import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler
from tensorflow.keras import layers, models
import numpy as np


def augment(real_data, num_augmented_records=None, start_date=None, end_date=None, technique='random', lat=None, lon=None, crop_type=None):
    """
    Augment real-world agricultural data with synthetic data using ML-based techniques.

    :param real_data: DataFrame of real agricultural data.
    :param num_augmented_records: Number of synthetic records to generate.
    :param start_date: Start date for synthetic data generation.
    :param end_date: End date for synthetic data generation.
    :param technique: Augmentation technique ('random', 'interpolation', 'linear_regression', 'autoencoder', 'gan').
    :param lat: Latitude for API-based data generation (optional).
    :param lon: Longitude for API-based data generation (optional).
    :param crop_type: Optional crop type for synthetic data.
    :return: DataFrame containing augmented data, including latitude and longitude.
    """

    augmented_data = None

    if technique == 'linear_regression':
        # Linear regression-based augmentation
        features = ['soil_ph', 'temperature_min', 'rainfall', 'fertilizer_n']
        X = real_data[features]
        y = real_data['yield_kg_per_ha']
        model = LinearRegression()
        model.fit(X, y)

        new_data = pd.DataFrame({
            'latitude': [lat] * num_augmented_records,
            'longitude': [lon] * num_augmented_records,
            'soil_ph': np.random.normal(6.5, 0.5, num_augmented_records),
            'temperature_min': np.random.uniform(10, 20, num_augmented_records),
            'rainfall': np.random.uniform(0, 50, num_augmented_records),
            'fertilizer_n': np.random.uniform(50, 150, num_augmented_records)
        })
        new_data['yield_kg_per_ha'] = model.predict(new_data[features])
        augmented_data = pd.concat([real_data, new_data], ignore_index=True)

    elif technique == 'autoencoder':
        # Autoencoder-based augmentation
        scaler = StandardScaler()
        scaled_data = scaler.fit_transform(real_data.drop(columns='yield_kg_per_ha'))

        input_layer = layers.Input(shape=(scaled_data.shape[1],))
        encoded = layers.Dense(8, activation='relu')(input_layer)
        decoded = layers.Dense(scaled_data.shape[1], activation='sigmoid')(encoded)
        autoencoder = models.Model(input_layer, decoded)
        autoencoder.compile(optimizer='adam', loss='mse')
        autoencoder.fit(scaled_data, scaled_data, epochs=50, batch_size=32, verbose=0)

        synthetic_data = autoencoder.predict(scaled_data)
        synthetic_data = scaler.inverse_transform(synthetic_data)
        synthetic_df = pd.DataFrame(synthetic_data, columns=real_data.columns.drop('yield_kg_per_ha'))
        synthetic_df['yield_kg_per_ha'] = np.random.normal(3000, 500, num_augmented_records)
        synthetic_df['latitude'] = lat
        synthetic_df['longitude'] = lon
        augmented_data = pd.concat([real_data, synthetic_df], ignore_index=True)

    elif technique == 'gan':
        # GAN-based augmentation (GAN example implementation)
        pass

    elif technique == 'random':
        # Random augmentation
        new_data = real_data.sample(num_augmented_records, replace=True)
        new_data['latitude'] = lat
        new_data['longitude'] = lon
        augmented_data = pd.concat([real_data, new_data], ignore_index=True)

    return augmented_data
