{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/shrey/Desktop/Kidney-Disease-Classifcation/research'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('../')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/shrey/Desktop/Kidney-Disease-Classifcation'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ[\"MLFLOW_TRACKING_URI\"]=\"https://dagshub.com/Shrey-patel-07/Kidney-Disease-Classifcation.mlflow\"\n",
    "os.environ[\"MLFLOW_TRACKING_USERNAME\"]=\"Shrey-patel-07\"\n",
    "os.environ[\"MLFLOW_TRACKING_PASSWORD\"]=\"6a425a20bb5b645c5efa1ca49ab28097a92a76ee\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-01-04 12:24:18.257148: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2024-01-04 12:24:18.258826: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-01-04 12:24:18.285564: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-01-04 12:24:18.285595: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-01-04 12:24:18.286297: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2024-01-04 12:24:18.290364: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-01-04 12:24:18.290802: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-01-04 12:24:18.864121: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-01-04 12:24:20.808055: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:274] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n"
     ]
    }
   ],
   "source": [
    "model = tf.keras.models.load_model('artifacts/training/model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
    "    mlflow_uri: str\n",
    "    params_image_size: list\n",
    "    params_batch_size: int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('src/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from kidney_classification.constants import *\n",
    "from kidney_classification.utils.common import read_yaml, create_directories, save_json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ConfigurationManager:\n",
    "    def __init__(\n",
    "        self,\n",
    "        config_filepath=CONFIG_FILE_PATH,\n",
    "        param_filepath=PARAMS_FILE_PATH\n",
    "    ):\n",
    "        self.config = read_yaml(config_filepath)\n",
    "        self.params = read_yaml(param_filepath)\n",
    "        create_directories([self.config.artifacts_root])\n",
    "\n",
    "    def get_evaluation_config(self) -> EvaluationConfig:\n",
    "        eval_config = EvaluationConfig(\n",
    "            path_of_model=\"artifacts/training/model.h5\",\n",
    "            training_data=\"artifacts/data_ingestion/CT-KIDNEY-DATASET-Normal-Cyst-Tumor-Stone\",\n",
    "            mlflow_uri=\"https://dagshub.com/Shrey-patel-07/Kidney-Disease-Classifcation.mlflow\",\n",
    "            all_params=self.params,\n",
    "            params_image_size=self.params.IMAGE_SIZE,\n",
    "            params_batch_size=self.params.BATCH_SIZE\n",
    "        )\n",
    "        return eval_config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mlflow\n",
    "import mlflow.keras\n",
    "from urllib.parse import urlparse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Evaluation:\n",
    "    def __init__(self, config: EvaluationConfig):\n",
    "        self.config = config\n",
    "        self.valid_generator = None  # Initialize to None\n",
    "\n",
    "    def _valid_generator(self):\n",
    "        img_height, img_width = self.config.params_image_size[:-1]\n",
    "\n",
    "        self.valid_generator = tf.keras.utils.image_dataset_from_directory(\n",
    "            self.config.training_data,\n",
    "            image_size=(img_height, img_width),\n",
    "            validation_split=0.30,\n",
    "            subset='validation',\n",
    "            seed=123\n",
    "        )\n",
    "\n",
    "        self.valid_generator = self.valid_generator.map(lambda x, y: (x / 255, y))\n",
    "        AUTOTUNE = tf.data.AUTOTUNE\n",
    "        self.valid_generator = self.valid_generator.cache().prefetch(buffer_size=AUTOTUNE)\n",
    "\n",
    "\n",
    "    @staticmethod\n",
    "    def load_model(path: Path) -> tf.keras.Model:\n",
    "        return tf.keras.models.load_model(path)\n",
    "    \n",
    "\n",
    "    def evaluation(self):\n",
    "        self.model = self.load_model(self.config.path_of_model)\n",
    "        self._valid_generator()\n",
    "        self.score = self.model.evaluate(self.valid_generator)\n",
    "        self.save_score()\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)\n",
    "\n",
    "    def log_into_mlflow(self):\n",
    "        mlflow.set_registry_uri(self.config.mlflow_uri)\n",
    "        tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme\n",
    "        \n",
    "        with mlflow.start_run():\n",
    "            mlflow.log_params(self.config.all_params)\n",
    "            mlflow.log_metrics(\n",
    "                {\"loss\": self.score[0], \"accuracy\": self.score[1]}\n",
    "            )\n",
    "            # Model registry does not work with file store\n",
    "            if tracking_url_type_store != \"file\":\n",
    "\n",
    "                # Register the model\n",
    "                mlflow.keras.log_model(self.model, \"model\", registered_model_name=\"VGG16Model\")\n",
    "            else:\n",
    "                mlflow.keras.log_model(self.model, \"model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('../')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-01-04 12:24:51,908: INFO: common yaml file: config/config.yaml loaded successfully]\n",
      "[2024-01-04 12:24:51,910: INFO: common yaml file: params.yaml loaded successfully]\n",
      "Found 12446 files belonging to 1 classes.\n",
      "Using 3733 files for validation.\n",
      "117/117 [==============================] - 147s 1s/step - loss: 3.2269e-06 - accuracy: 1.0000\n",
      "[2024-01-04 12:27:19,911: INFO: common json file saved at: scores.json]\n",
      "[2024-01-04 12:27:19,912: INFO: common json file saved at: scores.json]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024/01/04 12:27:22 WARNING mlflow.tensorflow: You are saving a TensorFlow Core model or Keras model without a signature. Inference with mlflow.pyfunc.spark_udf() will not work unless the model's pyfunc representation accepts pandas DataFrames as inference inputs.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /tmp/tmpdrlg6a4p/model/data/model/assets\n",
      "[2024-01-04 12:27:23,234: INFO: builder_impl Assets written to: /tmp/tmpdrlg6a4p/model/data/model/assets]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/shrey/Desktop/Kidney-Disease-Classifcation/env/lib/python3.11/site-packages/_distutils_hack/__init__.py:33: UserWarning: Setuptools is replacing distutils.\n",
      "  warnings.warn(\"Setuptools is replacing distutils.\")\n",
      "Registered model 'VGG16Model' already exists. Creating a new version of this model...\n",
      "2024/01/04 12:27:47 INFO mlflow.store.model_registry.abstract_store: Waiting up to 300 seconds for model version to finish creation. Model name: VGG16Model, version 3\n",
      "Created version '3' of model 'VGG16Model'.\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    config = ConfigurationManager()\n",
    "    eval_config = config.get_evaluation_config()\n",
    "    evaluation = Evaluation(eval_config)\n",
    "    evaluation.evaluation()\n",
    "    evaluation.log_into_mlflow()\n",
    "    \n",
    "except Exception as e:\n",
    "   raise e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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