["EngCol"])
-        df["EngCut"] = loaded_label_encoder['EngCut'].transform(df["EngCut"])
-        df["EngPol"] = loaded_label_encoder['EngPol'].transform(df["EngPol"])
-        df["EngSym"] = loaded_label_encoder['EngSym'].transform(df["EngSym"])
-        df["EngFlo"] = loaded_label_encoder['EngFlo'].transform(df["EngFlo"])
-        df["EngNts"] = loaded_label_encoder['EngNts'].transform(df["EngNts"])
-        df["EngMikly"] = loaded_label_encoder['EngMikly'].transform(df["EngMikly"])
-        #EngLab = loaded_label_encoder['EngLab'].transform(df[EngLab])
-        
-        df=df.astype(float)
-        print(df.head())  
-          
-        dx = df.copy()
-        
-        print(df.columns)
-        x= df.copy()
-        
-        # print("Model expects", gia_model.n_features_in_, "features.")
-        # print("X_features shape:", x.shape)
-        
-        print("===================================process_dataframe=2==================================")
-        
-        # ================================================================================================
-        # Prediction report
-        # ================================================================================================
-        
-        # Predict prices
-        df['GIA_Predicted'] = gia_model.predict(x)
-        df['Grade_Predicted'] = grade_model.predict(x)
-        df['ByGrade_Predicted'] = bygrade_model.predict(x)
-        df['Makable_Predicted'] = makable_model.predict(x)
-                            
-        
-        # Compute differences
-        df['GIA_Diff'] = df['EngAmt'] - df['GIA_Predicted']
-        df['Grade_Diff'] = df['EngAmt'] - df['Grade_Predicted']
-        df['ByGrade_Diff'] = df['EngAmt'] - df['ByGrade_Predicted']
-        df['Makable_Diff'] = df['EngAmt'] - df['Makable_Predicted']
-        
-        print(df.head())
-        
-        predictions = df.to_dict(orient='records')
-        analysis = df.describe().to_html()
-        #print(analysis)
-        #print(predictions)
-        print("===================================process_dataframe=3==================================")
-        
-        # ================================================================================================
-        # Classification report
-        # ================================================================================================      
-                      
-        dx['col_change'] = col_model.predict(x)
-        dx['cts_change'] = cts_model.predict(x)
-        dx['cut_change'] = cut_model.predict(x)
-        dx['qua_change'] = qua_model.predict(x)
-        dx['shp_change'] = shp_model.predict(x)
-        
-        # Inverse transform the predictions
-        dx['col_change'] = loaded_label_encoder['Change_color_value'].inverse_transform(dx['col_change'])
-        dx['cts_change'] = loaded_label_encoder['Change_cts_value'].inverse_transform(dx['cts_change'])
-        dx['cut_change'] = loaded_label_encoder['Change_cut_value'].inverse_transform(dx['cut_change'])
-        dx['qua_change'] = loaded_label_encoder['Change_quality_value'].inverse_transform(dx['qua_change'])
-        dx['shp_change'] = loaded_label_encoder['Change_shape_value'].inverse_transform(dx['shp_change'])   
-        
-        print(dx.head())     
-        
-        print("===================================process_dataframe=4==================================")
-        
-        # Save output file with date and time
-        time = str(pd.Timestamp.now().strftime("%Y-%m-%d"))
-        
