ATS_Resume_Using_Bart_Facebook.ipynb

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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/arssite/GENAi-Assessment/blob/main/ATS_Resume_Using_Bart_Facebook.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 gradio transformers python-docx spacy torch\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P22t7k8KAb64",
        "outputId": "e1853379-96b1-4159-bfeb-1a229b681540"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25hInstalling collected packages: pydub, websockets, tomlkit, semantic-version, ruff, python-multipart, python-docx, orjson, h11, ffmpy, aiofiles, uvicorn, starlette, httpcore, httpx, fastapi, gradio-client, gradio\n",
            "Successfully installed aiofiles-23.2.1 fastapi-0.115.0 ffmpy-0.4.0 gradio-4.44.1 gradio-client-1.3.0 h11-0.14.0 httpcore-1.0.6 httpx-0.27.2 orjson-3.10.7 pydub-0.25.1 python-docx-1.1.2 python-multipart-0.0.12 ruff-0.6.9 semantic-version-2.10.0 starlette-0.38.6 tomlkit-0.12.0 uvicorn-0.31.0 websockets-12.0\n"
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          "output_type": "display_data",
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            "text/plain": [
              "config.json:   0%|          | 0.00/1.58k [00:00<?, ?B/s]"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Running on public URL: https://1e17801fc1b72b38b8.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "<IPython.core.display.HTML object>"
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              "<div><iframe src=\"https://1e17801fc1b72b38b8.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
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          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "import gradio as gr\n",
        "from transformers import pipeline\n",
        "import spacy\n",
        "from docx import Document\n",
        "import tempfile\n",
        "import os\n",
        "import torch\n",
        "\n",
        "# Load spaCy model\n",
        "try:\n",
        "    nlp = spacy.load('en_core_web_sm')\n",
        "except IOError:\n",
        "    import subprocess\n",
        "    subprocess.run([\"python\", \"-m\", \"spacy\", \"download\", \"en_core_web_sm\"])\n",
        "    nlp = spacy.load('en_core_web_sm')\n",
        "\n",
        "# Check for GPU availability\n",
        "device = 0 if torch.cuda.is_available() else -1\n",
        "\n",
        "# Load BART model for text generation\n",
        "summarizer = pipeline(\"summarization\", model=\"facebook/bart-large-cnn\", device=device)  # Use GPU if available\n",
        "\n",
        "# Define functions\n",
        "def extract_text_from_docx(file_path):\n",
        "    try:\n",
        "        doc = Document(file_path)\n",
        "        return '\\n'.join([para.text for para in doc.paragraphs])\n",
        "    except:\n",
        "        return \"\"\n",
        "\n",
        "def save_updated_resume(text):\n",
        "    try:\n",
        "        temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.docx')\n",
        "        doc = Document()\n",
        "        for line in text.split('\\n'):\n",
        "            doc.add_paragraph(line)\n",
        "        doc.save(temp_file.name)\n",
        "        return temp_file.name\n",
        "    except:\n",
        "        return None\n",
        "\n",
        "def bart_optimize_text(text):\n",
        "    # Use BART to generate an optimized version of the text\n",
        "    try:\n",
        "        optimized_text = summarizer(text, max_length=300, min_length=100, do_sample=False)[0]['summary_text']\n",
        "        return optimized_text\n",
        "    except:\n",
        "        return text  # Return original text if an error occurs\n",
        "\n",
        "def process_resume(file):\n",
        "    if file is None:\n",
        "        return \"Please upload a resume file.\", \"No suggestions yet.\", None\n",
        "\n",
        "    try:\n",
        "        resume_text = extract_text_from_docx(file.name)\n",
        "        if not resume_text:\n",
        "            return \"Could not read the resume. Please ensure it's a valid .docx file.\", \"No suggestions available.\", None\n",
        "\n",
        "        updated_text = bart_optimize_text(resume_text)  # Use BART for text optimization\n",
        "        output_file_path = save_updated_resume(updated_text)\n",
        "\n",
        "        return updated_text, \"Text optimized using BART.\", output_file_path\n",
        "    except:\n",
        "        return \"An error occurred. Please try again with a different file.\", \"No suggestions available.\", None\n",
        "\n",
        "# Create and launch Gradio interface\n",
        "iface = gr.Interface(\n",
        "    fn=process_resume,\n",
        "    inputs=gr.File(label=\"Upload Your Resume (.docx only)\", file_types=[\".docx\"]),\n",
        "    outputs=[\n",
        "        gr.Textbox(label=\"Optimized Resume Text\", lines=10),\n",
        "        gr.Textbox(label=\"ATS Improvement Suggestions\", lines=10),\n",
        "        gr.File(label=\"Download Optimized Resume\")\n",
        "    ],\n",
        "    title=\"ATS Resume Optimizer\",\n",
        "    description=\"Upload your .docx resume for ATS optimization. We'll suggest improvements and provide an updated version.\",\n",
        "    theme=\"default\",\n",
        "    css=\"\"\"\n",
        "        .gradio-container {max-width: 800px; margin: auto;}\n",
        "        .output-markdown {white-space: pre-wrap;}\n",
        "    \"\"\"\n",
        ")\n",
        "\n",
        "iface.launch(share=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zTQOAPqqAt13"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}