{ "cells": [ { "cell_type": "code", "execution_count": 22, "id": "3f7540d9", "metadata": { "id": "3f7540d9" }, "outputs": [], "source": [ "import os\n", "import requests\n", "from IPython.display import Markdown, display, update_display\n", "from openai import OpenAI\n", "from google.colab import drive\n", "from huggingface_hub import login\n", "from google.colab import userdata\n", "from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer, BitsAndBytesConfig\n", "import torch\n", "\n", "from functools import lru_cache\n", "from diffusers import StableDiffusionPipeline\n", "import gradio as gr" ] }, { "cell_type": "code", "source": [ "hf_token = userdata.get('HF_TOKEN')\n", "login(hf_token, add_to_git_credential=True)" ], "metadata": { "id": "BX0nP9tyGG6S" }, "id": "BX0nP9tyGG6S", "execution_count": 23, "outputs": [] }, { "cell_type": "code", "execution_count": 24, "id": "27c5d024", "metadata": { "id": "27c5d024" }, "outputs": [], "source": [ "TEXT_MODEL_ID = \"meta-llama/Llama-3.2-3B-Instruct\"\n", "IMAGE_MODEL_ID = \"runwayml/stable-diffusion-v1-5\"\n", "\n", "FORMAT_RULES = {\n", " \"JSON\": \"Return a JSON array containing records_count objects with consistent fields tailored to the context.\",\n", " \"CSV\": \"Return a CSV document with a header row and records_count data rows aligned to the context.\",\n", " \"Raw Text\": \"Return records_count prose entries separated by blank lines that reflect the context.\",\n", " \"Code\": \"Return records_count code snippets grouped in a single fenced block that models the context.\"\n", "}\n", "\n", "@lru_cache(maxsize=2)\n", "def load_text_components(use_quant: bool):\n", " tokenizer = AutoTokenizer.from_pretrained(TEXT_MODEL_ID)\n", " if tokenizer.pad_token is None:\n", " tokenizer.pad_token = tokenizer.eos_token\n", " if use_quant:\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", " )\n", " model = AutoModelForCausalLM.from_pretrained(\n", " TEXT_MODEL_ID,\n", " device_map=\"auto\",\n", " quantization_config=quant_config,\n", " trust_remote_code=True\n", " )\n", " else:\n", " kwargs = {\"trust_remote_code\": True}\n", " kwargs[\"device_map\"] = \"auto\"\n", " kwargs[\"torch_dtype\"] = torch.float16\n", "\n", " model = AutoModelForCausalLM.from_pretrained(TEXT_MODEL_ID, **kwargs)\n", "\n", " model.eval()\n", " return tokenizer, model\n", "\n", "def build_text_messages(style: str, context: str, return_format: str, record_count: int):\n", " context_value = context.strip() if context else \"general purpose scenario\"\n", " style_value = style.strip() if style else \"Balanced\"\n", " directive = FORMAT_RULES[return_format]\n", " system_prompt = \"You generate synthetic datasets that are high quality, diverse, and free of personally identifiable information. \" + directive + \" Ensure outputs are consistent in structure, imaginative in content, and avoid explanations.\"\n", " user_prompt = f\"Context: {context_value}\\nStyle: {style_value}\\nRecords: {record_count}\\nOutput style: {return_format}\"\n", " return [\n", " {\"role\": \"system\", \"content\": system_prompt},\n", " {\"role\": \"user\", \"content\": user_prompt}\n", " ]\n", "\n", "def generate_text_data(style: str, context: str, return_format: str, quantize: bool, record_count: int):\n", " tokenizer, model = load_text_components(bool(quantize))\n", " messages = build_text_messages(style, context, return_format, int(record_count))\n", " inputs = tokenizer.apply_chat_template(messages, return_tensors=\"pt\", add_generation_prompt=True)\n", " inputs = inputs.to(\"cuda\")\n", " attention_mask = torch.ones_like(inputs)\n", " with torch.inference_mode():\n", " generated = model.generate(\n", " input_ids=inputs,\n", " attention_mask=attention_mask,\n", " max_new_tokens=512,\n", " temperature=0.7,\n", " top_p=0.9,\n", " repetition_penalty=1.05,\n", " do_sample=True\n", " )\n", "\n", " output_ids = generated[:, inputs.shape[-1]:]\n", " text = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]\n", " return text.strip()\n", "\n", "@lru_cache(maxsize=1)\n", "def load_image_pipeline():\n", " pipeline = StableDiffusionPipeline.from_pretrained(IMAGE_MODEL_ID, torch_dtype=torch.float16)\n", " pipeline = pipeline.to(\"cuda\")\n", " return pipeline\n", "\n", "def generate_image_data(style: str, context: str, image_prompt: str, image_count: int):\n", " pipeline = load_image_pipeline()\n", " parts = []\n", " if image_prompt:\n", " parts.append(image_prompt.strip())\n", "\n", " if context:\n", " parts.append(context.strip())\n", "\n", " base = \", \".join([p for p in parts if p])\n", " if not base:\n", " base = \"Synthetic data concept visualization\"\n", "\n", " prompt = f\"{base}, {style.lower()} style\"\n", " images = pipeline(prompt, num_images_per_prompt=int(image_count), guidance_scale=7.0, num_inference_steps=30).images\n", " return images\n", "\n", "def run_generation(data_type: str, style: str, context: str, return_format: str, quantize: bool, image_prompt: str, record_count: int, image_count: int):\n", " if data_type == \"Text\":\n", " text = generate_text_data(style, context, return_format, quantize, record_count)\n", " return gr.update(value=text, visible=True), gr.update(value=[], visible=False)\n", "\n", " images = generate_image_data(style, context, image_prompt, image_count)\n", " return gr.update(value=\"\", visible=False), gr.update(value=images, visible=True)\n", "\n", "def toggle_inputs(data_type: str):\n", " if data_type == \"Text\":\n", " return (\n", " gr.update(visible=True),\n", " gr.update(visible=True),\n", " gr.update(visible=False),\n", " gr.update(visible=True),\n", " gr.update(visible=False),\n", " gr.update(value=\"\", visible=True),\n", " gr.update(value=[], visible=False)\n", " )\n", " return (\n", " gr.update(visible=False),\n", " gr.update(visible=False),\n", " gr.update(visible=True),\n", " gr.update(visible=False),\n", " gr.update(visible=True),\n", " gr.update(value=\"\", visible=False),\n", " gr.update(value=[], visible=True)\n", ")" ] }, { "cell_type": "code", "execution_count": 25, "id": "3d1c45e6", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 919, "referenced_widgets": [ "4c8d7ca79cc74a94a98b0713b20dfe8f", "6b9303db835d4a89b0b8dc61d122cee2", "ef3165923086493cb77cf0884e32e004", "141b5765befc4dabb34cb078da81293c", "70170fe59f9f4c4c8d4c31669b4231d6", "55574da55e0a4ac295f4a979a91c0eb8", "8db965e763e246bba8463cbf37ab2266", "3417a8ea7eba45dcb1a3504b34111619", "8e7ef80834774a779247865809b88ee1", "52570f3f9f3242d9891454331f20d321", "0496daff34c546dfba5f1aab3cada450", "91840a9230c64ec09a9b7211c547ac30", "83fa6555742847babffb2a0396f704b5", "ab9c5275104d4fb48a4cdfe820f85414", "31228bbd323f4afa9b9cf9c300d45554", "2a036f8bb20547d3a5b5b770fada56ae", "4525e8fefbcc42c892906f7a035dafdf", "fc65dfc49f074695a4a30a38c302ac7e", 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"483c396f-8876-4d59-db22-debe4b2bb2b8" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "It looks like you are running Gradio on a hosted Jupyter notebook, which requires `share=True`. Automatically setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n", "\n", "Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n", "* Running on public URL: https://b5fd391afd63f4968c.gradio.live\n", "\n", "This share link expires in 1 week. 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" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "
" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Loading checkpoint shards: 0%| | 0/2 [00:00 https://b5fd391afd63f4968c.gradio.live\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [] }, "metadata": {}, "execution_count": 25 } ], "source": [ "with gr.Blocks(title=\"Synthetic Data Generator\") as demo:\n", " gr.Markdown(\"## Synthetic Data Generator\")\n", " with gr.Row():\n", " data_type = gr.Radio([\"Text\", \"Image\"], label=\"Type\", value=\"Text\")\n", " style = gr.Dropdown([\"Concise\", \"Detailed\", \"Narrative\", \"Technical\", \"Tabular\"], label=\"Style\", value=\"Detailed\")\n", "\n", " context_input = gr.Textbox(label=\"Context\", lines=4, placeholder=\"Describe the entities, attributes, and purpose of the dataset.\")\n", " return_format = gr.Dropdown([\"JSON\", \"CSV\", \"Raw Text\", \"Code\"], label=\"Return Format\", value=\"JSON\")\n", " quantize = gr.Checkbox(label=\"Quantize\", value=False)\n", " record_count = gr.Slider(1, 20, value=5, step=1, label=\"Records\")\n", " image_prompt = gr.Textbox(label=\"Image Prompt\", lines=2, visible=False, placeholder=\"Detail the visual you want to synthesize.\")\n", " image_count = gr.Slider(1, 4, value=1, step=1, label=\"Images\", visible=False)\n", " generate_button = gr.Button(\"Generate\")\n", " text_output = gr.Textbox(label=\"Text Output\", lines=12)\n", " image_output = gr.Gallery(label=\"Generated Images\", visible=False, columns=2, rows=1)\n", " data_type.change(\n", " toggle_inputs,\n", " inputs=data_type,\n", " outputs=[return_format, quantize, image_prompt, record_count, image_count, text_output, image_output]\n", " )\n", " generate_button.click(\n", " run_generation,\n", " inputs=[data_type, style, context_input, return_format, quantize, image_prompt, record_count, image_count],\n", " outputs=[text_output, image_output]\n", " )\n", "\n", "\n", "demo.launch(debug=True)" ] } ], "metadata": { "language_info": { "name": "python" }, "colab": { 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