-        #saving the output file
-        global PRED_OUTPUT_FILE
-        PRED_OUTPUT_FILE = f'data/prediction_output_{time}.csv'
-        df.to_csv(PRED_OUTPUT_FILE, index=False)
-        
-        #saving the output file
-        global CLASS_OUTPUT_FILE
-        CLASS_OUTPUT_FILE = f'data/classification_output_{time}.csv'
-        dx.to_csv(CLASS_OUTPUT_FILE, index=False)
-        
-        print("===================================Output file saved as output.csv===================================")
-        
-        return df.head(), dx.head()
-    except Exception as e:
-        print(f'Error processing file: {e}')
-        flash(f'Error processing file: {e}', 'error')
-        return pd.DataFrame(), pd.DataFrame()
-    
-def classification_report(df):
-    try:
-        classifcation_data = df[["EngGraphCts","EngCts","EngShp","EngQua","EngCol","EngCut","EngPol","EngSym","EngFlo","EngNts","EngMikly","EngLab","EngAmt",
-                                 "MkblCts","MkblShp","MkblQua","MkblCol","MkblCut","MkblPol","MkblSym","MkblFlo","MkblNts","MkblMikly","MkblLab","MkblAmt"]] 
-        
-        # Make predictions
-        classifcation_data["Cts_diff_eng_mkbl"] = round(classifcation_data["EngCts"] - classifcation_data["MkblCts"],2)
-
-        # Create a new column 'Change_Label' based on the values in 'Cts_diff_eng_mkbl'
-        classifcation_data['Change_cts_value'] = classifcation_data['Cts_diff_eng_mkbl'].apply(
-            lambda x: str(x)+' negative change' if x < 0 else (str(x)+' positive change' if x > 0 else 'no change')
-        )
-        
-        # Create a new column 'Shape_Change' based on the values in 'EngShp' and 'MkblShp'
-        classifcation_data['Change_shape_value'] = classifcation_data.apply(
-            lambda row: str(row['EngShp'])+' to '+str(row['MkblShp'])+' shape change' if row['EngShp'] != row['MkblShp'] else 'shape not change', axis=1
-        )
-        
-        # Create a new column 'quality_Change' based on the values in 'EngQua' and 'MkblQua'
-        classifcation_data['Change_quality_value'] = classifcation_data.apply(
-            lambda row: str(row['EngQua'])+' to '+str(row['MkblQua'])+' quality change' if row['EngQua'] != row['MkblQua'] else 'quality not change', axis=1
-        )
-        
-        # Create a new column 'color_Change' based on the values in 'EngCol' and 'MkblCol'
-        classifcation_data['Change_color_value'] = classifcation_data.apply(
-            lambda row: str(row['EngCol'])+' to '+str(row['MkblCol'])+' color change' if row['EngCol'] != row['MkblCol'] else 'color not change', axis=1
-        )
-        
-        # Create a new column 'cut_Change' based on the values in 'EngCut' and 'MkblCut'
-        classifcation_data['Change_cut_value'] = classifcation_data.apply(
-            lambda row: str(row['EngCut'])+' to '+str(row['MkblCut'])+' cut change' if row['EngCut'] != row['MkblCut'] else 'cut not change', axis=1
-        )
-        
-        # Generate classification report
-        
-        
-        return classifcation_data
-    except Exception as e:
-        flash(f'Error generating classification report: {e}', 'error')
-        print(f'Error generating classification report: {e}')
-        return None
-    
-@app.route('/download_pred', methods=['GET'])
-def download_pred():
-    """Serve the output.csv file for download."""
-    return send_file(PRED_OUTPUT_FILE, as_attachment=True)  
-
-@app.route('/download_class', methods=['GET'])
-def download_class():
-    """Serve the output.csv file for download."""
