from utils.preprocessing import preprocess_image
import tensorflow as tf
import numpy as np
import os 
import logging
import json



class AfroPalmModel:
    """
    Class to load the model and make predictions
    """
    def __init__(self):
        logging.info("Loading classification model")

        self.model_path = os.path.dirname(os.path.abspath("ghostnet_model_float32.tflite")) + "/models/ghostnet_model_float32.tflite"

        logging.debug(f"Preparing to read from {self.model_path}")
        
        with open(self.model_path, 'rb') as fid:
            tflite_model = fid.read()

        logging.info("File read successfully")
        
        # Create and allocate the interpreter using the loaded state
        self.interpreter = tf.lite.Interpreter(model_content=tflite_model)
        self.interpreter.allocate_tensors()
        
        # Retrieve the input and output indices
        self.input_index = self.interpreter.get_input_details()[0]["index"]
        self.output_index = self.interpreter.get_output_details()[0]["index"]

        logging.info("Model loaded successfully")




    def predict(self, image_path):
        """
        Make a prediction on the image
        :param image: image to make prediction on
        :return: prediction and confidence score
        """

        logging.info("Making prediction")

        img = preprocess_image(image_path)

        logging.debug(f'Image preprocessed with shape {np.array(img).shape}')

        self.interpreter.set_tensor(self.input_index, img)
        self.interpreter.invoke()

        predictions = list(self.interpreter.get_tensor(self.output_index)[0])

        logging.info("Classification successful")


        return predictions.index(max(predictions)), max(predictions)