app.py

from flask import Flask, render_template, request
import pickle
import numpy as np

app = Flask(__name__)

# Load the trained model
model = pickle.load(open("crop.pkl", "rb"))

# Load the SAME LabelEncoder used during training
le = pickle.load(open("label_encoder.pkl", "rb"))

@app.route("/")
def home():
    return render_template("index.html")

@app.route("/predict", methods=["POST"])
def predict():
    N = float(request.form['N'])
    P = float(request.form['P'])
    K = float(request.form['K'])
    temperature = float(request.form['temperature'])
    humidity = float(request.form['humidity'])
    ph = float(request.form['ph'])
    rainfall = float(request.form['rainfall'])

    features = np.array([[N, P, K, temperature, humidity, ph, rainfall]])
    pred = model.predict(features)

    crop = le.inverse_transform(pred)[0]

    return render_template('result.html', prediction_text=f"Recommended Crop: {crop}")