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import pandas as pd
from flask import Flask, request, jsonify
from flask_cors import CORS
from sklearn.preprocessing import LabelEncoder
from xgboost import XGBClassifier
import joblib
app = Flask(__name__)
CORS(app)
# Load the model and label encoder
model = joblib.load('crop_model.pkl')
label_encoder = joblib.load('label_encoder.pkl')
# Define preprocessing function
def preprocess_input(form_data):
"""Ensure input data matches the model's requirements."""
required_columns = ['N', 'P', 'K', 'temperature', 'humidity', 'ph', 'rainfall']
input_data = pd.DataFrame([form_data], columns=required_columns)
return input_data
# Define prediction function
def predict(input_data):
"""Predict the crop label for given input data."""
predictions = model.predict(input_data)
decoded_predictions = label_encoder.inverse_transform(predictions)
return decoded_predictions[0]
@app.route('/', methods=['GET'])
def health_check():
"""Health check endpoint."""
return jsonify({'status': 'healthy', 'message': 'CropSmartAI API is running'})
@app.route('/predict', methods=['POST'])
def get_prediction():
"""Receive input data, preprocess, and return crop prediction."""
if not request.json:
return jsonify({'error': 'No input data provided'}), 400
required_fields = ['N', 'P', 'K', 'temperature', 'humidity', 'ph', 'rainfall']
if not all(field in request.json for field in required_fields):
return jsonify({'error': 'Missing required fields'}), 400
try:
print("here")
# Get the input JSON data from the request
form_data = request.json
print(form_data)
# Preprocess the input data
preprocessed_data = preprocess_input(form_data)
# Get the prediction from the model
predicted_label = predict(preprocessed_data)
print(predicted_label)
# Return the prediction as a JSON response
return jsonify({'crop': predicted_label})
except Exception as e:
response = jsonify({'error': f'Prediction error: {str(e)}'})
return response, 500
if __name__ == '__main__':
# Update to use port 7860 to match Hugging Face Spaces requirements
app.run(host='0.0.0.0', port=7860, debug=False)
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