import joblib
import numpy as np
import os

current_dir = os.path.dirname(os.path.abspath(__file__))

models_dir = os.path.join(current_dir, '..', 'models')

crop_prediction_model = joblib.load(os.path.join(models_dir, 'crop_prediction_model.pkl'))
crop_prediction_scaler = joblib.load(os.path.join(models_dir, 'crop_prediction_scaler.pkl'))

fertilizer_prediction_model = joblib.load(os.path.join(models_dir, 'fertilizer_prediction_model.pkl'))
fertilizer_prediction_scaler = joblib.load(os.path.join(models_dir, 'fertilizer_prediction_scaler.pkl'))
crop_encoder = joblib.load(os.path.join(models_dir, 'crop_encoder.pkl'))
soil_encoder = joblib.load(os.path.join(models_dir, 'soil_encoder.pkl'))
fertilizer_encoder = joblib.load(os.path.join(models_dir, 'fertilizer_encoder.pkl'))


def get_crop_prediction(data):
    features = np.array([[
        data.Nitrogen,
        data.Phosphorus,
        data.Potassium,
        data.temperature,
        data.humidity,
        data.ph,
        data.rainfall
    ]])
    scaled_features = crop_prediction_scaler.transform(features)
    prediction = crop_prediction_model.predict(scaled_features)
    return {"predicted_crop": prediction[0]}


def get_fertilizer_prediction(data):
    crop_encoded = crop_encoder.transform(np.array([[data.crop_type]]))
    soil_encoded = soil_encoder.transform(np.array([[data.soil_type]]))
    features = np.array([[
        data.temperature,
        data.humidity,
        data.moisture,
        data.Nitrogen,
        data.Potassium,
        data.Phosphorus
    ]])
    scaled_features = fertilizer_prediction_scaler.transform(features)
    final_features = np.concatenate([scaled_features, [soil_encoded], [crop_encoded]], axis=1)
    prediction_encoded = fertilizer_prediction_model.predict(final_features)
    prediction = fertilizer_encoder.inverse_transform(prediction_encoded)

    return {"predicted_fertilizer": prediction[0]}



# import joblib
# import numpy as np
# import os

# current_dir = os.path.dirname(os.path.abspath(__file__))

# models_dir = os.path.join(current_dir, '..', 'models')

# crop_prediction_model = joblib.load(os.path.join(models_dir, 'crop_prediction_model.pkl'))
# crop_prediction_scaler = joblib.load(os.path.join(models_dir, 'crop_prediction_scaler.pkl'))

# fertilizer_prediction_model = joblib.load(os.path.join(models_dir, 'fertilizer_prediction_model.pkl'))
# fertilizer_prediction_scaler = joblib.load(os.path.join(models_dir, 'fertilizer_prediction_scaler.pkl'))
# crop_encoder = joblib.load(os.path.join(models_dir, 'crop_encoder.pkl'))
# soil_encoder = joblib.load(os.path.join(models_dir, 'soil_encoder.pkl'))
# fertilizer_encoder = joblib.load(os.path.join(models_dir, 'fertilizer_encoder.pkl'))


# def get_crop_prediction(data):
#     features = np.array([[
#         data.Nitrogen,
#         data.Phosphorous,
#         data.Potassium,
#         data.temperature,
#         data.humidity,
#         data.ph,
#         data.rainfall
#     ]])
#     scaled_features = crop_prediction_scaler.transform(features)
#     prediction = crop_prediction_model.predict(scaled_features)
#     return {"predicted_crop": prediction[0]}


# def get_fertilizer_prediction(data):
#     crop_encoded = crop_encoder.transform(np.array([[data.crop_type]]))
#     soil_encoded = soil_encoder.transform(np.array([[data.soil_type]]))
#     features = np.array([[
#         data.Temperature,
#         data.Humidity,
#         data.Moisture,
#         data.Nitrogen,
#         data.Potassium,
#         data.Phosphorus
#     ]])
#     scaled_features = fertilizer_prediction_scaler.transform(features)
#     final_features = np.concatenate([scaled_features, [soil_encoded], [crop_encoded]], axis=1)
#     prediction_encoded = fertilizer_prediction_model.predict(final_features)
#     prediction = fertilizer_encoder.inverse_transform(prediction_encoded)

#     return {"predicted_fertilizer": prediction[0]}




# # import joblib
# # import numpy as np
# # import os

# # # Get the absolute path to the current file's directory
# # current_dir = os.path.dirname(os.path.abspath(__file__))

# # # Construct the absolute path to the models directory
# # models_dir = os.path.join(current_dir, '..', 'models')

# # # Load crop prediction model and scaler
# # crop_prediction_model = joblib.load(os.path.join(models_dir, 'crop_prediction_model.pkl'))
# # crop_prediction_scaler = joblib.load(os.path.join(models_dir, 'crop_prediction_scaler.pkl'))

