{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "s7zEKhq9h6eb" }, "source": [ "# Resume and Job Description Data Generator" ] }, { "cell_type": "markdown", "metadata": { "id": "cH3arpfLiAWR" }, "source": [ "The purpose of this notebook is to generate resumes and job description data for training a model that will assist job applicants finetune their resumes to an advertised role; we will use an open source model and create a ui with gradio" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 9567, "status": "ok", "timestamp": 1761736938504, "user": { "displayName": "Kwabena Baah-Boakye", "userId": "14758998715370101460" }, "user_tz": 240 }, "id": "p2cGxlB2j5r5", "outputId": "a025aa62-d896-45e8-b3bd-6e615cedc455" }, "outputs": [], "source": [ "!pip install -q transformers accelerate bitsandbytes torch gradio" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "executionInfo": { "elapsed": 24516, "status": "ok", "timestamp": 1761736965621, "user": { "displayName": "Kwabena Baah-Boakye", "userId": "14758998715370101460" }, "user_tz": 240 }, "id": "Hhqp912jj_Fb" }, "outputs": [], "source": [ "# imports\n", "\n", "import os\n", "import requests\n", "from huggingface_hub import login\n", "from google.colab import userdata\n", "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, pipeline\n", "import torch\n", "import json\n", "import pandas as pd\n", "import gradio as gr" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "executionInfo": { "elapsed": 688, "status": "ok", "timestamp": 1761736970821, "user": { "displayName": "Kwabena Baah-Boakye", "userId": "14758998715370101460" }, "user_tz": 240 }, "id": "1GlDBWg2kYW9" }, "outputs": [], "source": [ "# Sign in to HuggingFace Hub\n", "\n", "hf_token = userdata.get('HF_TOKEN')\n", "login(hf_token, add_to_git_credential=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "executionInfo": { "elapsed": 6, "status": "ok", "timestamp": 1761736993552, "user": { "displayName": "Kwabena Baah-Boakye", "userId": "14758998715370101460" }, "user_tz": 240 }, "id": "CF1l129DlaD6" }, "outputs": [], "source": [ "# Available models\n", "AVAILABLE_MODELS = {\n", " \"Mistral-7B\": \"mistralai/Mistral-7B-Instruct-v0.2\",\n", " \"Phi-2\": \"microsoft/phi-2\",\n", " \"Gemma-2B\": \"google/gemma-2b-it\",\n", " \"TinyLlama\": \"TinyLlama/TinyLlama-1.1B-Chat-v1.0\",\n", "}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "executionInfo": { "elapsed": 11, "status": "ok", "timestamp": 1761737009179, "user": { "displayName": "Kwabena Baah-Boakye", "userId": "14758998715370101460" }, "user_tz": 240 }, "id": "KdPYdC9tmoEy" }, "outputs": [], "source": [ "# Available industries with roles\n", "AVAILABLE_INDUSTRIES = {\n", " \"Technology\": [\"Software Engineer\", \"Data Scientist\", \"Product Manager\", \"DevOps Engineer\", \"Frontend Developer\"],\n", " \"Healthcare\": [\"Registered Nurse\", \"Medical Assistant\", \"Healthcare Administrator\", \"Pharmacist\", \"Physical Therapist\"],\n", " \"Finance\": [\"Financial Analyst\", \"Accountant\", \"Investment Banker\", \"Risk Manager\", \"Portfolio Manager\"],\n", " \"Marketing\": [\"Digital Marketing Manager\", \"Content Strategist\", \"SEO Specialist\", \"Brand Manager\", \"Social Media Manager\"],\n", " \"Sales\": [\"Account Executive\", \"Sales Manager\", \"Business Development Rep\", \"Sales Engineer\", \"Customer Success Manager\"],\n", " \"Education\": [\"Teacher\", \"Curriculum