Update app.py

app.py

@@ -1,124 +1,124 @@
-from flask import Flask, render_template, request, redirect, url_for, flash
-import os
-from joblib import load
-import numpy as np
-
-app = Flask(__name__)
-
-# Set the secret key for session management
-app.secret_key = os.urandom(24)
-
-# Configurations
-UPLOAD_FOLDER = "uploads/"
-app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
-app.config['DEBUG'] = True  # Enable debugging during development
-app.config['ENV'] = 'development'
-
-# Load models
-gia_model = load(r'H:\DEV PATEL\2025\AI_In_Diamond_Industry\Model\linear_regression_model_gia_price.joblib')
-grade_model = load(r'H:\DEV PATEL\2025\AI_In_Diamond_Industry\Model\linear_regression_model_grade_price.joblib')
-bygrade_model = load(r'H:\DEV PATEL\2025\AI_In_Diamond_Industry\Model\linear_regression_model_bygrade_price.joblib')
-makable_model = load(r'H:\DEV PATEL\2025\AI_In_Diamond_Industry\Model\linear_regression_model_makable_price.joblib')
-
-# Load label encoders for categorical features (renamed variable to avoid conflict with built-in list)
-encoder_list = ['Tag', 'EngShp', 'EngQua', 'EngCol', 'EngCut', 'EngPol', 'EngSym', 'EngFlo', 'EngNts', 'EngMikly', 'EngLab']
-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"
-    loaded_label_encoder[val] = load(encoder_path)
-
-@app.route('/', methods=['GET'])
-def home():
-    return render_template('home.html')
-
-@app.route('/predict', methods=['GET', 'POST'])
-def predict():
-    # Redirect GET requests back to the home page
-    if request.method == 'GET':
-        return redirect(url_for('home'))
-
-    try:
-        # Retrieve form data and strip any extra spaces ['Tag', 'EngCts', 'EngShp', 'EngQua', 'EngCol', 'EngCut', 'EngPol', 'EngSym', 'EngFlo', 'EngNts', 'EngMikly', 'EngLab', 'EngAmt',
-        Tag = request.form.get('Tag', '').strip()                        
-        # ICarat = request.form.get('ICarat', '').strip()
-        # MkblCarat = request.form.get('MkblCarat', '').strip()  
-        # SawLossCarat = request.form.get('SawLossCarat', '').strip()
-        # CrapsCarat = request.form.get('CrapsCarat', '').strip()
-        # EngGraphCts = request.form.get('EngGraphCts', '').strip()
-        EngCts = request.form.get('EngCts', '').strip()
-        EngShp = request.form.get('EngShp', '').strip()
-        EngQua = request.form.get('EngQua', '').strip()
-        EngCol = request.form.get('EngCol', '').strip()
-        EngCut = request.form.get('EngCut', '').strip()
-        EngPol = request.form.get('EngPol', '').strip()
-        EngSym = request.form.get('EngSym', '').strip()
-        EngFlo = request.form.get('EngFlo', '').strip()
-        EngNts = request.form.get('EngNts', '').strip()
-        EngMikly = request.form.get('EngMikly', '').strip()
-        EngLab = request.form.get('EngLab', '').strip()
-        EngAmt = request.form.get('EngAmt', '').strip()
-        
-        if EngLab == "nan":  # or if not EngLab:
-            EngLab = np.nan
-        else:
-            EngLab = EngLab  # remains the same
-
-        # Check if any required field is empty
-        if not all([Tag, 
-                    #ICarat, MkblCarat, SawLossCarat, CrapsCarat, EngGraphCts, 
-                    EngCts, EngShp, EngQua, EngCol, 
-                    EngCut, EngPol, EngSym, EngFlo, EngNts, EngMikly, 
-                    EngLab, EngAmt]):
-            flash("Please fill all the fields", "error")
-            return redirect(url_for('home'))
-        
-        # Transform categorical features using loaded label encoders
-        Tag = loaded_label_encoder['Tag'].transform([Tag])[0]
-        EngShp = loaded_label_encoder['EngShp'].transform([EngShp])[0] 
-        EngQua = loaded_label_encoder['EngQua'].transform([EngQua])[0]
-        EngCol = loaded_label_encoder['EngCol'].transform([EngCol])[0]
-        EngCut = loaded_label_encoder['EngCut'].transform([EngCut])[0]
