ElasticSearch_haystack_on_Colab_QA.ipynb

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            "_model_name": "DescriptionStyleModel",
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    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/rajni-arora/Deep-Learning-Projects/blob/main/ElasticSearch_haystack_on_Colab_QA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "\n",
        "pip install --upgrade pip\n",
        "pip install farm-haystack[colab,preprocessing,elasticsearch,inference]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "_ZqTVoNtVXss",
        "outputId": "4faa4a95-92d5-4256-f75d-f71136bd1b36"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\n",
            "WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "\n",
        "wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q\n",
        "tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz\n",
        "chown -R daemon:daemon elasticsearch-7.9.2"
      ],
      "metadata": {
        "id": "ynMLRFilVd_V"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash --bg\n",
        "\n",
        "sudo -u daemon -- elasticsearch-7.9.2/bin/elasticsearch"
      ],
      "metadata": {
        "id": "LFKDIulfV0ew"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "\n",
        "time.sleep(30)"
      ],
      "metadata": {
        "id": "zFmMLOWxV3VT"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "res = requests.get('http://localhost:9200/_cluster/health')\n",
        "\n",
        "res.json()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "rIG5YiIjWJdr",
        "outputId": "d209e27c-107a-4b4c-e0c8-f3c3735c7b12"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'cluster_name': 'elasticsearch',\n",
              " 'status': 'yellow',\n",
              " 'timed_out': False,\n",
              " 'number_of_nodes': 1,\n",
              " 'number_of_data_nodes': 1,\n",
              " 'active_primary_shards': 2,\n",
              " 'active_shards': 2,\n",
              " 'relocating_shards': 0,\n",
              " 'initializing_shards': 0,\n",
              " 'unassigned_shards': 2,\n",
              " 'delayed_unassigned_shards': 0,\n",
              " 'number_of_pending_tasks': 0,\n",
              " 'number_of_in_flight_fetch': 0,\n",
              " 'task_max_waiting_in_queue_millis': 0,\n",
              " 'active_shards_percent_as_number': 50.0}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open(\"/content/test_reader.txt\", 'r') as file:\n",
        "  data = file.read()"
      ],
      "metadata": {
        "id": "731KYGoQMEhU"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "gkDD6-N4MR0Z",
        "outputId": "f21c1226-3faf-431b-9597-343d0047ea32"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Ins'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "#data = requests.get('https://raw.githubusercontent.com/jamescalam/transformers/main/data/text/meditations/clean.txt')\n",
        "text = data.split('\\n')\n",
        "text[:3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "GAdLuNNsGpuV",
        "outputId": "b34365bf-786d-405c-c6b9-8be53db58db0"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Instant payments',\n",
              " 'Retail instant payment systems (instant payment systems) process small-value interbank',\n",
              " 'transfers such that funds are available nearly instantly, as opposed to the potentially multiday settlement period for retail transfers on certain legacy bank payment systems.48 Instant']"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "data_json = [\n",
        "    {\n",
        "        'content': paragraph,\n",
        "        'meta': {\n",
        "            'source': 'meditations'\n",
        "        }\n",
        "    } for paragraph in text\n",
        "]"
      ],
      "metadata": {
        "id": "MQerrrEJGq4p"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from haystack.document_stores import ElasticsearchDocumentStore\n",
        "\n",
        "# Get the host where Elasticsearch is running, default to localhost\n",
        "host = os.environ.get(\"ELASTICSEARCH_HOST\", \"localhost\")\n",
        "\n",
        "document_store = ElasticsearchDocumentStore(host=host, username=\"\", password=\"\", index=\"data\")"
      ],
      "metadata": {
        "id": "T9XOifIUMwPD"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_json[:3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "V9GDktJGG5Uy",
        "outputId": "61f86aa9-8375-4b2e-ea0b-7d5983678cda"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'content': 'Instant payments', 'meta': {'source': 'meditations'}},\n",
              " {'content': 'Retail instant payment systems (instant payment systems) process small-value interbank',\n",
              "  'meta': {'source': 'meditations'}},\n",
              " {'content': 'transfers such that funds are available nearly instantly, as opposed to the potentially multiday settlement period for retail transfers on certain legacy bank payment systems.48 Instant',\n",
              "  'meta': {'source': 'meditations'}}]"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "document_store.write_documents(data_json)"
