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from flask import Flask, render_template, request
import pickle
import numpy as np
app = Flask(__name__)
# Load model and columns
model = pickle.load(open("car_price_model.pkl", "rb"))
columns = pickle.load(open("model_columns.pkl", "rb"))
# @app.route("/")
# def home():
# return "Flask is working!"
@app.route("/", methods=["GET", "POST"])
def index():
if request.method == "POST":
try:
present_price = float(request.form["present_price"])
kms_driven = int(request.form["kms_driven"])
owner = int(request.form["owner"])
car_age = int(request.form["car_age"])
fuel_type = request.form["fuel_type"]
company = request.form["company"]
# Prepare input dictionary
input_data = {
"kms_driven": kms_driven,
"Owner": owner,
"car_age": car_age,
"company_" + company: 1,
"fuel_type_" + fuel_type: 1
}
input_vector = np.zeros(len(columns))
for i, col in enumerate(columns):
if col in input_data:
input_vector[i] = input_data[col]
elif col == 'Present_Price':
input_vector[i] = present_price
predicted_price = model.predict([input_vector])[0]
return render_template("index.html", prediction_text=f"Estimated Selling Price: ₹ {predicted_price:,.2f}")
except Exception as e:
return render_template("index.html", prediction_text=f"Error: {str(e)}")
return render_template("index.html", prediction_text="")
if __name__ == "__main__":
app.run(debug=True,use_reloader=False)
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