Update app.py

app.py

@@ -1,55 +1,67 @@
 from flask import Flask, request, render_template
 import pandas as pd
 import joblib
+import numpy as np
 
-# Initialize Flask app
 app = Flask(__name__)
 
-# Load model and encoders
+# Load model, encoders, and optional scaler
 model = joblib.load('placement_model.pkl')
 le_placement = joblib.load('le_placement.pkl')
 le_internship = joblib.load('le_internship.pkl')
+# scaler = joblib.load('scaler.pkl')  # Uncomment if you used scaling
 
-# Feature columns
+# Feature columns in exact order used during training
 features = ['IQ', 'Prev_Sem_Result', 'CGPA', 'Academic_Performance',
             'Extra_Curricular_Score', 'Communication_Skills', 'Projects_Completed',
             'Internship_Encoded']
 
 @app.route('/', methods=['GET', 'POST'])
 def index():
+    prediction = None
     if request.method == 'POST':
-        # Collect input from form
-        IQ = float(request.form['IQ'])
-        Prev_Sem_Result = float(request.form['Prev_Sem_Result'])
-        CGPA = float(request.form['CGPA'])
-        Academic_Performance = float(request.form['Academic_Performance'])
-        Extra_Curricular_Score = float(request.form['Extra_Curricular_Score'])
-        Communication_Skills = float(request.form['Communication_Skills'])
-        Projects_Completed = int(request.form['Projects_Completed'])
-        Internship_Experience = request.form['Internship_Experience']
-
-        # Prepare DataFrame
-        df = pd.DataFrame([{
-            'IQ': IQ,
-            'Prev_Sem_Result': Prev_Sem_Result,
-            'CGPA': CGPA,
-            'Academic_Performance': Academic_Performance,
-            'Extra_Curricular_Score': Extra_Curricular_Score,
-            'Communication_Skills': Communication_Skills,
-            'Projects_Completed': Projects_Completed,
-            'Internship_Experience': Internship_Experience
-        }])
-        df['Internship_Encoded'] = le_internship.transform(df['Internship_Experience'])
-        X = df[features]
-
-        # Predict
-        pred_encoded = model.predict(X)[0]
-        pred_label = le_placement.inverse_transform([pred_encoded])[0]
-
-        return render_template('index.html', prediction=f'Predicted Placement: {pred_label}')
-
-    return render_template('index.html', prediction=None)
-    
+        try:
+            # Collect inputs and convert to proper type
+            IQ = float(request.form['IQ'])
+            Prev_Sem_Result = float(request.form['Prev_Sem_Result'])
+            CGPA = float(request.form['CGPA'])
+            Academic_Performance = float(request.form['Academic_Performance'])
+            Extra_Curricular_Score = float(request.form['Extra_Curricular_Score'])
+            Communication_Skills = float(request.form['Communication_Skills'])
+            Projects_Completed = int(request.form['Projects_Completed'])
+            Internship_Experience = request.form['Internship_Experience'].strip()
+
+            # Handle unknown internship category
+            if Internship_Experience not in le_internship.classes_:
+                # Assign most frequent category from training
+                Internship_Experience = le_internship.classes_[0]
+
+            internship_encoded = le_internship.transform([Internship_Experience])[0]
+
+            # Prepare DataFrame in the same feature order as training
+            X = pd.DataFrame([[IQ, Prev_Sem_Result, CGPA, Academic_Performance,
+                               Extra_Curricular_Score, Communication_Skills,
+                               Projects_Completed, internship_encoded]],
+                             columns=features)
+
+            # Optional: scale features if model expects it
+            # X = pd.DataFrame(scaler.transform(X), columns=features)
+
+            # Make prediction
+            pred_encoded = model.predict(X)[0]
+            pred_label = le_placement.inverse_transform([pred_encoded])[0]
+
+            prediction = f'Predicted Placement: {pred_label}'
+
+        except ValueError:
+            prediction = "Invalid input! Please enter numeric values."
+
+        except Exception as e:
+            # Catch any unexpected error
+            prediction = f"Error in prediction: {str(e)}"
+
+    return render_template('index.html', prediction=prediction)
+
 @app.route('/about')
 def about():
     return render_template('about.html')