1767 lines
65 KiB
Plaintext
1767 lines
65 KiB
Plaintext
{
|
||
"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",
|
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"from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer, BitsAndBytesConfig\n",
|
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"import torch\n",
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"\n",
|
||
"from functools import lru_cache\n",
|
||
"from diffusers import StableDiffusionPipeline\n",
|
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"import gradio as gr"
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]
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},
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{
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||
"cell_type": "code",
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||
"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": []
|
||
},
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||
{
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"cell_type": "code",
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"execution_count": 24,
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"id": "27c5d024",
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||
"metadata": {
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||
"id": "27c5d024"
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},
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"outputs": [],
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||
"source": [
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"TEXT_MODEL_ID = \"meta-llama/Llama-3.2-3B-Instruct\"\n",
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"IMAGE_MODEL_ID = \"runwayml/stable-diffusion-v1-5\"\n",
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"\n",
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"FORMAT_RULES = {\n",
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" \"JSON\": \"Return a JSON array containing records_count objects with consistent fields tailored to the context.\",\n",
|
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" \"CSV\": \"Return a CSV document with a header row and records_count data rows aligned to the context.\",\n",
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" \"Raw Text\": \"Return records_count prose entries separated by blank lines that reflect the context.\",\n",
|
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" \"Code\": \"Return records_count code snippets grouped in a single fenced block that models the context.\"\n",
|
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"}\n",
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"\n",
|
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"@lru_cache(maxsize=2)\n",
|
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"def load_text_components(use_quant: bool):\n",
|
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" tokenizer = AutoTokenizer.from_pretrained(TEXT_MODEL_ID)\n",
|
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" if tokenizer.pad_token is None:\n",
|
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" tokenizer.pad_token = tokenizer.eos_token\n",
|
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" if use_quant:\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_compute_dtype=torch.bfloat16,\n",
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" bnb_4bit_quant_type=\"nf4\"\n",
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" )\n",
|
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" model = AutoModelForCausalLM.from_pretrained(\n",
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" TEXT_MODEL_ID,\n",
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" device_map=\"auto\",\n",
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" quantization_config=quant_config,\n",
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" trust_remote_code=True\n",
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" )\n",
|
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" else:\n",
|
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" kwargs = {\"trust_remote_code\": True}\n",
|
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" kwargs[\"device_map\"] = \"auto\"\n",
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" kwargs[\"torch_dtype\"] = torch.float16\n",
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"\n",
|
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" model = AutoModelForCausalLM.from_pretrained(TEXT_MODEL_ID, **kwargs)\n",
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"\n",
|
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" model.eval()\n",
|
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" return tokenizer, model\n",
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"\n",
|
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"def build_text_messages(style: str, context: str, return_format: str, record_count: int):\n",
|
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" context_value = context.strip() if context else \"general purpose scenario\"\n",
|
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" style_value = style.strip() if style else \"Balanced\"\n",
|
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" directive = FORMAT_RULES[return_format]\n",
|
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" 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",
|
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" 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",
|
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" {\"role\": \"user\", \"content\": user_prompt}\n",
|
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" ]\n",
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"\n",
|
||
"def generate_text_data(style: str, context: str, return_format: str, quantize: bool, record_count: int):\n",
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" tokenizer, model = load_text_components(bool(quantize))\n",
|
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" messages = build_text_messages(style, context, return_format, int(record_count))\n",
|
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" inputs = tokenizer.apply_chat_template(messages, return_tensors=\"pt\", add_generation_prompt=True)\n",
|
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" inputs = inputs.to(\"cuda\")\n",
|
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" attention_mask = torch.ones_like(inputs)\n",
|
||
" with torch.inference_mode():\n",
|
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" generated = model.generate(\n",
|
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" 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",
|
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"\n",
|
||
"@lru_cache(maxsize=1)\n",
|
||
"def load_image_pipeline():\n",
|
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" 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",
|
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"\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",
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"\n",
|
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" prompt = f\"{base}, {style.lower()} style\"\n",
|
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" images = pipeline(prompt, num_images_per_prompt=int(image_count), guidance_scale=7.0, num_inference_steps=30).images\n",
|
||
" return images\n",
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"\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",
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" text = generate_text_data(style, context, return_format, quantize, record_count)\n",
|
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" return gr.update(value=text, visible=True), gr.update(value=[], visible=False)\n",
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"\n",
|
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" images = generate_image_data(style, context, image_prompt, image_count)\n",
|
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" return gr.update(value=\"\", visible=False), gr.update(value=images, visible=True)\n",
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"\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": {
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||
"base_uri": "https://localhost:8080/",
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"height": 919,
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"referenced_widgets": [
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]
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},
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||
"id": "3d1c45e6",
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||
"outputId": "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": [
|
||
"<IPython.core.display.HTML object>"
|
||
],
|
||
"text/html": [
|
||
"<div><iframe src=\"https://b5fd391afd63f4968c.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
|
||
]
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "4c8d7ca79cc74a94a98b0713b20dfe8f"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n",
|
||
"Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n",
|
||
"Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n",
|
||
"Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
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"text/plain": [
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"Loading pipeline components...: 0%| | 0/7 [00:00<?, ?it/s]"
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],
|
||
"application/vnd.jupyter.widget-view+json": {
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||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "91840a9230c64ec09a9b7211c547ac30"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
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{
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"output_type": "display_data",
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],
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"application/vnd.jupyter.widget-view+json": {
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"version_major": 2,
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||
"version_minor": 0,
|
||
"model_id": "192a0110e8064f56ae288e0781fa4583"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n",
|
||
"Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
|
||
]
|
||
},
|
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{
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"output_type": "display_data",
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"data": {
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],
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"application/vnd.jupyter.widget-view+json": {
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"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "a895f63ec6e0486eaf253775142e0778"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Keyboard interruption in main thread... closing server.\n",
|
||
"Killing tunnel 127.0.0.1:7862 <> 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": {
|
||
"provenance": [],
|
||
"history_visible": true,
|
||
"gpuType": "T4"
|
||
},
|
||
"accelerator": "GPU",
|
||
"kernelspec": {
|
||
"name": "python3",
|
||
"display_name": "Python 3"
|
||
},
|
||
"widgets": {
|
||
"application/vnd.jupyter.widget-state+json": {
|
||
"4c8d7ca79cc74a94a98b0713b20dfe8f": {
|
||
"model_module": "@jupyter-widgets/controls",
|
||
"model_name": "HBoxModel",
|
||
"model_module_version": "1.5.0",
|
||
"state": {
|
||
"_dom_classes": [],
|
||
"_model_module": "@jupyter-widgets/controls",
|
||
"_model_module_version": "1.5.0",
|
||
"_model_name": "HBoxModel",
|
||
"_view_count": null,
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