-    return send_file(CLASS_OUTPUT_FILE, as_attachment=True)   
-
-if __name__ == "__main__":
+from flask import Flask, render_template, request, redirect, url_for, flash, send_file
+import os
+import pandas as pd
+from werkzeug.utils import secure_filename
+from joblib import load
+import numpy as np
+from sklearn.preprocessing import OneHotEncoder, LabelEncoder
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.decomposition import PCA
+from sklearn.pipeline import Pipeline
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.linear_model import LinearRegression
+from xgboost import XGBRegressor
+from sklearn.neighbors import KNeighborsRegressor
+from sklearn.model_selection import cross_val_score
+from sklearn.metrics import mean_squared_error
+from sklearn import metrics
+from sklearn.metrics.pairwise import cosine_similarity
+from time import time
+
+app = Flask(__name__)
+
+# Set the secret key for session management
+app.secret_key = os.urandom(24)
+
+# Configurations
+UPLOAD_FOLDER = "uploads/"
+DATA_FOLDER = "data/"
+
+# Define the model directory (ensuring correct path formatting)
+MODEL_DIR = r'.\Model'
+LABEL_ENOCDER_DIR = r'.\Label_encoders'
+
+# Define the output file path
+PRED_OUTPUT_FILE = "data/pred_output.csv"
+CLASS_OUTPUT_FILE = "data/class_output.csv"
+
+ALLOWED_EXTENSIONS = {'csv', 'xlsx'}
+
+app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
+
+# Ensure the upload folder exists
+os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
+os.makedirs(app.config['DATA_FOLDER'], exist_ok=True)
+
+# Load models using os.path.join for better cross-platform compatibility
+
+# linear_regression_model
+gia_model = load(os.path.join(MODEL_DIR, 'linear_regression_model_gia_price.joblib'))
+grade_model = load(os.path.join(MODEL_DIR, 'linear_regression_model_grade_price.joblib'))
+bygrade_model = load(os.path.join(MODEL_DIR, 'linear_regression_model_bygrade_price.joblib'))
+makable_model = load(os.path.join(MODEL_DIR, 'linear_regression_model_makable_price.joblib'))
+
+# classifier_model
+col_model = load(os.path.join(MODEL_DIR, 'classification_LogisticRegression_col.joblib'))
+cts_model = load(os.path.join(MODEL_DIR, 'classification_LogisticRegression_cts.joblib'))
+cut_model = load(os.path.join(MODEL_DIR, 'classification_LogisticRegression_cut.joblib'))
+qua_model = load(os.path.join(MODEL_DIR, 'classification_LogisticRegression_qua.joblib'))
+shp_model = load(os.path.join(MODEL_DIR, 'classification_LogisticRegression_shp.joblib'))
+
+# print("===================================models==================================")
+# print(gia_model)
+# print(grade_model)  
+# print(bygrade_model)
+# print(makable_model)
+
+# Load label encoders
+encoder_list = ['Tag', 'EngShp', 'EngQua', 'EngCol', 'EngCut', 'EngPol', 'EngSym', 'EngFlo', 'EngNts', 'EngMikly', 'EngLab',
+                'Change_cts_value', 'Change_shape_value', 'Change_quality_value', 'Change_color_value', 'Change_cut_value']
+#loaded_label_encoder = {val: load(f"./Label_encoders/label_encoder_{val}.joblib") for val in encoder_list}
+loaded_label_encoder = {}
+for val in encoder_list:
+    #encoder_path = f"H:/DEV PATEL/2025/AI_In_Diamond_Industry/Label_encoders/label_encoder_{val}.joblib"
+    encoder_path = os.path.join(LABEL_ENOCDER_DIR, f"label_encoder_{val}.joblib")
+    loaded_label_encoder[val] = load(encoder_path)
+    
+# print(loaded_label_encoder)
+
+# Ensure upload folder exists
+os.makedirs(UPLOAD_FOLDER, exist_ok=True)
+
+def allowed_file(filename):
+    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    if 'file' not in request.files:
+        flash('No file part', 'error')
+        return redirect(request.url)
+    
+    file = request.files['file']
+    if file.filename == '':
+        flash('No selected file', 'error')
+        return redirect(request.url)
+    
+    if file and allowed_file(file.filename):
+        filename = secure_filename(file.filename)
+        filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
+        file.save(filepath)
+        
+        # Convert to DataFrame
+        if filename.endswith('.csv'):
+            df = pd.read_csv(filepath)
+        else:
+            df = pd.read_excel(filepath)
+        
+        # Preprocess DataFrame
+        print("===================================process_dataframe=0==================================")
+        df,dx = process_dataframe(df)
+        print("===================================process_dataframe=5==================================")
+        return render_template('output.html', df=df.to_html(), dx=dx.to_html())
+    else:
+        flash('Invalid file type. Only CSV and Excel files are allowed.', 'error')
+        print('Invalid file type. Only CSV and Excel files are allowed.')