Developer\", \"Academic Advisor\", \"Education Consultant\", \"Training Coordinator\"],\n", "}" ] }, { "cell_type": "markdown", "metadata": { "id": "spQDmFzOtfn0" }, "source": [ "## Load Model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "executionInfo": { "elapsed": 11, "status": "ok", "timestamp": 1761737069184, "user": { "displayName": "Kwabena Baah-Boakye", "userId": "14758998715370101460" }, "user_tz": 240 }, "id": "LBXsJk8Ith7j" }, "outputs": [], "source": [ "\n", "# Global variables for model\n", "current_model = None\n", "current_pipeline = None\n", "current_model_name = None\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "executionInfo": { "elapsed": 43, "status": "ok", "timestamp": 1761737106153, "user": { "displayName": "Kwabena Baah-Boakye", "userId": "14758998715370101460" }, "user_tz": 240 }, "id": "he0q6JGCtmRd" }, "outputs": [], "source": [ "def load_model(model_name):\n", " \"\"\"Load the selected model\"\"\"\n", " global current_model, current_pipeline, current_model_name\n", "\n", " # If model is already loaded, return\n", " if current_model_name == model_name and current_pipeline is not None:\n", " return f\"βœ… {model_name} already loaded!\"\n", "\n", " # Clear previous model\n", " if current_model is not None:\n", " del current_model\n", " del current_pipeline\n", " torch.cuda.empty_cache()\n", "\n", " print(f\"πŸ€– Loading {model_name}...\")\n", "\n", " # Quantization config\n", " bnb_config = BitsAndBytesConfig(\n", " load_in_4bit=True,\n", " bnb_4bit_quant_type=\"nf4\",\n", " bnb_4bit_compute_dtype=torch.float16,\n", " )\n", "\n", " # Load model\n", " model_path = AVAILABLE_MODELS[model_name]\n", " tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)\n", " current_model = AutoModelForCausalLM.from_pretrained(\n", " model_path,\n", " quantization_config=bnb_config,\n", " device_map=\"auto\",\n", " trust_remote_code=True\n", " )\n", "\n", " # Create pipeline\n", " current_pipeline = pipeline(\n", " \"text-generation\",\n", " model=current_model,\n", " tokenizer=tokenizer,\n", " max_new_tokens=800,\n", " temperature=0.8,\n", " top_p=0.95,\n", " do_sample=True\n", " )\n", "\n", " current_model_name = model_name\n", " return f\"βœ… {model_name} loaded successfully!\"\n" ] }, { "cell_type": "markdown", "metadata": { "id": "lnfqfrVztx3t" }, "source": [ "## Data Generation Functions" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "executionInfo": { "elapsed": 9, "status": "ok", "timestamp": 1761737187566, "user": { "displayName": "Kwabena Baah-Boakye", "userId": "14758998715370101460" }, "user_tz": 240 }, "id": "bl0iTahzt24r" }, "outputs": [], "source": [ "def generate_job_description(role, industry):\n", " \"\"\"Generate a realistic job description\"\"\"\n", " prompt = f\"\"\"Create a detailed job description for a {role} position in the {industry} industry.\n", "\n", " Include:\n", " - Job title and company type\n", " - Job overview (2-3 sentences)\n", " - Key responsibilities (4-5 bullet points)\n", " - Required qualifications (3-4 items)\n", " - Preferred skills (2-3 items)\n", "\n", " Job Description:\n", " \"\"\"\n", "\n", " result = current_pipeline(prompt, return_full_text=False)[0]['generated_text']\n", " return result.strip()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "executionInfo": { "elapsed": 5, "status": "ok", "timestamp": 1761737230115, "user": { "displayName": "Kwabena Baah-Boakye", "userId": "14758998715370101460" }, "user_tz": 240 }, "id": "XEgO5PvXuE3x" }, "outputs": [], "source": [ "def generate_matching_resume(role, industry, job_description):\n", " \"\"\"Generate a resume that matches the job description\"\"\"\n", " prompt = f\"\"\"Create a professional resume for a qualified {role} candidate applying to this position in {industry}.