Delete week3/community-contributions/llm.wk3synthetic-data-creator.ipynb
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bluebells1
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"cells": [
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"- This creates dummy / test data from a usecase provided by the user.\n",
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"- The usecase can be as simple or complex as the user wants (I've tested both and the results are good).\n",
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"- I've used a Phi3 model as I'm having issues with llama access on Hugging Face."
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]
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "s7ERjTCEKSi_"
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},
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"outputs": [],
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"source": [
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"!pip install -q requests torch bitsandbytes transformers sentencepiece accelerate openai httpx==0.27.2"
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]
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "GG5VMcmhcA2N"
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},
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"outputs": [],
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"source": [
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"import os\n",
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"import requests\n",
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"from openai import OpenAI\n",
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"import gradio as gr\n",
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"from IPython.display import Markdown, display, update_display\n",
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"from huggingface_hub import login\n",
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"from google.colab import userdata\n",
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"from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer, BitsAndBytesConfig\n",
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"import torch\n",
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"import json\n",
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"import re\n",
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"import pandas as pd\n",
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"import io"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "UfL-2XNicpEB"
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},
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"outputs": [],
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"source": [
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"# constants\n",
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"\n",
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"OPENAI = 'gpt-4o-mini'\n",
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"PHI3 = \"microsoft/Phi-3-mini-4k-instruct\"\n",
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"\n",
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"limit = 100\n",
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"max_tokens = 1000\n",
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"temperature = 0.3"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "ZQ0dcQ6hdTPo"
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},
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"outputs": [],
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"source": [
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"# keys\n",
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"\n",
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"openai_api_key = userdata.get('OPENAI_API_KEY')\n",
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"openai = OpenAI(api_key=openai_api_key)\n",
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"\n",
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"hf_token = userdata.get('HF_TOKEN')\n",
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"login(hf_token, add_to_git_credential=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "2eHsLdYgd2d_"
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},
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"outputs": [],
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"source": [
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"system_prompt = f\"\"\"You create synthetic datasets for testing purposes. Based on the use case description, generate a CSV dataset with appropriate columns and a maximum of {limit} rows\n",
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"of realistic data.\n",
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"\n",
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"IMPORTANT RULES:\n",
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"1. Return ONLY the CSV data with headers and ensure there are no duplicate headers\n",
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"2. No explanatory text before or after\n",
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"3. No markdown formatting or code fences\n",
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"4. No quotation marks around the entire response\n",
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"5. Start directly with the column headers\n",
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"\n",
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"Format: column1 (e.g. customer_id),column2 (e.g. country),column3 (e.g. age)\n",
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"row1data,row1data,row1data\n",
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"row2data,row2data,row2data\"\"\"\n",
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"\n",
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"def data_user_prompt(usecase):\n",
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" user_prompt = \"Create a synthetic dataset for the use case provided below: \"\n",
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" user_prompt += usecase\n",
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" user_prompt += f\" Respond in csv with appropriate headers. Do not include any other explanatory text, markdown formatting or code fences, or quotation marks around the entire response. \\\n",
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" Limit the rows in the dataset to {limit}.\"\n",
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" return user_prompt\n",
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"\n",
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"messages = [\n",
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" {\"role\":\"system\",\"content\":system_prompt},\n",
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" {\"role\":\"user\",\"content\":data_user_prompt(usecase)}\n",
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"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "necoAEc1gNPF"
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},
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"outputs": [],
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"source": [
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"def dataset_call(usecase):\n",
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"\n",
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" #quantisation\n",
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" quant_config = BitsAndBytesConfig(\n",
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" load_in_4bit=True,\n",
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" bnb_4bit_use_double_quant=True,\n",
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" bnb_4bit_quant_type=\"nf4\",\n",
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" bnb_4bit_compute_dtype=torch.bfloat16\n",
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" )\n",
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"\n",
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" #tokenization\n",
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" tokenizer = AutoTokenizer.from_pretrained(PHI3)\n",
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" tokenizer.pad_token = tokenizer.eos_token\n",