+        return redirect(request.url)
+
+def process_dataframe(df):
+    try:
+        print("===================================process_dataframe=1==================================")
+        # 'EngLab' is not in the required columns
+        required_columns = ['Tag', 'EngCts', 'EngShp', 'EngQua', 'EngCol', 'EngCut', 'EngPol',
+                            'EngSym', 'EngFlo', 'EngNts', 'EngMikly', 'EngAmt']
+        
+        # for prediction
+        df = df[required_columns]
+        df = df.copy()
+        # for classification
+        
+        
+        # df[col] = df[col].map(lambda x: loaded_label_encoder[col].transform([x])[0] if x in loaded_label_encoder[col].classes_ else np.nan)
+
+        # Transform categorical features using loaded label encoders
+        df["Tag"] = loaded_label_encoder['Tag'].transform(df["Tag"])
+        df["EngShp"] = loaded_label_encoder['EngShp'].transform(df["EngShp"]) 
+        df["EngQua"] = loaded_label_encoder['EngQua'].transform(df["EngQua"])
+        df["EngCol"] = loaded_label_encoder['EngCol'].transform(df["EngCol"])
+        df["EngCut"] = loaded_label_encoder['EngCut'].transform(df["EngCut"])
+        df["EngPol"] = loaded_label_encoder['EngPol'].transform(df["EngPol"])
+        df["EngSym"] = loaded_label_encoder['EngSym'].transform(df["EngSym"])
+        df["EngFlo"] = loaded_label_encoder['EngFlo'].transform(df["EngFlo"])
+        df["EngNts"] = loaded_label_encoder['EngNts'].transform(df["EngNts"])
+        df["EngMikly"] = loaded_label_encoder['EngMikly'].transform(df["EngMikly"])
+        #EngLab = loaded_label_encoder['EngLab'].transform(df[EngLab])
+        
+        df=df.astype(float)
+        print(df.head())  
+          
+        dx = df.copy()
+        
+        print(df.columns)
+        x= df.copy()
+        
+        # print("Model expects", gia_model.n_features_in_, "features.")
+        # print("X_features shape:", x.shape)
+        
+        print("===================================process_dataframe=2==================================")
+        
+        # ================================================================================================
+        # Prediction report
+        # ================================================================================================
+        
+        # Predict prices
+        df['GIA_Predicted'] = gia_model.predict(x)
+        df['Grade_Predicted'] = grade_model.predict(x)
+        df['ByGrade_Predicted'] = bygrade_model.predict(x)
+        df['Makable_Predicted'] = makable_model.predict(x)
+                            
+        
+        # Compute differences
+        df['GIA_Diff'] = df['EngAmt'] - df['GIA_Predicted']
+        df['Grade_Diff'] = df['EngAmt'] - df['Grade_Predicted']
+        df['ByGrade_Diff'] = df['EngAmt'] - df['ByGrade_Predicted']
+        df['Makable_Diff'] = df['EngAmt'] - df['Makable_Predicted']
+        
+        print(df.head())
+        
+        predictions = df.to_dict(orient='records')
+        analysis = df.describe().to_html()
+        #print(analysis)
+        #print(predictions)
+        print("===================================process_dataframe=3==================================")
+        
+        # ================================================================================================
+        # Classification report
+        # ================================================================================================      
+                      
+        dx['col_change'] = col_model.predict(x)
+        dx['cts_change'] = cts_model.predict(x)
+        dx['cut_change'] = cut_model.predict(x)
+        dx['qua_change'] = qua_model.predict(x)
+        dx['shp_change'] = shp_model.predict(x)
+        
+        # Inverse transform the predictions
+        dx['col_change'] = loaded_label_encoder['Change_color_value'].inverse_transform(dx['col_change'])
+        dx['cts_change'] = loaded_label_encoder['Change_cts_value'].inverse_transform(dx['cts_change'])
+        dx['cut_change'] = loaded_label_encoder['Change_cut_value'].inverse_transform(dx['cut_change'])
