Copy_of_15_01_24_block.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1FL24nNzPSY4Z4eRP1hOx7L--tMxXQwEa",
      "authorship_tag": "ABX9TyNXsxGRwwxBLltbQxIB7Ymr",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dsk-003/Air-Quality/blob/main/Copy_of_15_01_24_block.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V7Ze-ft5oYmi",
        "outputId": "6451f8e1-9d38-4335-d766-cda6a83e2886"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "folder_path = '/content/drive/MyDrive/cv_data'\n",
        "# Get the list of files in the specified folder\n",
        "files = [f for f in os.listdir(folder_path) if os.path.isfile(os.path.join(folder_path, f))]\n",
        "\n",
        "# Count the number of files\n",
        "num_files = len(files)\n",
        "\n",
        "print(f'Total number of files in the folder: {num_files}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t11NRl1h1kH6",
        "outputId": "00085221-c354-497c-8caa-faca5ad1e8b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total number of files in the folder: 20\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(files)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TV2MMOU49xMJ",
        "outputId": "dbadc051-38d0-4a5c-c420-734a79bde721"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Neha.pdf', 'Pallavi+Chavan-Resume.pdf', 'Anuja_bagad_ML.pdf', 'Balan_Resume_DS.pdf', 'Owais+NEW.pdf', 'AnujKumarTiwari_194102303.pdf', 'Amay_Trivedi.pdf', 'Arpan_Srivastava.pdf', 'Ashish_Anand_Resume_May_2021.pdf', \"Mahesh's+Resume.pdf\", 'Resume (1).pdf', 'MachineLearnig.pdf', 'Rohit_chhabra_1528.pdf', 'Shravan-Anand-Komakula.pdf', 'sujith_resume.pdf', 'Sonam_Wadhwani_Resume.pdf', 'Resume_June.pdf', 'Ruchit_resume.pdf', 'RohitCV_C.pdf', 'Resume_Shalini_AI+&ML+(Python).pdf']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install PyMuPDF\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lEs187s13kH7",
        "outputId": "34611f5f-b944-4058-f4bb-82918a2f53cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting PyMuPDF\n",
            "  Downloading PyMuPDF-1.23.15-cp310-none-manylinux2014_x86_64.whl (4.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.4/4.4 MB\u001b[0m \u001b[31m30.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting PyMuPDFb==1.23.9 (from PyMuPDF)\n",
            "  Downloading PyMuPDFb-1.23.9-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (30.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m30.6/30.6 MB\u001b[0m \u001b[31m37.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: PyMuPDFb, PyMuPDF\n",
            "Successfully installed PyMuPDF-1.23.15 PyMuPDFb-1.23.9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import sys, fitz"
      ],
      "metadata": {
        "id": "9MSaMQS_3kKm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pypdf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zsq0hDcCCOIL",
        "outputId": "766a4968-d071-46a5-902f-100f28a19e26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pypdf\n",
            "  Downloading pypdf-3.17.4-py3-none-any.whl (278 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/278.2 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.4/278.2 kB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m278.2/278.2 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: pypdf\n",
            "Successfully installed pypdf-3.17.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import fitz  # Import PyMuPDF for PDF processing\n",
        "import os\n",
        "import spacy  # Import for file path operations\n",
        "nlp = spacy.load(\"en_core_web_sm\")"
      ],
      "metadata": {
        "id": "4RRGOxPkExgV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import json"
      ],
      "metadata": {
        "id": "VeJDc6PlH3HX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "5eE9YYwDF0qD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import fitz\n",
        "import os\n",
        "import spacy\n",
        "\n",
        "# Load a spaCy language model (e.g., en_core_web_sm)\n",
        "nlp = spacy.load(\"en_core_web_sm\")  # Adjust model as needed\n",
        "\n",
        "# Specify folder paths\n",
        "folder_path = \"/content/drive/MyDrive/Resume_data\"  # Replace with actual path\n",
        "output_folder = \"/content/drive/MyDrive/Resume_data/Out\"  # Replace with desired path\n",
        "\n",
        "# Get PDF files\n",
        "pdf_files = [f for f in os.listdir(folder_path) if f.endswith(\".pdf\")]\n",
        "\n",
        "for file in pdf_files:\n",
        "    file_path = os.path.join(folder_path, file)\n",
        "\n",
        "    try:\n",
        "        with fitz.open(file_path) as doc:\n",
        "            text = \"\"\n",
        "\n",
        "            for page in doc:\n",
        "                text += page.get_text()\n",
        "\n",
        "            # Perform NER after all text is extracted\n",
        "            doc = nlp(text)\n",
        "            ents = [(e.text, e.start_char, e.end_char, e.label_) for e in doc.ents]\n",
        "\n",
        "            # Create output file path\n",
        "            output_file_path = os.path.join(output_folder, os.path.splitext(file)[0] + \".txt\")\n",
        "\n",
        "            with open(output_file_path, \"w\") as output_file:\n",
        "                output_file.write(text)  # Write text first\n",
        "                for item in ents:\n",
        "                    output_file.write(f\"{item}\\n\")  # Append NER results\n",
        "\n",
        "    except Exception as e:\n",
        "        print(f\"Error processing {file_path}: {e}\")\n"
      ],
      "metadata": {
        "id": "ErZHNQBZZFy3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "def merge_text_files(input_folder, output_file):\n",
        "    \"\"\"Merges all text files in a given input folder into a single output file.\"\"\"\n",
        "\n",
        "    with open(output_file, \"w\") as outfile:\n",
        "        for filename in os.listdir(input_folder):\n",
        "            if filename.endswith(\".txt\"):\n",
        "                file_path = os.path.join(input_folder, filename)\n",