# # # Load fertilizer prediction model and encoders/scalers
# # fertilizer_prediction_model = joblib.load(os.path.join(models_dir, 'fertilizer_prediction_model.pkl'))
# # fertilizer_prediction_scaler = joblib.load(os.path.join(models_dir, 'fertilizer_prediction_scaler.pkl'))
# # crop_encoder = joblib.load(os.path.join(models_dir, 'crop_encoder.pkl'))
# # soil_encoder = joblib.load(os.path.join(models_dir, 'soil_encoder.pkl'))
# # fertilizer_encoder = joblib.load(os.path.join(models_dir, 'fertilizer_encoder.pkl'))


# # def get_crop_prediction(data):
# #     """
# #     Predicts the recommended crop based on soil and weather conditions.
# #     """
# #     features = np.array([[
# #         data.Nitrogen,
# #         data.Phosphorous,
# #         data.Potassium,
# #         data.temperature,
# #         data.humidity,
# #         data.ph,
# #         data.rainfall
# #     ]])
# #     scaled_features = crop_prediction_scaler.transform(features)
# #     prediction = crop_prediction_model.predict(scaled_features)
# #     return {"predicted_crop": prediction[0]}


# # def get_fertilizer_prediction(data):
# #     """
# #     Predicts the recommended fertilizer based on soil, weather, and crop type.
# #     """
# #     soil_encoded = soil_encoder.transform(np.array([[data.soil_type]]))
# #     crop_encoded = crop_encoder.transform(np.array([[data.crop_type]]))

# #     features = np.array([[
# #         data.Nitrogen,
# #         data.Phosphorus,
# #         data.Potassium,
# #         data.Temperature,
# #         data.Humidity,
# #         data.Moisture
# #     ]])

# #     scaled_features = fertilizer_prediction_scaler.transform(features)

# #     final_features = np.concatenate([scaled_features, [soil_encoded], [crop_encoded]], axis=1)

# #     prediction_encoded = fertilizer_prediction_model.predict(final_features)
# #     prediction = fertilizer_encoder.inverse_transform(prediction_encoded)

# #     return {"predicted_fertilizer": prediction[0]}


# import joblib
# import numpy as np
# import os

# # Get the absolute path to the current file's directory
# current_dir = os.path.dirname(os.path.abspath(__file__))

# # Construct the absolute path to the models directory
# models_dir = os.path.join(current_dir, '..', 'models')

# # Load crop prediction model and scaler
# crop_prediction_model = joblib.load(os.path.join(models_dir, 'crop_prediction_model.pkl'))
# crop_prediction_scaler = joblib.load(os.path.join(models_dir, 'crop_prediction_scaler.pkl'))

# # Load fertilizer prediction model and encoders/scalers
# fertilizer_prediction_model = joblib.load(os.path.join(models_dir, 'fertilizer_prediction_model.pkl'))
# fertilizer_prediction_scaler = joblib.load(os.path.join(models_dir, 'fertilizer_prediction_scaler.pkl'))
# crop_encoder = joblib.load(os.path.join(models_dir, 'crop_encoder.pkl'))
# soil_encoder = joblib.load(os.path.join(models_dir, 'soil_encoder.pkl'))
# fertilizer_encoder = joblib.load(os.path.join(models_dir, 'fertilizer_encoder.pkl'))


# def get_crop_prediction(data):
#     """
#     Predicts the recommended crop based on soil and weather conditions.
#     """
#     features = np.array([[
#         data.Nitrogen,
#         data.Phosphorous,
#         data.Potassium,
#         data.temperature,
#         data.humidity,
#         data.ph,
#         data.rainfall
#     ]])
#     scaled_features = crop_prediction_scaler.transform(features)
#     prediction = crop_prediction_model.predict(scaled_features)
#     return {"predicted_crop": prediction[0]}


# def get_fertilizer_prediction(data):
#     """
#     Predicts the recommended fertilizer based on soil, weather, and crop type.
#     """
#     soil_encoded = soil_encoder.transform(np.array([[data.soil_type]]))
#     crop_encoded = crop_encoder.transform(np.array([[data.crop_type]]))

#     features = np.array([[
#         data.Nitrogen,
#         data.Phosphorus,
#         data.Potassium,
#         data.Temperature,
#         data.Humidity,
#         data.Moisture
#     ]])

#     scaled_features = fertilizer_prediction_scaler.transform(features)

#     final_features = np.concatenate([scaled_features, [soil_encoded], [crop_encoded]], axis=1)

#     prediction_encoded = fertilizer_prediction_model.predict(final_features)
#     prediction = fertilizer_encoder.inverse_transform(prediction_encoded)

#     return {"predicted_fertilizer": prediction[0]}