app.py

from flask import Flask, request, jsonify
import pickle
import numpy as np
from flask_cors import CORS

app = Flask(__name__)
CORS(app)

# Load the trained model and scaler
with open('crop_recommendation_model.pkl', 'rb') as model_file:
    model = pickle.load(model_file)

with open('scaler.pkl', 'rb') as scaler_file:
    scaler = pickle.load(scaler_file)

@app.route('/predict', methods=['POST'])
def predict():
    try:
        # Get input data from the request
        data = request.get_json()
        features = [
            float(data['N']),
            float(data['P']),
            float(data['K']),
            float(data['temperature']),
            float(data['humidity']),
            float(data['ph']),
            float(data['rainfall'])
        ]

        # Convert features to numpy array and reshape for prediction
        features = np.array(features).reshape(1, -1)

        # Scale the input features
        scaled_features = scaler.transform(features)

        # Make prediction
        prediction = model.predict(scaled_features)

        # Return raw model output
        return jsonify({'prediction': prediction[0]})
    except Exception as e:
        return jsonify({'error': str(e)}), 400

if __name__ == '__main__':
    import os
    port = int(os.environ.get('PORT', 7860))
    app.run(host='0.0.0.0', port=port, debug=True)