+        dx['qua_change'] = loaded_label_encoder['Change_quality_value'].inverse_transform(dx['qua_change'])
+        dx['shp_change'] = loaded_label_encoder['Change_shape_value'].inverse_transform(dx['shp_change'])   
+        
+        print(dx.head())     
+        
+        print("===================================process_dataframe=4==================================")
+        
+        # Save output file with date and time
+        time = str(pd.Timestamp.now().strftime("%Y-%m-%d"))
+        
+        #saving the output file
+        global PRED_OUTPUT_FILE
+        PRED_OUTPUT_FILE = f'data/prediction_output_{time}.csv'
+        df.to_csv(PRED_OUTPUT_FILE, index=False)
+        
+        #saving the output file
+        global CLASS_OUTPUT_FILE
+        CLASS_OUTPUT_FILE = f'data/classification_output_{time}.csv'
+        dx.to_csv(CLASS_OUTPUT_FILE, index=False)
+        
+        print("===================================Output file saved as output.csv===================================")
+        
+        return df.head(), dx.head()
+    except Exception as e:
+        print(f'Error processing file: {e}')
+        flash(f'Error processing file: {e}', 'error')
+        return pd.DataFrame(), pd.DataFrame()
+    
+def classification_report(df):
+    try:
+        classifcation_data = df[["EngGraphCts","EngCts","EngShp","EngQua","EngCol","EngCut","EngPol","EngSym","EngFlo","EngNts","EngMikly","EngLab","EngAmt",
+                                 "MkblCts","MkblShp","MkblQua","MkblCol","MkblCut","MkblPol","MkblSym","MkblFlo","MkblNts","MkblMikly","MkblLab","MkblAmt"]] 
+        
+        # Make predictions
+        classifcation_data["Cts_diff_eng_mkbl"] = round(classifcation_data["EngCts"] - classifcation_data["MkblCts"],2)
+
+        # Create a new column 'Change_Label' based on the values in 'Cts_diff_eng_mkbl'
+        classifcation_data['Change_cts_value'] = classifcation_data['Cts_diff_eng_mkbl'].apply(
+            lambda x: str(x)+' negative change' if x < 0 else (str(x)+' positive change' if x > 0 else 'no change')
+        )
+        
+        # Create a new column 'Shape_Change' based on the values in 'EngShp' and 'MkblShp'
+        classifcation_data['Change_shape_value'] = classifcation_data.apply(
+            lambda row: str(row['EngShp'])+' to '+str(row['MkblShp'])+' shape change' if row['EngShp'] != row['MkblShp'] else 'shape not change', axis=1
+        )
+        
+        # Create a new column 'quality_Change' based on the values in 'EngQua' and 'MkblQua'
+        classifcation_data['Change_quality_value'] = classifcation_data.apply(
+            lambda row: str(row['EngQua'])+' to '+str(row['MkblQua'])+' quality change' if row['EngQua'] != row['MkblQua'] else 'quality not change', axis=1
+        )
+        
+        # Create a new column 'color_Change' based on the values in 'EngCol' and 'MkblCol'
+        classifcation_data['Change_color_value'] = classifcation_data.apply(
+            lambda row: str(row['EngCol'])+' to '+str(row['MkblCol'])+' color change' if row['EngCol'] != row['MkblCol'] else 'color not change', axis=1
+        )
+        
+        # Create a new column 'cut_Change' based on the values in 'EngCut' and 'MkblCut'
+        classifcation_data['Change_cut_value'] = classifcation_data.apply(
+            lambda row: str(row['EngCut'])+' to '+str(row['MkblCut'])+' cut change' if row['EngCut'] != row['MkblCut'] else 'cut not change', axis=1
+        )
+        
+        # Generate classification report
+        
+        
+        return classifcation_data
+    except Exception as e:
+        flash(f'Error generating classification report: {e}', 'error')
+        print(f'Error generating classification report: {e}')
+        return None
+    
+@app.route('/download_pred', methods=['GET'])
+def download_pred():
+    """Serve the output.csv file for download."""
+    return send_file(PRED_OUTPUT_FILE, as_attachment=True)  
+
+@app.route('/download_class', methods=['GET'])
+def download_class():
+    """Serve the output.csv file for download."""
+    return send_file(CLASS_OUTPUT_FILE, as_attachment=True)   
+
+if __name__ == "__main__":
     app.run(debug=True)