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"\n",
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" #model\n",
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" model = AutoModelForCausalLM.from_pretrained(PHI3, quantization_config=quant_config, device_map=\"auto\")\n",
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"\n",
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" #inputs & outputs\n",
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" inputs = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n",
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" model_inputs = tokenizer(inputs, return_tensors=\"pt\").to(model.device)\n",
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" #streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)\n",
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"\n",
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" with torch.no_grad():\n",
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" outputs = model.generate(**model_inputs, max_new_tokens=max_tokens,do_sample=True, temperature=temperature)\n",
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"\n",
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" response = tokenizer.decode(outputs[0][len(model_inputs['input_ids'][0]):],skip_special_tokens=True)\n",
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" return response.strip()\n",
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" print(response.strip())\n"
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]
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},
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "g8zEBraI0grT"
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},
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"outputs": [],
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"source": [
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"# convert csv string into panda\n",
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"\n",
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"def csv_handler(csv_string):\n",
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"\n",
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" try:\n",
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" # Convert CSV string to DataFrame\n",
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" df = pd.read_csv(io.StringIO(csv_string))\n",
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" return df\n",
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" except Exception as e:\n",
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" # Return error message as DataFrame if parsing fails\n",
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" error_df = pd.DataFrame({\"Error\": [f\"Failed to parse CSV: {str(e)}\"]})\n",
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" return error_df\n",
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" print(df, error_df)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "vLPsusTL1zNB"
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},
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"outputs": [],
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"source": [
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"# usecase to csv_string\n",
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"\n",
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"def usecase_to_csv(usecase):\n",
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" try:\n",
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" # Get CSV string from your LLM\n",
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" csv_string = dataset_call(usecase)\n",
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"\n",
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" # Process into DataFrame for Gradio display\n",
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" df = csv_handler(csv_string)\n",
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"\n",
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" return df\n",
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"\n",
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" except Exception as e:\n",
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" error_df = pd.DataFrame({\"Error\": [f\"LLM processing failed: {str(e)}\"]})\n",
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" return error_df, \"\", gr.update(visible=False)\n",
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"\n",
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" print(df, error_df)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "H3WTLa9a2Rdy"
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},
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"outputs": [],
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"source": [
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"def download_csv(csv_string):\n",
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" if csv_string:\n",
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" return csv_string\n",
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" return \"\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "XhMVSrVhjYvz"
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},
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"outputs": [],
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"source": [
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"#test\n",
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"usecase = \"A financial services company is looking for synthetic data to test its Expected Credit Losses (ECL) model under IFRS9.\"\n",
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"#dataset_call(usecase)\n",
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"usecase_to_csv(usecase)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "z3Ze4o2qjs5y"
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},
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"outputs": [],
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"source": [
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"\n",
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"demo = gr.Interface(\n",
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" fn = usecase_to_csv,\n",
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" inputs = gr.Textbox(lines=5,label=\"Describe your usecase\",placeholder=\"Describe the dataset you would like to create and how you will use it\"),\n",
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" outputs = gr.DataFrame(label=\"Here is your dataset!\",interactive=True),\n",
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" title = \"Friendly Neighbourhood Synthetic Data Creator!\",\n",
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" description = \"Let me know your use case for synthetic data and I will create it for you.\",\n",
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" examples=[\n",
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" \"Generate a dataset of 10 employees with name, department, salary, and years of experience\",\n",
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" \"Create sample e-commerce data with product names, categories, prices, and ratings\",\n",
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" \"Generate customer survey responses with demographics and satisfaction scores\",\n",
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" \"A financial services company is looking for synthetic data to test its Expected Credit Losses (ECL) model under IFRS9.\"\n",
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" ]\n",
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")\n",
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"\n",
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"demo.launch(debug=True)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "ck1qdmbHo_G3"
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"accelerator": "GPU",
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"colab": {
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"authorship_tag": "ABX9TyOay+EACzwO0uXDLuayhscX",
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"gpuType": "L4",
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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