ats_application.ipynb

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      "authorship_tag": "ABX9TyOmoM7BeQZ5t3w+eUNp/HT8",
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      "name": "python3",
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    "language_info": {
      "name": "python"
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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/arshad831/Community_Workshops-/blob/main/ats_application.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q -U google-generativeai"
      ],
      "metadata": {
        "id": "9Cu6ZRaXLnuU"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Or use `os.getenv('GOOGLE_API_KEY')` to fetch an environment variable.\n",
        "import google.generativeai as genai\n",
        "\n",
        "# Used to securely store your API key\n",
        "from google.colab import userdata\n",
        "\n",
        "GOOGLE_API_KEY=userdata.get('gemini')\n",
        "\n",
        "genai.configure(api_key=GOOGLE_API_KEY)"
      ],
      "metadata": {
        "id": "0jKqv31wLnfR"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for m in genai.list_models():\n",
        "  if 'generateContent' in m.supported_generation_methods:\n",
        "    print(m.name)"
      ],
      "metadata": {
        "id": "N7CbgL50L1hW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install PyPDF2"
      ],
      "metadata": {
        "id": "YsYu198mL3qw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Import necessary libraries\n",
        "import google.generativeai as genai  # For accessing Google's generative AI models\n",
        "import PyPDF2 as pdf  # For PDF file reading and processing\n",
        "from google.colab import files  # For file upload functionality in Google Colab\n",
        "import io  # For handling byte streams\n",
        "\n",
        "# Function to get a response from Google's Gemini AI model\n",
        "def get_gemini_response(input_prompt):\n",
        "    model = genai.GenerativeModel('gemini-pro')  # Initialize the Gemini Pro model\n",
        "    response = model.generate_content(input_prompt)  # Generate content based on the input prompt\n",
        "    return response.text  # Return the generated text\n",
        "\n",
        "# Function to extract text from an uploaded PDF file\n",
        "def input_pdf_text(uploaded_file):\n",
        "    try:\n",
        "        reader = pdf.PdfReader(io.BytesIO(uploaded_file))  # Read the PDF from a byte stream\n",
        "        text = \"\"\n",
        "        for page in reader.pages:  # Iterate through each page in the PDF\n",
        "            text += page.extract_text() or ''  # Extract and concatenate text from each page\n",
        "        return text\n",
        "    except Exception as e:\n",
        "        return f\"Error reading PDF: {e}\"  # Return error message in case of failure\n",
        "\n",
        "# Main function to process the resume against the job description\n",
        "def process_resume(jd, uploaded_file):\n",
        "    if uploaded_file is None:\n",
        "        return \"Please upload a resume.\"  # Check for resume upload\n",
        "    if not jd:\n",
        "        return \"Please provide a job description.\"  # Check for job description input\n",
        "\n",
        "    resume_text = input_pdf_text(uploaded_file)  # Extract text from the uploaded resume\n",
        "    if resume_text:\n",
        "        # Prepare the input prompt for the generative AI model\n",
        "        input_prompt = f\"\"\"\n",
        "        Hey Act Like a skilled or very experienced ATS (Application Tracking System)\n",
        "        with a deep understanding of tech field, software engineering, data science, data analyst, artificial intelligence\n",
        "        and big data engineer. Your task is to evaluate the resume based on the given job description.\n",
        "        You must consider the job market is very competitive and you should provide\n",
        "        the best assistance for improving the resumes. Assign the percentage Matching based\n",
        "        on JD and the missing keywords with high accuracy.\n",
        "        resume: {resume_text}\n",
        "        description: {jd}\n",
        "\n",
        "        I want the response in different single string in each separate line  having the structure\n",
        "        {{\"JD Match\":\"%\",\n",
        "        \"MissingKeywords:[]\",\n",
        "         Tips to improve resume to get more match}}\n",
        "        \"\"\"\n",
        "        response = get_gemini_response(input_prompt)  # Get AI-generated evaluation\n",
        "        return response\n",
        "    else:\n",
        "        return \"Error processing the resume.\"  # Error message if resume processing fails\n",
        "\n",
        "def main():\n",
        "    jd = input(\"Please paste the job description here: \")  # Get job description as text input\n",
        "\n",
        "    print(\"Please upload your resume (PDF):\")  # Prompt user to upload resume\n",
        "    uploaded_file = files.upload()  # Upload file\n",
        "\n",
        "    uploaded_file_name = next(iter(uploaded_file))  # Assuming only one file is uploaded\n",
        "\n",
        "    response = process_resume(jd, uploaded_file[uploaded_file_name])  # Process the resume\n",
        "    print(response)  # Print the AI's evaluation\n",
        "\n",
        "# Run the main function if this script is executed\n",
        "if __name__ == \"__main__\":\n",
        "    main()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 159
        },
        "id": "DDeRKCdJ-btq",
        "outputId": "0979daef-a797-45d2-f2a2-6555fc02c740"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Please paste the job description here: data scientist 6 years\n",
            "Please upload your resume (PDF):\n"
          ]
        },
        {
          "output_type": "display_data",
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              "      <script>// Copyright 2017 Google LLC\n",
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              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving Arshad_Analytics_CV_12 (3).pdf to Arshad_Analytics_CV_12 (3).pdf\n",
            "{\"JD Match\":\"75%\",\n",
            " \"MissingKeywords\":[\"Data Visualization\"],\n",
            " \"Tips to improve resume\":\"Add a section highlighting your experience with data visualization tools and techniques.\"}\n"
          ]
        }
      ]
    }
  ]
}