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"source": [
"!pip install -q requests torch bitsandbytes transformers sentencepiece accelerate openai gradio"
]
},
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"#imports\n",
"\n",
"import time\n",
"from io import StringIO\n",
"import torch\n",
"import numpy as np\n",
"import pandas as pd\n",
"import random\n",
"from openai import OpenAI\n",
"from sqlalchemy import create_engine\n",
"from google.colab import drive, userdata\n",
"import gradio as gr\n",
"from huggingface_hub import login\n",
"from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig"
],
"metadata": {
"id": "JXpWOzKve7kr"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Model Constants\n",
"LLAMA = \"meta-llama/Meta-Llama-3.1-8B-Instruct\""
],
"metadata": {
"id": "rcv0lCS5GRPX"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Authentication\n",
"\n",
"hf_token = userdata.get(\"HF_TOKEN\")\n",
"openai_api_key = userdata.get(\"OPENAI_API_KEY\")\n",
"if not hf_token or not openai_api_key:\n",
" raise ValueError(\"Missing HF_TOKEN or OPENAI_API_KEY. Set them as environment variables.\")\n",
"\n",
"login(hf_token, add_to_git_credential=True)\n",
"openai = OpenAI(api_key=openai_api_key)"
],
"metadata": {
"id": "3XS-s_CwFSQU"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Tokenizer Setup\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(LLAMA)\n",
"tokenizer.pad_token = tokenizer.eos_token"
],
"metadata": {
"id": "oRdmdzXoF_f9",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 113,
"referenced_widgets": [
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]
},
"outputId": "ad3d1ca1-b6c1-4456-c563-41c7eb1d283c"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"tokenizer_config.json: 0%| | 0.00/55.4k [00:00, ?B/s]"
],
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},
{
"output_type": "display_data",
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"tokenizer.json: 0%| | 0.00/9.09M [00:00, ?B/s]"
],
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"version_major": 2,
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}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
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"special_tokens_map.json: 0%| | 0.00/296 [00:00, ?B/s]"
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}
]
},
{
"cell_type": "code",
"source": [
"# Model Quantization for Performance Optimization\n",
"\n",
"quant_config = BitsAndBytesConfig(\n",
" load_in_4bit=True,\n",
" bnb_4bit_use_double_quant=True,\n",
" bnb_4bit_compute_dtype=torch.bfloat16,\n",
" bnb_4bit_quant_type=\"nf4\"\n",
")"
],
"metadata": {
"id": "kRN0t2yrGmAe"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Load Model Efficiency\n",
"\n",
"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
"model = AutoModelForCausalLM.from_pretrained(LLAMA, device_map=\"auto\", quantization_config=quant_config)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 305,
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},
"id": "fYPyudKHGuE9",
"outputId": "b4925c33-f1b4-4787-c0f5-9531ff8cdeec"
},
"execution_count": null,
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{
"output_type": "display_data",
"data": {
"text/plain": [
"config.json: 0%| | 0.00/855 [00:00, ?B/s]"
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}
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{
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{
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"data": {
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{
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{
"output_type": "display_data",
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{
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"Loading checkpoint shards: 0%| | 0/4 [00:00, ?it/s]"
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{
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]
},
{
"cell_type": "code",
"source": [
"def generate_ev_driver(num_records, address_type):\n",
" # Adjusting the prompt based on checkbox selection\n",
" address_prompts = {\n",
" \"international\": f\"Generate {num_records} rows of synthetic personal data with international addresses and phone numbers.\",\n",
" \"us_only\": f\"Generate {num_records} rows of synthetic personal data with U.S.-only addresses and phone numbers.\",\n",
" \"us_international\": f\"Generate {num_records} rows of synthetic personal data with a mix of U.S. and international addresses and phone numbers.\",\n",
" \"americas\": f\"Generate {num_records} rows of synthetic personal data with a mix of U.S., Canada, Central America, and South America addresses and phone numbers.\",\n",
" \"europe\": f\"Generate {num_records} rows of synthetic personal data with Europe-only addresses and phone numbers.\",\n",