-        EngPol = loaded_label_encoder['EngPol'].transform([EngPol])[0]
-        EngSym = loaded_label_encoder['EngSym'].transform([EngSym])[0]
-        EngFlo = loaded_label_encoder['EngFlo'].transform([EngFlo])[0]
-        EngNts = loaded_label_encoder['EngNts'].transform([EngNts])[0]
-        EngMikly = loaded_label_encoder['EngMikly'].transform([EngMikly])[0]
-        EngLab = loaded_label_encoder['EngLab'].transform([EngLab])[0]
-
-        # Convert numeric fields to float
-        # ICarat = float(ICarat)
-        # MkblCarat = float(MkblCarat)
-        # SawLossCarat = float(SawLossCarat)
-        # CrapsCarat = float(CrapsCarat)
-        # EngGraphCts = float(EngGraphCts)
-        EngCts = float(EngCts)
-        EngAmt = float(EngAmt)
-        
-        # Prepare input data for prediction
-        input_features = [[Tag, 
-                           #ICarat, MkblCarat, SawLossCarat, CrapsCarat, EngGraphCts,
-                           EngCts, EngShp, EngQua, EngCol, EngCut, 
-                           EngPol, EngSym, EngFlo, EngNts, EngMikly, EngLab, EngAmt]]
-        
-        # Make predictions
-        gia_price = gia_model.predict(input_features)[0]
-        grade_price = grade_model.predict(input_features)[0]
-        bygrade_price = bygrade_model.predict(input_features)[0]
-        makable_price = makable_model.predict(input_features)[0]
-        
-        gia_diff = EngAmt - gia_price
-        grade_diff = EngAmt - grade_price 
-        bygrade_diff = EngAmt - bygrade_price
-        makable_diff = EngAmt - makable_price
-
-    except Exception as e:
-        flash(f"Error in calculating the price: {e}", "error")
-        return redirect(url_for('home'))
-
-    return render_template('results.html', gia_price=gia_price, grade_price=grade_price, 
-                           bygrade_price=bygrade_price, makable_price=makable_price,
-                           gia_diff=gia_diff,grade_diff=grade_diff,bygrade_diff=bygrade_diff,
-                           makable_diff=makable_diff)
-
-if __name__ == "__main__":
-    app.run(debug=True, use_reloader=False)
+from flask import Flask, render_template, request, redirect, url_for, flash
+import os
+from joblib import load
+import numpy as np
+
+app = Flask(__name__)
+
+# Set the secret key for session management
+app.secret_key = os.urandom(24)
+
+# Configurations
+UPLOAD_FOLDER = "uploads/"
+app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
+app.config['DEBUG'] = True  # Enable debugging during development
+app.config['ENV'] = 'development'
+
+# Load models
+gia_model = load(r'.\Model\linear_regression_model_gia_price.joblib')
+grade_model = load(r'.\Model\linear_regression_model_grade_price.joblib')
+bygrade_model = load(r'.\Model\linear_regression_model_bygrade_price.joblib')
+makable_model = load(r'.\Model\linear_regression_model_makable_price.joblib')
+
+# Load label encoders for categorical features (renamed variable to avoid conflict with built-in list)
+encoder_list = ['Tag', 'EngShp', 'EngQua', 'EngCol', 'EngCut', 'EngPol', 'EngSym', 'EngFlo', 'EngNts', 'EngMikly', 'EngLab']
+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"
+    loaded_label_encoder[val] = load(encoder_path)
+
+@app.route('/', methods=['GET'])
+def home():
+    return render_template('home.html')
+
+@app.route('/predict', methods=['GET', 'POST'])
+def predict():
+    # Redirect GET requests back to the home page
+    if request.method == 'GET':
+        return redirect(url_for('home'))
+
+    try:
+        # Retrieve form data and strip any extra spaces ['Tag', 'EngCts', 'EngShp', 'EngQua', 'EngCol', 'EngCut', 'EngPol', 'EngSym', 'EngFlo', 'EngNts', 'EngMikly', 'EngLab', 'EngAmt',
+        Tag = request.form.get('Tag', '').strip()                        
+        # ICarat = request.form.get('ICarat', '').strip()
+        # MkblCarat = request.form.get('MkblCarat', '').strip()  
+        # SawLossCarat = request.form.get('SawLossCarat', '').strip()