        "                with open(file_path, \"r\") as infile:\n",
        "                    outfile.write(infile.read())\n",
        "                    outfile.write(\"\\n\")  # Add a newline between files\n",
        "\n",
        "# Example usage:\n",
        "input_folder = \"/content/drive/MyDrive/Resume_data/Out\"  # Replace with the actual folder path\n",
        "output_file = \"/content/drive/MyDrive/Resume_data/merge_output/merged_text.txt\"  # Replace with the desired output filename\n",
        "merge_text = merge_text_files(input_folder, output_file)\n"
      ],
      "metadata": {
        "id": "PUFKUooFz4ml"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "\n",
        "# Specify the input text file and output JSON file\n",
        "input_file = \"/content/drive/MyDrive/Resume_data/merge_output/merged_text.txt\"\n",
        "output_file = \"/content/drive/MyDrive/Resume_data/merge_output/output.json\"\n",
        "\n",
        "# Load the text data from the file\n",
        "with open(input_file, \"r\") as text_file:\n",
        "    text_data = text_file.read()\n",
        "\n",
        "# **Handling different text file structures:**\n",
        "\n",
        "# 1. If the text file contains a single JSON object:\n",
        "if text_data.startswith(\"{\"):\n",
        "   try:\n",
        "    json_data = [json.loads(line.strip()) for line in text_data.splitlines() if line.strip()]\n",
        "   except json.JSONDecodeError:\n",
        "    print(\"Text file contains invalid JSON format.\")\n",
        "    # Optionally, skip invalid lines or provide custom parsing logic\n",
        "\n",
        "\n",
        "# 2. If the text file contains newline-delimited JSON objects:\n",
        "elif \"\\n\" in text_data:\n",
        "    json_data = [json.loads(line.strip()) for line in text_data.splitlines() if line.strip()]\n",
        "\n",
        "# 3. If the text file has a different structure, you'll need custom logic:\n",
        "else:\n",
        "    print(\"Text file structure is not recognized. Please provide parsing instructions.\")\n",
        "    exit()\n",
        "\n",
        "# Write the JSON data to the output file\n",
        "with open(output_file, \"w\") as json_file:\n",
        "    json.dump(json_data, json_file, indent=4)  # Indent for readability\n",
        "\n",
        "print(\"Text file converted to JSON successfully!\")\n"
      ],
      "metadata": {
        "id": "koVGrBxt0Ejc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "outputId": "ff4a6f68-4a7c-4424-c4f9-d5231ee7ffe6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "JSONDecodeError",
          "evalue": "Expecting value: line 1 column 1 (char 0)",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mJSONDecodeError\u001b[0m                           Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-14-4311a81752a8>\u001b[0m in \u001b[0;36m<cell line: 14>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;31m# 2. If the text file contains newline-delimited JSON objects:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0;34m\"\\n\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtext_data\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m     \u001b[0mjson_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtext_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplitlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;31m# 3. If the text file has a different structure, you'll need custom logic:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-14-4311a81752a8>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;31m# 2. If the text file contains newline-delimited JSON objects:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0;34m\"\\n\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtext_data\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m     \u001b[0mjson_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtext_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplitlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;31m# 3. If the text file has a different structure, you'll need custom logic:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/json/__init__.py\u001b[0m in \u001b[0;36mloads\u001b[0;34m(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[1;32m    344\u001b[0m             \u001b[0mparse_int\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mparse_float\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    345\u001b[0m             parse_constant is None and object_pairs_hook is None and not kw):\n\u001b[0;32m--> 346\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_default_decoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    347\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcls\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    348\u001b[0m         \u001b[0mcls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mJSONDecoder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/json/decoder.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, s, _w)\u001b[0m\n\u001b[1;32m    335\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m         \"\"\"\n\u001b[0;32m--> 337\u001b[0;31m         \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraw_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    338\u001b[0m         \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    339\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/json/decoder.py\u001b[0m in \u001b[0;36mraw_decode\u001b[0;34m(self, s, idx)\u001b[0m\n\u001b[1;32m    353\u001b[0m             \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscan_once\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    354\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 355\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mJSONDecodeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Expecting value\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    356\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mJSONDecodeError\u001b[0m: Expecting value: line 1 column 1 (char 0)"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KmZOA7QJfE2q"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}