" }\n",
"\n",
" address_prompt = address_prompts.get(address_type, \"Generate synthetic personal data.\")\n",
" # Generate unique driver IDs\n",
" driver_ids = random.sample(range(1, 1000001), num_records)\n",
"\n",
" user_prompt = f\"\"\"\n",
" {address_prompt}\n",
" Each row should include:\n",
" - driverid (unique from the provided list: {driver_ids})\n",
" - first_name (string)\n",
" - last_name (string)\n",
" - email (string)\n",
" - phone_number (string)\n",
" - address (string)\n",
" - city (string)\n",
" - state (string)\n",
" - zip_code (string)\n",
" - country (string)\n",
"\n",
" Ensure the CSV format is valid, with proper headers and comma separation.\n",
" \"\"\"\n",
"\n",
" response = openai.chat.completions.create(\n",
" model=\"gpt-4o-mini\",\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": \"You are a helpful assistant that generates structured CSV data.\"},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ]\n",
" )\n",
"\n",
" # Call the new function to clean and extract the CSV data\n",
" return clean_and_extract_csv(response)"
],
"metadata": {
"id": "9q9ccNr8fMyg"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def clean_and_extract_csv(response):\n",
" # Clean up the response and remove the last occurrence of the code block formatting\n",
" csv_data = response.choices[0].message.content.strip()\n",
" csv_data = csv_data.rsplit(\"```\", 1)[0].strip()\n",
"\n",
" # Define header and split the content to extract the data\n",
" header = \"driverid,first_name,last_name,email,phone_number,address,city,state,zip_code,country\"\n",
" _, *content = csv_data.split(header, 1)\n",
"\n",
" # Return the cleaned CSV data along with the header\n",
" return header + content[0].split(\"\\n\\n\")[0] if content else csv_data"
],
"metadata": {
"id": "So1aGRNJBUyv"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def update_dataset(num_records, address_type):\n",
" response = generate_ev_driver(num_records, address_type)\n",
"\n",
" # Convert response to DataFrame\n",
" try:\n",
" df = pd.read_csv(StringIO(response))\n",
" except Exception as e:\n",
" return pd.DataFrame(), f\"Error parsing dataset: {str(e)}\"\n",
"\n",
" return df, response"
],
"metadata": {
"id": "T0KxUm2yYtuQ"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Function to handle address type selection\n",
"def check_address_selection(selected_type):\n",
" if not selected_type:\n",
" # Return the error message and set button to yellow and disabled\n",
" return (\n",
" \"⚠️ Address type is required. Please select one.\",\n",
" gr.update(interactive=False, elem_classes=\"yellow_btn\")\n",
" )\n",
" # Return success message and set button to blue and enabled\n",
" return (\n",
" \"Ready to generate dataset.\",\n",
" gr.update(interactive=True, elem_classes=\"blue_btn\")\n",
" )\n"
],
"metadata": {
"id": "z5pFDbnTz-fP"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Gradio UI\n",
"with gr.Blocks() as app:\n",
" gr.Markdown(\"## Dynamic CSV Dataset Viewer\")\n",
"\n",
" num_records_slider = gr.Slider(minimum=5, maximum=50, step=5, value=20, label=\"Number of Records\")\n",
"\n",
" with gr.Row(equal_height=True):\n",
" address_type_radio = gr.Radio(\n",
" [\"us_only\", \"international\", \"us_international\", \"americas\", \"europe\"],\n",
" value=\"\",\n",
" label=\"Address and Phone Type\",\n",
" info=\"Select the type of addresses and phone numbers\"\n",
" )\n",
" status_text = gr.Markdown(\n",
" \"⚠️ Please select an address type above to proceed.\",\n",
" elem_id=\"status_text\"\n",
" )\n",
"\n",
" generate_btn = gr.Button(\"Generate Data\", interactive=True, elem_id=\"generate_btn\")\n",
"\n",
" response_text = gr.Textbox(value=\"\", label=\"Generated Driver List CSV\", interactive=False)\n",
" dataframe_output = gr.Dataframe(value=pd.DataFrame(), label=\"Generated Driver List Dataset\")\n",
"\n",
" # Update status text and button style dynamically\n",
" address_type_radio.change(fn=check_address_selection, inputs=[address_type_radio], outputs=[status_text, generate_btn])\n",
"\n",
" generate_btn.click(update_dataset, inputs=[num_records_slider, address_type_radio], outputs=[dataframe_output, response_text])\n",
"\n",
" # Custom CSS for button colors\n",
" app.css = \"\"\"\n",
" .blue_btn {\n",
" background-color: green;\n",
" color: white;\n",
" }\n",
" \"\"\"\n",
"\n",
"app.launch(share=True) # Ensure sharing is enabled in Colab"
],
"metadata": {
"id": "z3K6PfAiL2ZA",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 591
},
"outputId": "50cd6ee9-8229-4f58-ad1f-da67990dfc28"
},
"execution_count": null,
"outputs": [
{
"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://2d1cc8e9fab628c1ca.gradio.live\n",
"\n",
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
]
},
{
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