+        # CrapsCarat = request.form.get('CrapsCarat', '').strip()
+        # EngGraphCts = request.form.get('EngGraphCts', '').strip()
+        EngCts = request.form.get('EngCts', '').strip()
+        EngShp = request.form.get('EngShp', '').strip()
+        EngQua = request.form.get('EngQua', '').strip()
+        EngCol = request.form.get('EngCol', '').strip()
+        EngCut = request.form.get('EngCut', '').strip()
+        EngPol = request.form.get('EngPol', '').strip()
+        EngSym = request.form.get('EngSym', '').strip()
+        EngFlo = request.form.get('EngFlo', '').strip()
+        EngNts = request.form.get('EngNts', '').strip()
+        EngMikly = request.form.get('EngMikly', '').strip()
+        EngLab = request.form.get('EngLab', '').strip()
+        EngAmt = request.form.get('EngAmt', '').strip()
+        
+        if EngLab == "nan":  # or if not EngLab:
+            EngLab = np.nan
+        else:
+            EngLab = EngLab  # remains the same
+
+        # Check if any required field is empty
+        if not all([Tag, 
+                    #ICarat, MkblCarat, SawLossCarat, CrapsCarat, EngGraphCts, 
+                    EngCts, EngShp, EngQua, EngCol, 
+                    EngCut, EngPol, EngSym, EngFlo, EngNts, EngMikly, 
+                    EngLab, EngAmt]):
+            flash("Please fill all the fields", "error")
+            return redirect(url_for('home'))
+        
+        # Transform categorical features using loaded label encoders
+        Tag = loaded_label_encoder['Tag'].transform([Tag])[0]
+        EngShp = loaded_label_encoder['EngShp'].transform([EngShp])[0] 
+        EngQua = loaded_label_encoder['EngQua'].transform([EngQua])[0]
+        EngCol = loaded_label_encoder['EngCol'].transform([EngCol])[0]
+        EngCut = loaded_label_encoder['EngCut'].transform([EngCut])[0]
+        EngPol = loaded_label_encoder['EngPol'].transform([EngPol])[0]
+        EngSym = loaded_label_encoder['EngSym'].transform([EngSym])[0]
+        EngFlo = loaded_label_encoder['EngFlo'].transform([EngFlo])[0]
+        EngNts = loaded_label_encoder['EngNts'].transform([EngNts])[0]
+        EngMikly = loaded_label_encoder['EngMikly'].transform([EngMikly])[0]
+        EngLab = loaded_label_encoder['EngLab'].transform([EngLab])[0]
+
+        # Convert numeric fields to float
+        # ICarat = float(ICarat)
+        # MkblCarat = float(MkblCarat)
+        # SawLossCarat = float(SawLossCarat)
+        # CrapsCarat = float(CrapsCarat)
+        # EngGraphCts = float(EngGraphCts)
+        EngCts = float(EngCts)
+        EngAmt = float(EngAmt)
+        
+        # Prepare input data for prediction
+        input_features = [[Tag, 
+                           #ICarat, MkblCarat, SawLossCarat, CrapsCarat, EngGraphCts,
+                           EngCts, EngShp, EngQua, EngCol, EngCut, 
+                           EngPol, EngSym, EngFlo, EngNts, EngMikly, EngLab, EngAmt]]
+        
+        # Make predictions
+        gia_price = gia_model.predict(input_features)[0]
+        grade_price = grade_model.predict(input_features)[0]
+        bygrade_price = bygrade_model.predict(input_features)[0]
+        makable_price = makable_model.predict(input_features)[0]
+        
+        gia_diff = EngAmt - gia_price
+        grade_diff = EngAmt - grade_price 
+        bygrade_diff = EngAmt - bygrade_price
+        makable_diff = EngAmt - makable_price
+
+    except Exception as e:
+        flash(f"Error in calculating the price: {e}", "error")
+        return redirect(url_for('home'))
+
+    return render_template('results.html', gia_price=gia_price, grade_price=grade_price, 
+                           bygrade_price=bygrade_price, makable_price=makable_price,
+                           gia_diff=gia_diff,grade_diff=grade_diff,bygrade_diff=bygrade_diff,
+                           makable_diff=makable_diff)
+
+if __name__ == "__main__":
+    app.run(debug=True, use_reloader=False)