diff --git a/week3/community-contributions/kwabena/Week3 Resume and Job Description Data Generator.ipynb b/week3/community-contributions/kwabena/Week3 Resume and Job Description Data Generator.ipynb new file mode 100644 index 0000000..b4ff44f --- /dev/null +++ b/week3/community-contributions/kwabena/Week3 Resume and Job Description Data Generator.ipynb @@ -0,0 +1,5181 @@ +{ + "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", 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