      ],
      "metadata": {
        "id": "u5llL_m7G8Qp"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "requests.get('http://localhost:9200/data/_count').json()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "imfuuX0kHJYv",
        "outputId": "510e32da-fe15-4562-e6a9-511296c95f6d"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'count': 43,\n",
              " '_shards': {'total': 1, 'successful': 1, 'skipped': 0, 'failed': 0}}"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install farm-haystack[inference]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "SYPBHVgcIq5X",
        "outputId": "a71044ba-7a92-4bf3-eec7-4044e419c57f"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: farm-haystack[inference] in /usr/local/lib/python3.10/dist-packages (1.24.1)\n",
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            "Requirement already satisfied: lazy-imports==0.3.1 in /usr/local/lib/python3.10/dist-packages (from farm-haystack[inference]) (0.3.1)\n",
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            "Requirement already satisfied: sentencepiece!=0.1.92,>=0.1.91 in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece,torch]==4.36.2; extra == \"inference\"->farm-haystack[inference]) (0.1.99)\n",
            "Requirement already satisfied: protobuf in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece,torch]==4.36.2; extra == \"inference\"->farm-haystack[inference]) (3.20.3)\n",
            "Requirement already satisfied: torch!=1.12.0,>=1.10 in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece,torch]==4.36.2; extra == \"inference\"->farm-haystack[inference]) (2.1.0+cu121)\n",
            "Requirement already satisfied: accelerate>=0.21.0 in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece,torch]==4.36.2; extra == \"inference\"->farm-haystack[inference]) (0.27.2)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.5.0->farm-haystack[inference]) (2023.6.0)\n",
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            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->farm-haystack[inference]) (2023.4)\n",
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            "Requirement already satisfied: monotonic>=1.5 in /usr/local/lib/python3.10/dist-packages (from posthog->farm-haystack[inference]) (1.6)\n",
            "Requirement already satisfied: backoff>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from posthog->farm-haystack[inference]) (2.2.1)\n",
            "Requirement already satisfied: inflect in /usr/local/lib/python3.10/dist-packages (from quantulum3->farm-haystack[inference]) (7.0.0)\n",
            "Requirement already satisfied: num2words in /usr/local/lib/python3.10/dist-packages (from quantulum3->farm-haystack[inference]) (0.5.13)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate>=0.21.0->transformers[sentencepiece,torch]==4.36.2; extra == \"inference\"->farm-haystack[inference]) (5.9.5)\n",
            "Requirement already satisfied: exceptiongroup>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from cattrs>=22.2->requests-cache<1.0.0->farm-haystack[inference]) (1.2.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.10->transformers[sentencepiece,torch]==4.36.2; extra == \"inference\"->farm-haystack[inference]) (1.12)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.10->transformers[sentencepiece,torch]==4.36.2; extra == \"inference\"->farm-haystack[inference]) (3.1.3)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.10->transformers[sentencepiece,torch]==4.36.2; extra == \"inference\"->farm-haystack[inference]) (2.1.0)\n",
            "Requirement already satisfied: docopt>=0.6.2 in /usr/local/lib/python3.10/dist-packages (from num2words->quantulum3->farm-haystack[inference]) (0.6.2)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch!=1.12.0,>=1.10->transformers[sentencepiece,torch]==4.36.2; extra == \"inference\"->farm-haystack[inference]) (2.1.5)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch!=1.12.0,>=1.10->transformers[sentencepiece,torch]==4.36.2; extra == \"inference\"->farm-haystack[inference]) (1.3.0)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#from haystack.retriever.sparse import ElasticsearchRetriever\n",
        "from haystack.nodes import TfidfRetriever\n",
        "from haystack.nodes import FARMReader\n",
        "model_name_or_path = \"deepset/roberta-base-squad2\"\n",
        "reader = FARMReader(model_name_or_path, use_gpu=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337,
          "referenced_widgets": [
            "2803cd1d737d487da470b6dff490040a",
            "33d26ed014ad497d925d358244e4dad5",
            "7d6aaea6b12f494db241f0a606333ce6",
            "bfcf7a963478473b8b18cf96d86996ce",
            "804315adfc054420a480fab77a33074b",
            "41387fee7a8740bda1802193d2a2afc2",
            "a5b56d3e118f4a12b98075700a17a34a",
            "3b54d581f57e44678de533092ef87a14",
            "24d3476ac0ad4ddcbde13afd3af12391",
            "7880759426ca47e780c30b6e1bc3df61",
            "57d6ede3bd0e4edba357e1258296ad52",
            "30256000c2b74e1dbe1b93b0d239e187",
