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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) | |