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| from flask import Flask, request, render_template, jsonify | |
| import joblib | |
| import numpy as np | |
| app = Flask(__name__) | |
| # Load the trained model and scaler (update paths as necessary) | |
| model = joblib.load("model_rf.joblib") | |
| scaler = joblib.load("scaler.joblib") | |
| def home(): | |
| return render_template("index.html") | |
| def predict(): | |
| try: | |
| # Expecting form data from the HTML template | |
| CGPA = float(request.form.get("CGPA")) | |
| Internships = int(request.form.get("Internships")) | |
| Projects = int(request.form.get("Projects")) | |
| Workshops_Certifications = int(request.form.get("Workshops_Certifications")) | |
| AptitudeTestScore = float(request.form.get("AptitudeTestScore")) | |
| SoftSkillRating = float(request.form.get("SoftSkillRating")) | |
| ExtracurricularActivities = request.form.get("ExtracurricularActivities") | |
| PlacementTraining = request.form.get("PlacementTraining") | |
| SSC_Marks = float(request.form.get("SSC_Marks")) | |
| HSC_Marks = float(request.form.get("HSC_Marks")) | |
| # Convert categorical fields to numerical | |
| extra_act = 1 if ExtracurricularActivities.lower() == "yes" else 0 | |
| placement_training = 1 if PlacementTraining.lower() == "yes" else 0 | |
| # Construct feature vector | |
| features = [ | |
| CGPA, | |
| Internships, | |
| Projects, | |
| Workshops_Certifications, | |
| AptitudeTestScore, | |
| SoftSkillRating, | |
| extra_act, | |
| placement_training, | |
| SSC_Marks, | |
| HSC_Marks, | |
| ] | |
| # Scale features and make prediction | |
| features_scaled = scaler.transform(np.array(features).reshape(1, -1)) | |
| prediction = model.predict(features_scaled) | |
| result = "Placed" if prediction[0] == 1 else "Not Placed" | |
| return render_template("index.html", prediction=result) | |
| except Exception as e: | |
| return render_template("index.html", prediction=f"Error: {e}") | |
| if __name__ == "__main__": | |
| app.run(host="0.0.0.0", port=7860, debug=True) | |