\n", "\n", " Job Requirements Summary:\n", " {job_description[:400]}...\n", "\n", " Generate a resume with:\n", " - Name and contact info\n", " - Professional summary (2-3 sentences)\n", " - Work experience (2-3 relevant positions with bullet points)\n", " - Skills section (matching job requirements)\n", " - Education\n", "\n", " Resume:\n", " \"\"\"\n", "\n", " result = current_pipeline(prompt, return_full_text=False)[0]['generated_text']\n", " return result.strip()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "executionInfo": { "elapsed": 9, "status": "ok", "timestamp": 1761737302930, "user": { "displayName": "Kwabena Baah-Boakye", "userId": "14758998715370101460" }, "user_tz": 240 }, "id": "RhtLuV3huNbK" }, "outputs": [], "source": [ "# main generation function\n", "def generate_synthetic_data(model_name, industry, selected_roles, num_samples):\n", " \"\"\"Main function to generate synthetic data\"\"\"\n", "\n", " # Validate inputs\n", " if not selected_roles:\n", " return \"❌ Please select at least one role!\", None, None\n", "\n", " # Load model if needed\n", " status = load_model(model_name)\n", " if \"❌\" in status:\n", " return status, None, None\n", "\n", " synthetic_data = []\n", " progress_text = f\"πŸš€ Generating data with {model_name}...\\n\\n\"\n", "\n", " for role in selected_roles:\n", " progress_text += f\"πŸ”Ή Generating {num_samples} samples for: {role}\\n\"\n", "\n", " for i in range(num_samples):\n", " # Generate job description\n", " job_desc = generate_job_description(role, industry)\n", "\n", " # Generate matching resume\n", " resume = generate_matching_resume(role, industry, job_desc)\n", "\n", " # Store the pair\n", " synthetic_data.append({\n", " 'id': len(synthetic_data) + 1,\n", " 'industry': industry,\n", " 'role': role,\n", " 'job_description': job_desc,\n", " 'resume': resume\n", " })\n", "\n", " progress_text += f\" βœ… Sample {i+1}/{num_samples}\\n\"\n", "\n", " progress_text += f\"\\n✨ Generated {len(synthetic_data)} job-resume pairs!\\n\"\n", "\n", " # Save as JSON\n", " json_file = 'synthetic_resume_data.json'\n", " with open(json_file, 'w') as f:\n", " json.dump(synthetic_data, f, indent=2)\n", "\n", " # Save as CSV\n", " csv_file = 'synthetic_resume_data.csv'\n", " df = pd.DataFrame(synthetic_data)\n", " df.to_csv(csv_file, index=False)\n", "\n", " # Create preview\n", " preview = f\"{'='*80}\\n\"\n", " preview += f\"πŸ“Š GENERATED DATA SUMMARY\\n\"\n", " preview += f\"{'='*80}\\n\\n\"\n", " preview += f\"Total Samples: {len(synthetic_data)}\\n\"\n", " preview += f\"Industry: {industry}\\n\"\n", " preview += f\"Roles: {', '.join(selected_roles)}\\n\\n\"\n", "\n", " # Show first sample\n", " sample = synthetic_data[0]\n", " preview += f\"{'='*80}\\n\"\n", " preview += f\"πŸ“„ SAMPLE #1\\n\"\n", " preview += f\"{'='*80}\\n\\n\"\n", " preview += f\"Role: {sample['role']}\\n\\n\"\n", " preview += f\"JOB DESCRIPTION:\\n{'-'*80}\\n{sample['job_description'][:400]}...\\n\\n\"\n", " preview += f\"MATCHING RESUME:\\n{'-'*80}\\n{sample['resume'][:400]}...