piped.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "import os\n",
        "import csv\n",
        "import json\n",
        "import time\n",
        "import requests\n",
        "from collections import defaultdict"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "7NAIsQC5ScHe"
      },
      "outputs": [],
      "source": [
        "# Configurações\n",
        "PIPED_API     = \"https://piapi.ggtyler.dev\"  # API Piped\n",
        "TIMEOUT       = (1000/9) # segundos de timeout para a requisição 111,111111111\n",
        "DELAY         = (1/3) # segundos de espera entre requisições\n",
        "TARGET_TYPES  = [\"music_songs\", \"music_videos\", \"music_albums\", \"stream\", \"videos\"]\n",
        "\n",
        "def load_progress(path):\n",
        "    \"\"\"Carrega playlists já processadas (para retomar o trabalho).\"\"\"\n",
        "    if not os.path.exists(path):\n",
        "        return defaultdict(list)\n",
        "    with open(path, encoding='utf-8') as f:\n",
        "        data = json.load(f)\n",
        "    pl = defaultdict(list)\n",
        "    for item in data.get(\"playlists\", []):\n",
        "        pl[item[\"name\"]] = item[\"videos\"]\n",
        "    return pl\n",
        "\n",
        "\n",
        "def save_progress(playlists, path):\n",
        "    \"\"\"Salva playlists no formato Piped imediatamente.\"\"\"\n",
        "    piped_format = {\n",
        "        \"format\": \"Piped\",\n",
        "        \"version\": 1,\n",
        "        \"playlists\": [\n",
        "            {\n",
        "                \"name\": name,\n",
        "                \"type\": \"playlist\",\n",
        "                \"visibility\": \"private\",\n",
        "                \"videos\": videos\n",
        "            }\n",
        "            for name, videos in playlists.items()\n",
        "        ]\n",
        "    }\n",
        "    # Grava em arquivo\n",
        "    with open(path, 'w', encoding='utf-8') as f:\n",
        "        json.dump(piped_format, f, ensure_ascii=False, indent=2)\n",
        "\n",
        "\n",
        "def search_video(track_name, artist_name):\n",
        "    \"\"\"Busca um vídeo no YouTube via Piped API.\"\"\"\n",
        "    query = f\"{track_name} {artist_name}\"\n",
        "    params = {\"q\": query, \"filter\": \"music_songs\"}\n",
        "    try:\n",
        "        resp = requests.get(f\"{PIPED_API}/search\", params=params, timeout=TIMEOUT)\n",
        "        resp.raise_for_status()\n",
        "        items = resp.json().get(\"items\", [])\n",
        "        # seleciona pela ordem de preferência de tipo\n",
        "        for t in TARGET_TYPES:\n",
        "            for item in items:\n",
        "                if item.get(\"type\") == t:\n",
        "                    return item.get(\"url\")\n",
        "        return None\n",
        "    except Exception as e:\n",
        "        print(f\"❌ Erro ao buscar «{query}»: {e}\")\n",
        "        return None\n",
        "\n",
        "\n",
        "def convert_csv_to_piped_format(csv_path, output_path):\n",
        "    playlists = load_progress(output_path)\n",
        "\n",
        "    with open(csv_path, newline='', encoding='utf-8') as csvfile:\n",
        "        reader = csv.DictReader(csvfile)\n",
        "        rows = list(reader)\n",
        "\n",
        "    for idx, row in enumerate(rows, start=1):\n",
        "        playlist_name = row.get('Playlist name') or \"Liked videos\"\n",
        "        track_name    = row['Track name'].strip()\n",
        "        artist_name   = row['Artist name'].strip()\n",
        "\n",
        "        # Se já houver esse vídeo, pula\n",
        "        if any(track_name in url for url in playlists[playlist_name]):\n",
        "            print(f\"⏭️  Já processado ({idx}/{len(rows)}): {track_name}\")\n",
        "            continue\n",
        "\n",
        "        print(f\"🔎 Buscando {idx}/{len(rows)}: {track_name} – {artist_name}\")\n",
        "        url = search_video(track_name, artist_name)\n",
        "        if url:\n",
        "            full_url = f\"https://youtube.com{url}\"\n",
        "            playlists[playlist_name].append(full_url)\n",
        "            # salva progresso imediatamente\n",
        "            save_progress(playlists, output_path)\n",
        "            print(f\"✅ Salvo: {full_url}\")\n",
        "        else:\n",
        "            print(f\"⚠️  Não encontrado: {track_name}\")\n",
        "\n",
        "        time.sleep(DELAY)\n",
        "\n",
        "    print(\"🏁 Conversão concluída.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sVcOdeD7SiWg",
        "outputId": "084f8279-80d1-4d74-8d49-b7e07724c79b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "🔄 Iniciando conversão do CSV para formato Piped...\n",
            "🔎 Buscando 1/4920: A Arena do Vazio | O Filme | - Minecraft Em busca da casa automática #329 – \n"
          ]
        }
      ],
      "source": [
        "print(\"🔄 Iniciando conversão do CSV para formato Piped...\")\n",
        "convert_csv_to_piped_format(\"seu_arquivo.csv\", \"playlists_piped.json\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "base",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.12.2"
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  "nbformat": 4,
  "nbformat_minor": 0
}