{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir('../')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\Users\\User\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\backend.py:1398: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\Users\\User\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\layers\\pooling\\max_pooling2d.py:161: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = tf.keras.models.load_model('artifacts/training/model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataclasses import dataclass\n",
    "from pathlib import Path\n",
    "\n",
    "@dataclass(frozen=True)\n",
    "class EvaluationConfig:\n",
    "    path_of_model : Path\n",
    "    training_data : Path\n",
    "    all_params : dict\n",
    "    params_image_size : list\n",
    "    params_batch_size: int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cnnClassfier.constants import *\n",
    "from cnnClassfier.utils.common import read_yaml, create_directories, save_json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ConfigurationManager:\n",
    "    def __init__(\n",
    "        self, \n",
    "        config_filepath = CONFIG_FILE_PATH,\n",
    "        params_filepath = PARAMS_FILE_PATH):\n",
    "        self.config = read_yaml(config_filepath)\n",
    "        self.params = read_yaml(params_filepath)\n",
    "        create_directories([self.config.artifacts_root])\n",
    "        \n",
    "        \n",
    "    def get_validation_config(self) -> EvaluationConfig:\n",
    "        eval_config = EvaluationConfig(\n",
    "            path_of_model=\"artifacts/training/model.h5\",\n",
    "            training_data=\"artifacts/data_ingestion/Chicken-fecal-images\",\n",
    "            all_params=self.params,\n",
    "            params_image_size=self.params.IMAZE_SIZE,\n",
    "            params_batch_size=self.params.BATCH_SIZE\n",
    "        )\n",
    "        return eval_config\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from urllib.parse import urlparse\n",
    "\n",
    "class Evaluation:\n",
    "    def __init__(self, config: EvaluationConfig):\n",
    "        self.config = config\n",
    "        \n",
    "    def _valid_generator(self):\n",
    "        datagenerator_kwargs = dict(\n",
    "            rescale = 1./255,\n",
    "            validation_split = 0.30\n",
    "        )\n",
    "        \n",
    "        dataflow_kwargs = dict(\n",
    "            target_size = self.config.params_image_size[:-1],\n",
    "            batch_size= self.config.params_batch_size,\n",
    "            interpolation = 'bilinear'\n",
    "        )\n",
    "        \n",
    "        valid_datagenerator = tf.keras.preprocessing.image.ImageDataGenerator(\n",
    "            **datagenerator_kwargs\n",
    "        )\n",
    "        \n",
    "        self.valid_generator = valid_datagenerator.flow_from_directory(\n",
    "            directory = self.config.training_data,\n",
    "            subset = 'validation',\n",
    "            shuffle = True,\n",
    "            **dataflow_kwargs\n",
    "        )\n",
    "        \n",
    "    @staticmethod\n",
    "    def load_model(path: Path) -> tf.keras.Model:\n",
    "        return tf.keras.models.load_model(path)\n",
    "    \n",
    "    def evaluation(self):\n",
    "        self.model = self.load_model(self.config.path_of_model)\n",
    "        self._valid_generator()\n",
    "        self.score = model.evaluate(self.valid_generator)\n",
    "        \n",
    "    def save_score(self):\n",
    "        scores = {'loss' : self.score[0], 'accuracy' : self.score[1]}\n",
    "        save_json(path = Path('scores.json'), data = scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-07-28 02:01:54,885: INFO: common: yaml file: config\\config.yaml loaded successfully]\n",
      "[2024-07-28 02:01:54,889: INFO: common: yaml file: params.yaml loaded successfully]\n",
      "[2024-07-28 02:01:54,890: INFO: common: Created directory  at: artifacts]\n",
      "Found 116 images belonging to 2 classes.\n",
      "WARNING:tensorflow:From c:\\Users\\User\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\utils\\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.\n",
      "\n",
      "[2024-07-28 02:01:56,004: WARNING: module_wrapper: From c:\\Users\\User\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\utils\\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.\n",
      "]\n",
      "8/8 [==============================] - 11s 1s/step - loss: 0.3306 - accuracy: 0.9569\n",
      "[2024-07-28 02:02:06,982: INFO: common: Json file saved at: scores.json]\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    config = ConfigurationManager()\n",
    "    val_config = config.get_validation_config()\n",
    "    evaluation = Evaluation(val_config)\n",
    "    evaluation.evaluation()\n",
    "    evaluation.save_score()\n",
    "    \n",
    "except Exception as e:\n",
    "    raise e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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