            "cef7128ea3724f01bcf62903e19d8cb2",
            "a06eb0caab824236aa5c288a0c4908fc",
            "ea144cbc0d844c26b9d91d7b5dcd2010",
            "fa449ba255a348248a4420518c4c9250",
            "b2a0e790a7f640a09f2403e13323103d",
            "b469c42bbf404e25b9d22caa23b7b657",
            "83905215a40c479f964f5c890bbe712e",
            "f52fa4313d63461e939cce9aec1c4afa",
            "af26951d0cc347ecae615cd78878336d",
            "205c24cf90df4cadadeffafdc513912a",
            "469e3015bb8949e98c8d1abde3860a80",
            "535c5bc512174b1b8d3e4c6971ff871e",
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            "e4084e0536094ae280c2faa5a215a584",
            "c745471ac488443693ffc1e314af3ef3",
            "5327ec1eec5b41a6be8bdb7fee41d163",
            "fe5a3278f7c24d11b2e677a5eb914aaa",
            "f52351a6a0a14f7fbfa173203e7dff13",
            "bf3b1babde5245f2bee79940faa1c993",
            "408cd7cdc9a94ba8aae083c726f3f915",
            "66eec648b9b34c9f8bcd8dbeb8ef522b",
            "5fff0262fd1243df8549a070a8ace429",
            "3e8b07235b70432cb99217423249a62d",
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            "6517e8c64a064b7c8eb3bcbb483c315a",
            "70351a92a7fa431087f8dc3518998bbf",
            "4267ad8a6d01426e92f89fc486967574",
            "d2dc25498c014c0aa3ae1e22b1c19d8b",
            "e9ead8a35f32483ea7389b01af71f1e2",
            "3f3bf90b951e404c97cb45ef50aea222",
            "966ed24f432b48f3a324224179ba368c",
            "ababe833d253480d8ea71262331a19ad",
            "1b5ec717c28f4684804b517ed2a5a779",
            "6dbe933d507c4a5bbb64b7addf34ea36",
            "50221030f7a44913b4ad5702ef856dff",
            "cf326b16ec1c45f7958a4ad07d316489",
            "f6980466522b4aaca0d40ac76a2377ea",
            "9da056c73fad4490abceb9adbdbfb7ea",
            "7fc30a63dd0c4296b77e2c25b9318d79",
            "ed8fd5c926e94c9da1aaf97fed9b1108",
            "89f7f94360a1428887d1968482886455",
            "3849a08279814afea4b3a8340ed0c238",
            "a8e350d7f4cf46f7adf9b665227db600",
            "fefe6c755ef34aa48afc528c4c297fcc",
            "6db399b43ac641768763621a4ab7033d",
            "e71d240b00e1419ca52133bee379e139",
            "2fc3c9bd4ee9407fb02a54f8072260ef"
          ]
        },
        "id": "GOwLbox0IP6c",
        "outputId": "11955783-909f-4d86-dd4f-bc41b6440bbc"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/571 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2803cd1d737d487da470b6dff490040a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/496M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "30256000c2b74e1dbe1b93b0d239e187"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/79.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "469e3015bb8949e98c8d1abde3860a80"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c745471ac488443693ffc1e314af3ef3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "70351a92a7fa431087f8dc3518998bbf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/772 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f6980466522b4aaca0d40ac76a2377ea"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "retriever = TfidfRetriever(document_store)  # BM25\n",
        "reader = FARMReader(model_name_or_path='deepset/bert-base-cased-squad2',\n",
        "                    context_window_size=1500,\n",
        "                    use_gpu=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "2KtQjyHFKJUB",
        "outputId": "1b5955f6-7398-4c9e-fa1a-00cbb3446368"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at deepset/bert-base-cased-squad2 were not used when initializing BertForQuestionAnswering: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight']\n",
            "- This IS expected if you are initializing BertForQuestionAnswering from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForQuestionAnswering from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from haystack.pipelines import ExtractiveQAPipeline\n",
        "\n",
        "qa = ExtractiveQAPipeline(reader=reader, retriever=retriever)"
      ],
      "metadata": {
        "id": "xLutC0HtKCCb"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "richa = qa.run(query='What is instant payment?')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VtjUr9BVK_6B",
        "outputId": "1bdb045f-a18b-423a-f426-b1e911e9e20e"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00,  5.13 Batches/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "richa.to_csv(\"result\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 146
        },
        "id": "nXYOm1dLLHVh",
        "outputId": "f7c20729-3bee-4ac9-e185-bf498a705455"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "'dict' object has no attribute 'to_csv'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-50-d0204a0ef375>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mricha\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"result\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: 'dict' object has no attribute 'to_csv'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NnZxpgHVS4as"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}