From 299f95f737fe712f11542bf2c934cd0b0419d32a Mon Sep 17 00:00:00 2001 From: KB Date: Wed, 29 Oct 2025 10:37:30 -0400 Subject: [PATCH] fix branch dependency --- ...e and Job Description Data Generator.ipynb | 5181 ----------------- 1 file changed, 5181 deletions(-) delete mode 100644 week3/community-contributions/kwabena/Week3 Resume and Job Description Data Generator.ipynb 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 deleted file mode 100644 index b4ff44f..0000000 --- a/week3/community-contributions/kwabena/Week3 Resume and Job Description Data Generator.ipynb +++ /dev/null @@ -1,5181 +0,0 @@ -{ - "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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