623 lines
42 KiB
Plaintext
623 lines
42 KiB
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "-b4-SW1aGOcF"
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},
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"source": [
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"# `Latex Mind 2B Codec`\n",
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"\n",
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"Qwen2VLForConditionalGeneration"
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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": 1,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "oDmd1ZObGSel",
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"outputId": "d672ebea-c7ec-4d77-e634-484078e9ef39"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Collecting gradio\n",
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" Downloading gradio-5.14.0-py3-none-any.whl.metadata (16 kB)\n",
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" Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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" Created wheel for fpdf: filename=fpdf-1.7.2-py2.py3-none-any.whl size=40704 sha256=ca4d2c3df640c199d761f48bf1315c641e2cddc8aad3891cb9088fc05ab88413\n",
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"Installing collected packages: pydub, fpdf, uvicorn, tomlkit, semantic-version, ruff, reportlab, python-multipart, python-docx, nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, markupsafe, jedi, ffmpy, av, aiofiles, starlette, qwen-vl-utils, nvidia-cusparse-cu12, nvidia-cudnn-cu12, safehttpx, nvidia-cusolver-cu12, gradio-client, fastapi, gradio, spaces\n",
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" Attempting uninstall: nvidia-nvjitlink-cu12\n",
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" Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n",
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" Found existing installation: nvidia-cufft-cu12 11.2.3.61\n",
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" Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n",
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" Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n",
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" Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n",
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" Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n",
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"Successfully installed aiofiles-23.2.1 av-14.1.0 fastapi-0.115.8 ffmpy-0.5.0 fpdf-1.7.2 gradio-5.14.0 gradio-client-1.7.0 jedi-0.19.2 markupsafe-2.1.5 nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127 pydub-0.25.1 python-docx-1.1.2 python-multipart-0.0.20 qwen-vl-utils-0.0.10 reportlab-4.2.5 ruff-0.9.4 safehttpx-0.1.6 semantic-version-2.10.0 spaces-0.32.0 starlette-0.45.3 tomlkit-0.13.2 uvicorn-0.34.0\n"
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]
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}
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],
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"source": [
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"!pip install gradio spaces transformers accelerate numpy requests torch torchvision qwen-vl-utils av ipython reportlab fpdf python-docx pillow huggingface_hub"
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]
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{
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 682
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"id": "ovBSsRFhGbs2",
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{
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"text": [
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"The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored.\n"
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]
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},
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{
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"text": [
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"Loading latexmind...\n",
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||
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"Running Gradio in a Colab notebook requires sharing enabled. Automatically setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
|
||
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"\n",
|
||
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"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",
|
||
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"* Running on public URL: https://22ef056d58ffefce09.gradio.live\n",