\\n\\n\"\n", "\n", " return progress_text, preview, [json_file, csv_file]" ] }, { "cell_type": "markdown", "metadata": { "id": "5DpmmFlpuhOI" }, "source": [ "## Gradio UI" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "executionInfo": { "elapsed": 185, "status": "ok", "timestamp": 1761737389227, "user": { "displayName": "Kwabena Baah-Boakye", "userId": "14758998715370101460" }, "user_tz": 240 }, "id": "I_WI5ZHcujfv" }, "outputs": [], "source": [ "with gr.Blocks(title=\"Resume Data Generator\", theme=gr.themes.Soft()) as demo:\n", "\n", " gr.Markdown(\"\"\"\n", " # 🎯 Synthetic Resume & Job Description Generator\n", " Generate realistic job descriptions and matching resumes using AI\n", " \"\"\")\n", "\n", " with gr.Row():\n", " with gr.Column(scale=1):\n", " gr.Markdown(\"### βš™οΈ Configuration\")\n", "\n", " model_dropdown = gr.Dropdown(\n", " choices=list(AVAILABLE_MODELS.keys()),\n", " value=\"Phi-2\",\n", " label=\"Select Model\",\n", " info=\"Phi-2 is fastest, Mistral-7B is highest quality\"\n", " )\n", "\n", " industry_dropdown = gr.Dropdown(\n", " choices=list(AVAILABLE_INDUSTRIES.keys()),\n", " value=\"Technology\",\n", " label=\"Select Industry\"\n", " )\n", "\n", " roles_checkbox = gr.CheckboxGroup(\n", " choices=AVAILABLE_INDUSTRIES[\"Technology\"],\n", " value=[\"Software Engineer\"],\n", " label=\"Select Roles\",\n", " info=\"Choose one or more roles\"\n", " )\n", "\n", " num_samples_slider = gr.Slider(\n", " minimum=1,\n", " maximum=5,\n", " value=2,\n", " step=1,\n", " label=\"Samples per Role\",\n", " info=\"Number of job-resume pairs per role\"\n", " )\n", "\n", " generate_btn = gr.Button(\"πŸš€ Generate Data\", variant=\"primary\", size=\"lg\")\n", "\n", " with gr.Column(scale=2):\n", " gr.Markdown(\"### πŸ“Š Generation Progress\")\n", " progress_output = gr.Textbox(\n", " label=\"Status\",\n", " lines=10,\n", " interactive=False\n", " )\n", "\n", " gr.Markdown(\"### πŸ‘€ Data Preview\")\n", " preview_output = gr.Textbox(\n", " label=\"Sample Output\",\n", " lines=15,\n", " interactive=False\n", " )\n", "\n", " download_files = gr.Files(\n", " label=\"πŸ“₯ Download Generated Data\",\n", " interactive=False\n", " )\n", "\n", " # Update roles when industry changes\n", " def update_roles(industry):\n", " return gr.CheckboxGroup(\n", " choices=AVAILABLE_INDUSTRIES[industry],\n", " value=[AVAILABLE_INDUSTRIES[industry][0]]\n", " )\n", "\n", " industry_dropdown.change(\n", " fn=update_roles,\n", " inputs=[industry_dropdown],\n", " outputs=[roles_checkbox]\n", " )\n", "\n", " # Generate button click\n", " generate_btn.click(\n", " fn=generate_synthetic_data,\n", " inputs=[model_dropdown, industry_dropdown, roles_checkbox, num_samples_slider],\n", " outputs=[progress_output, preview_output, download_files]\n", " )\n", "\n", " gr.Markdown(\"\"\"\n", " ---\n", " ### πŸ“ Instructions:\n", " 1. Select your preferred AI model (Phi-2 recommended for speed)\n", " 2. Choose an industry\n", " 3. Select one or more job roles\n", " 4. Set number of samples per role\n", " 5. Click \"Generate Data\"\n", " 6. Download the JSON and CSV files\n", "\n", " **Note:** First generation will take longer as the model loads (~1-2 