{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'f:\\\\Projects\\\\End-to-End-Chest-Cancer-Classification-using-MLflow-and-DVC\\\\research'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(\"../\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'f:\\\\Projects\\\\End-to-End-Chest-Cancer-Classification-using-MLflow-and-DVC'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ[\"MLFLOW_TRACKING_URI\"]=\"https://dagshub.com/AlyyanAhmed21/End-to-End-Chest-Cancer-Classification-using-MLflow-and-DVC.mlflow\"\n",
    "os.environ[\"MLFLOW_TRACKING_USERNAME\"]=\"AlyyanAhmed21\"\n",
    "os.environ[\"MLFLOW_TRACKING_PASSWORD\"]=\"776454e991d86ea3a96179a4dc1ef72fbc134642\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.load_model(\"artifacts/training/model.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cnnClassifier.constants import *\n",
    "from cnnClassifier.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",
    "        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_evaluation_config(self) -> EvaluationConfig:\n",
    "        eval_config = EvaluationConfig(\n",
    "            path_of_model=\"artifacts/training/model.h5\",\n",
    "            training_data=\"artifacts/data_ingestion/Chest-CT-Scan-data\",\n",
    "            mlflow_uri=\"https://dagshub.com/AlyyanAhmed21/End-to-End-Chest-Cancer-Classification-using-MLflow-and-DVC.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\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from pathlib import Path\n",
    "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",
    "\n",
    "    \n",
    "    def _valid_generator(self):\n",
    "\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=False,\n",
    "            **dataflow_kwargs\n",
    "        )\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 = 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",
    "    \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",
    "                # There are other ways to use the Model Registry, which depends on the use case,\n",
    "                # please refer to the doc for more information:\n",
    "                # https://mlflow.org/docs/latest/model-registry.html#api-workflow\n",
    "                mlflow.keras.log_model(self.model, \"model\", registered_model_name=\"VGG16Model\")\n",
    "            else:\n",
    "                mlflow.keras.log_model(self.model, \"model\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2025-08-20 04:01:28,984: INFO: common: yaml file: config\\config.yaml loaded successfully]\n",
      "[2025-08-20 04:01:28,988: INFO: common: yaml file: params.yaml loaded successfully]\n",
      "[2025-08-20 04:01:28,991: INFO: common: created directory at: artifacts]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 102 images belonging to 2 classes.\n",
      "7/7 [==============================] - 16s 2s/step - loss: 57.2713 - accuracy: 0.4314\n",
      "[2025-08-20 04:01:45,268: INFO: common: json file saved at: scores.json]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025/08/20 04:01:47 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": [
      "[2025-08-20 04:01:48,249: WARNING: save: Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 14). These functions will not be directly callable after loading.]\n",
      "INFO:tensorflow:Assets written to: E:\\Temp\\tmp32wvm7sm\\model\\data\\model\\assets\n",
      "[2025-08-20 04:01:49,538: INFO: builder_impl: Assets written to: E:\\Temp\\tmp32wvm7sm\\model\\data\\model\\assets]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\Projects\\End-to-End-Chest-Cancer-Classification-using-MLflow-and-DVC\\cnn_env\\Lib\\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",
      "2025/08/20 04:02:45 INFO mlflow.tracking._model_registry.client: Waiting up to 300 seconds for model version to finish creation.                     Model name: VGG16Model, version 2\n",
      "Created version '2' 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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