Update app.py

app.py

@@ -8,10 +8,9 @@ from flask_cors import CORS
 app = Flask("Engineering College General Predictor")
 CORS(app)
 
-# 🔷 Load trained model & helpers from backend_files/
-pipeline = joblib.load('xgb_best_model.joblib')
+# Load trained pipeline & label encoder & choice_code_map
+pipeline = joblib.load('pipeline.joblib')
 target_encoder = joblib.load('label_encoder.joblib')
-feature_columns = joblib.load('feature_columns.joblib')
 choice_code_map = pd.read_csv('choice_code_map.csv', index_col='Choice Code')
 
 # Home route
@@ -36,13 +35,10 @@ def predict():
         sample_df = pd.DataFrame([{
             'Category': data['Category'],
             'Rank': data['Rank'],
-            'Percentage': data['Percentage'],   
+            'Percentage': data['Percentage'],
             'Course Name': data['Course Name']
         }])
 
-        # 🔷 Ensure column order matches training
-        sample_df = sample_df[feature_columns]
-
         # Predict probabilities
         proba = pipeline.predict_proba(sample_df)[0]
 
@@ -63,7 +59,7 @@ def predict():
                 "rank": rank,
                 "choice_code": choice_code,
                 "college_name": college_name,
-                "course name" : course_name,
+                "course name": course_name,
                 "probability_percent": round(float(prob), 2)
             })
 
@@ -72,6 +68,7 @@ def predict():
     except Exception as e:
         return jsonify({"error": str(e)}), 500
 
+
 # Run server
 if __name__ == '__main__':
     app.run(debug=False, host='0.0.0.0', port=7860)