min)\n", " \"\"\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "fe77515ae60c473f9fd8f14d277d8018", "d19ac9d8e7164a81bfea0fc7755008c2", "fda8ed07f5134d17baafe93802fcbb65", "6ccd464fc32c4442ae536363f698c15a", "9cc58686388c4eba84073f915651c9fd", "b0cfd59674144bf3867d5fe71afc3d8d", "10c32a6e39c4461989507ec82faed89b", "69ef7bb7af4146e19e5395574551a5a9", "a3b5f02b92f64b10bd6d1be9f273874a", "75824129edc64bad89546692ee7c29ed", "86d074fedac54474aea3e9949c4acf16", "32a0dc8c74a14cf0be563f68b4564730", "bffe914c77f847dd9fcd90edc0b4b138", "a9536eebcb254487a1e13f56cd206790", "c6c8968c670b4609a55c47f6f0cf7a75", "09751e1d4b8b43eea2ee8d7f8db4dea2", "05cef2803a7443ec87962d4eca917b8b", "e8574b0fb8004963a0784e6716b2cb39", "7340abb9f7164daea130ea0b3e3a4a15", "a79aaa9a4f474a818ada2370ed7b9b90", "e6c1db9e739d455d8c1aec72462f4d0d", "ed1bfe71116a4220a2b40d38b758d56e", "bebe9b3d6d1349a5821a6d4ec27966db", "2c108876737247ab91d44678a0d26e5d", "d6b4b0abe1ec4302af1a8c08df86d12e", "8e264a6e23a844fc99b55cb2cf796ffe", "b6fdc5bda4464d1db53ec515c8a29326", "efa27007da42405abd22c7769cfacb86", "e60470937944412188c39401e7180ccc", "5e646c4728934ceea4d431d0057fcc43", "79753b5ddee246908626ad748bdce8e6", "1809b8d551d64a5bab1bbc4f9842c396", "1bf578436929490a8aff3fa556e59383", "5b9bb153c13943449c33f2bca460373d", "b20ae079666543d990a4a75b05e7b812", "697774c0b1b449cab9bff5a52b0af19a", "352cd919d3144a32a31291f5cf8708ef", "fe5fce525c454df3bad9119ca139f827", "6eeb1601fa634ea09c1bbce438d92250", "20d0693bbaee419996a8fb60f538bda6", "d0a0447c66b94e9b857941acf09411a4", "84ead0ed5fcc428b82569e6559da19e3", "bdec3876461546039be17f73ee22d93b", "50c012b9974648fc944dd619fc2f8fd7", "fde717f31c31403d9cea29631ae3fd3f", "3111841513b741428b0c1eb969115423", "d1a8a63d535146dfaeab7b11f9124e01", "02b22bed461842b5bd1f059a36ad15e9", "8c41d1252b1b47cf85db189ba113131b", "c0012429f772404b9294c0a7f7813668", "0e6f2a71098649b89f92c241c879322e", "0ecf653fc9b34323963d527d31394a5f", "9f021c8fdd1a4c5ebaf776cef4c3c6e5", "b874f7cb74cd484ea9d439fed902413f", "5eb1cd7471d541078083c4c65757dae3", "03db753561414d7486ec74f1e088f36f", "47b007b7088343299cf6dcdb5914b81f", "3d7e815062ad45c3850b629d690ce083", "2c02ee17eaf64c9e9ad2ebf45e28b228", "19459df330004fc296f325b1ca5e9848", "a398a448765d4039bcabd618b6cf9b9e", "60edfa9fa0664fc6a325654e2c1f82ac", "353a7f4c0a4c4dc9acf6bb1b9a82c785", "b4c7e11132fb4485848b0e3da773c670", "3b7bae1791f84ed98820981a09c77d71", "1f1d84ed40904433ad8d39f4c2fd1102", "6b7acd3a1217492e8df12d3bebb307ad", "a4e7392fda9943df909997e4396a505a", "50cdfb1773e44efb86ff58ecea54eb83", "eaffaf768b3b418e8b0155db7b1e69b7", "a0621443367a4916aa174c7a3f35b423", "b057dececf27483d8f10b2d26bd33889", "62191f7b25034058a3142f31efac4a14", "e33e29c1f46546b3804a689ceb535eb6", "eae7e9370d1a4ff1aca3ff7085abefe7", "d3866bd194224c86854f750c1e554e0a", "f7d91c33573b4d8cb7daad1fe91c0326", "c498f0f94fa9469aa747a8268b1f2b9d", "a0c170bc6704442f82511721c1679157", "a208763a089442349dd02b2124ea8ee2", "59d3f8b71282403baf2108d8572bcbba", "8098bf281c6e4d739458dac320b9b4d9", "ba1102f5a0de4f84b1b667873a695714", "1bcb249785cb43ccbb7599c24dc5b32c", "2f91c35ffac440fb809b191ddfdd4470", "0b9f3c40eefa485f9fb4cb04cc2cbfd4", "393a11916fc74cb0b06ff9883a277ce0", "a0be20313ec341618fe21dd823505f25", "c63cc27484314fb39b3e90ac08cbd634", "f9996f628896422b9d7ca245cf5778f2", "5b4c390602e543f9ba2c0b4d2791302e", "a08a2842e4304d10b70b6ff8ec4e6cf1", "6ce28d74c4fb43428c032522ad182a5a", "5affca0205774fdebbb704f6c203f5d5", "2e3152408e7c46548859aaee8bfb7026", "f7a1533042d147d98a47517020ddc381", "0570c9f9834b4a688308699091562237", "32def674c22340a2a767a19382e529af", "8b3883f4904d49ac9952e2e9d35fbb59", "639d1efbed734d64a589c56d484e3db4", 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