{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4991385a-1cc9-4cd7-b144-36dc0478fafe",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install renumics-spotlight datasets[audio]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a32c61a3-b0f8-430b-9c4d-ff27a1a7942e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datasets\n",
    "from renumics import spotlight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fada8bfa-c2ea-441a-9d07-f67fa8047138",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = datasets.load_dataset(\"renumics/speech_commands_enrichment_only\")\n",
    "raw_dataset = datasets.load_dataset(\"speech_commands\", 'v0.01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7e486382-31cd-4b69-8a9e-fe3d7ac94b41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['label_string', 'probability', 'probability_vector', 'prediction', 'prediction_string', 'embedding_reduced', '__index_level_0__'],\n",
       "        num_rows: 51093\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['label_string', 'probability', 'probability_vector', 'prediction', 'prediction_string', 'embedding_reduced', '__index_level_0__'],\n",
       "        num_rows: 6799\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['label_string', 'probability', 'probability_vector', 'prediction', 'prediction_string', 'embedding_reduced', '__index_level_0__'],\n",
       "        num_rows: 3081\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9594c54a-c024-4492-af7b-f1c25bb4de6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['file', 'audio', 'label', 'is_unknown', 'speaker_id', 'utterance_id'],\n",
       "        num_rows: 51093\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['file', 'audio', 'label', 'is_unknown', 'speaker_id', 'utterance_id'],\n",
       "        num_rows: 6799\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['file', 'audio', 'label', 'is_unknown', 'speaker_id', 'utterance_id'],\n",
       "        num_rows: 3081\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ddda31eb-fdab-4ed3-81cc-88506ae0d7d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "joined_dataset_enrichment = datasets.concatenate_datasets([dataset[\"train\"], dataset[\"validation\"], dataset[\"test\"]])\n",
    "raw_dataset_joined = datasets.concatenate_datasets([raw_dataset[\"train\"].sort(\"file\"), raw_dataset[\"validation\"].sort(\"file\"), \n",
    "                                                    raw_dataset[\"test\"].sort(\"file\")])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2b8918a3-5037-4062-b9cd-40b4a8a4d6c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['label_string', 'probability', 'probability_vector', 'prediction', 'prediction_string', 'embedding_reduced', '__index_level_0__'],\n",
       "    num_rows: 60973\n",
       "})"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined_dataset_enrichment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fa6ca118-3df0-4b6b-a036-4c70544500e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['file', 'audio', 'label', 'is_unknown', 'speaker_id', 'utterance_id'],\n",
       "    num_rows: 60973\n",
       "})"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#raw_dataset_joined = raw_dataset_joined.sort(\"file\")\n",
    "raw_dataset_joined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0a56d781-5b5c-4deb-aa4f-c9a1a96ac650",
   "metadata": {},
   "outputs": [],
   "source": [
    "complete_dataset = datasets.concatenate_datasets([raw_dataset_joined, joined_dataset_enrichment], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cb487431-8bc6-483a-bf25-a917278b6781",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['file', 'audio', 'label', 'is_unknown', 'speaker_id', 'utterance_id', 'label_string', 'probability', 'probability_vector', 'prediction', 'prediction_string', 'embedding_reduced', '__index_level_0__'],\n",
       "    num_rows: 60973\n",
       "})"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a11fefeb-9fd1-4500-9b9f-3275207b1cde",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'module' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[13], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m spotlight\u001b[38;5;241m.\u001b[39mshow(\n\u001b[1;32m      2\u001b[0m                 complete_dataset,\n\u001b[1;32m      3\u001b[0m                 \u001b[38;5;66;03m#layout= layout.parse(\"spotlight-layout.json\"),\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m                 layout\u001b[38;5;241m=\u001b[39m\u001b[43mspotlight\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlayouts\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel_debug\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m      5\u001b[0m                 port\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m7860\u001b[39m, \n\u001b[1;32m      6\u001b[0m                 host\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.0.0.0\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m      7\u001b[0m                 allow_filebrowsing\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m \n\u001b[1;32m      8\u001b[0m             )\n",
      "\u001b[0;31mTypeError\u001b[0m: 'module' object is not callable"
     ]
    }
   ],
   "source": [
    "spotlight.show(\n",
    "                complete_dataset,\n",
    "                #layout= layout.parse(\"spotlight-layout.json\"),\n",
    "                layout=spotlight.layouts.debug_classification(),\n",
    "                port=7860, \n",
    "                host=\"0.0.0.0\",\n",
    "                allow_filebrowsing=False \n",
    "            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1e38449-7bc1-4ae5-a754-a914a808a534",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "file_extension": ".py",
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