{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Evaluating the Recommendation Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mocha/miniconda3/envs/mamba/envs/neurobytes_music_recommender/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import gradio as gr\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from joblib import load\n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the same neural network model\n",
    "class ImprovedSongRecommender(nn.Module):\n",
    "    def __init__(self, input_size, num_titles):\n",
    "        super(ImprovedSongRecommender, self).__init__()\n",
    "        self.fc1 = nn.Linear(input_size, 128)\n",
    "        self.bn1 = nn.BatchNorm1d(128)\n",
    "        self.fc2 = nn.Linear(128, 256)\n",
    "        self.bn2 = nn.BatchNorm1d(256)\n",
    "        self.fc3 = nn.Linear(256, 128)\n",
    "        self.bn3 = nn.BatchNorm1d(128)\n",
    "        self.output = nn.Linear(128, num_titles)\n",
    "        self.dropout = nn.Dropout(0.5)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = torch.relu(self.bn1(self.fc1(x)))\n",
    "        x = self.dropout(x)\n",
    "        x = torch.relu(self.bn2(self.fc2(x)))\n",
    "        x = self.dropout(x)\n",
    "        x = torch.relu(self.bn3(self.fc3(x)))\n",
    "        x = self.dropout(x)\n",
    "        x = self.output(x)\n",
    "        return x\n",
    "\n",
    "# Load the trained model\n",
    "model_path = \"../models/improved_model.pth\"\n",
    "num_unique_titles = 4855  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ImprovedSongRecommender(input_size=2, num_titles=num_unique_titles)  \n",
    "model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mocha/miniconda3/envs/mamba/envs/neurobytes_music_recommender/lib/python3.8/site-packages/sklearn/base.py:348: InconsistentVersionWarning: Trying to unpickle estimator LabelEncoder from version 1.2.2 when using version 1.3.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n",
      "/Users/mocha/miniconda3/envs/mamba/envs/neurobytes_music_recommender/lib/python3.8/site-packages/sklearn/base.py:348: InconsistentVersionWarning: Trying to unpickle estimator MinMaxScaler from version 1.2.2 when using version 1.3.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Load the label encoders and scaler\n",
    "label_encoders_path = \"data/new_label_encoders.joblib\"\n",
    "scaler_path = \"data/new_scaler.joblib\"\n",
    "\n",
    "label_encoders = load(label_encoders_path)\n",
    "scaler = load(scaler_path)\n",
    "\n",
    "# Create a mapping from encoded indices to actual song titles\n",
    "index_to_song_title = {index: title for index, title in enumerate(label_encoders['title'].classes_)}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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