|
||
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"\n",
|
||
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"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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]
|
||
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},
|
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{
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"data": {
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"text/html": [
|
||
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"<div><iframe src=\"https://22ef056d58ffefce09.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
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}
|
||
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],
|
||
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"source": [
|
||
|
|
"# Authenticate with Hugging Face\n",
|
||
|
|
"from huggingface_hub import login\n",
|
||
|
|
"\n",
|
||
|
|
"# Log in to Hugging Face using the provided token\n",
|
||
|
|
"hf_token = '--------xxx---------'\n",
|
||
|
|
"login(hf_token)\n",
|
||
|
|
"\n",
|
||
|
|
"#Demo\n",
|
||
|
|
"import gradio as gr\n",
|
||
|
|
"import spaces\n",
|
||
|
|
"from transformers import Qwen2VLForConditionalGeneration, AutoProcessor, TextIteratorStreamer\n",
|
||
|
|
"from qwen_vl_utils import process_vision_info\n",
|
||
|
|
"import torch\n",
|
||
|
|
"from PIL import Image\n",
|
||
|
|
"import os\n",
|
||
|
|
"import uuid\n",
|
||
|
|
"import io\n",
|
||
|
|
"from threading import Thread\n",
|
||
|
|
"from reportlab.lib.pagesizes import A4\n",
|
||
|
|
"from reportlab.lib.styles import getSampleStyleSheet\n",
|
||
|
|
"from reportlab.lib import colors\n",
|
||
|
|
"from reportlab.platypus import SimpleDocTemplate, Image as RLImage, Paragraph, Spacer\n",
|
||
|
|
"from reportlab.lib.units import inch\n",
|
||
|
|
"from reportlab.pdfbase import pdfmetrics\n",
|
||
|
|
"from reportlab.pdfbase.ttfonts import TTFont\n",
|
||
|
|
"import docx\n",
|
||
|
|
"from docx.enum.text import WD_ALIGN_PARAGRAPH\n",
|
||
|
|
"\n",
|
||
|
|
"# Define model options\n",
|
||
|
|
"MODEL_OPTIONS = {\n",
|
||
|
|
" \"latexmind\": \"prithivMLmods/LatexMind-2B-Codec\",\n",
|
||
|
|
"}\n",
|
||
|
|
"\n",
|
||
|
|
"# Preload models and processors into CUDA\n",
|
||
|
|
"models = {}\n",
|
||
|
|
"processors = {}\n",
|
||
|
|
"for name, model_id in MODEL_OPTIONS.items():\n",
|
||
|
|
" print(f\"Loading {name}...\")\n",
|
||
|
|
" models[name] = Qwen2VLForConditionalGeneration.from_pretrained(\n",
|
||
|
|
" model_id,\n",
|
||
|
|
" trust_remote_code=True,\n",
|
||
|
|
" torch_dtype=torch.float16\n",
|
||
|
|
" ).to(\"cuda\").eval()\n",
|
||
|
|
" processors[name] = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)\n",
|
||
|
|
"\n",
|
||
|
|
"image_extensions = Image.registered_extensions()\n",
|
||
|
|
"\n",
|
||
|
|
"def identify_and_save_blob(blob_path):\n",
|
||
|
|
" \"\"\"Identifies if the blob is an image and saves it.\"\"\"\n",
|
||
|
|
" try:\n",
|
||
|
|
" with open(blob_path, 'rb') as file:\n",
|
||
|
|
" blob_content = file.read()\n",
|
||
|
|
" try:\n",
|
||
|
|
" Image.open(io.BytesIO(blob_content)).verify() # Check if it's a valid image\n",
|
||
|
|
" extension = \".png\" # Default to PNG for saving\n",
|
||
|
|
" media_type = \"image\"\n",
|
||
|
|
" except (IOError, SyntaxError):\n",
|
||
|
|
" raise ValueError(\"Unsupported media type. Please upload a valid image.\")\n",
|
||
|
|
"\n",
|
||
|
|
" filename = f\"temp_{uuid.uuid4()}_media{extension}\"\n",
|
||
|
|
" with open(filename, \"wb\") as f:\n",
|
||
|
|
" f.write(blob_content)\n",
|
||
|
|
"\n",
|
||
|
|
" return filename, media_type\n",
|
||
|
|
"\n",
|
||
|
|
" except FileNotFoundError:\n",
|
||
|
|
" raise ValueError(f\"The file {blob_path} was not found.\")\n",
|
||
|
|
" except Exception as e:\n",
|
||
|
|
" raise ValueError(f\"An error occurred while processing the file: {e}\")\n",
|
||
|
|
"\n",
|
||
|
|
"@spaces.GPU\n",
|
||
|
|
"def qwen_inference(model_name, media_input, text_input=None):\n",
|
||
|
|
" \"\"\"Handles inference for the selected model.\"\"\"\n",
|
||
|
|
" model = models[model_name]\n",
|
||
|
|
" processor = processors[model_name]\n",
|
||
|
|
"\n",
|
||
|
|
" if isinstance(media_input, str):\n",
|
||
|
|
" media_path = media_input\n",
|
||
|
|
" if media_path.endswith(tuple([i for i in image_extensions.keys()])):\n",
|
||
|
|
" media_type = \"image\"\n",
|
||
|
|
" else:\n",
|
||
|
|
" try:\n",
|
||
|
|
" media_path, media_type = identify_and_save_blob(media_input)\n",
|
||
|
|
" except Exception as e:\n",
|
||
|
|
" raise ValueError(\"Unsupported media type. Please upload a valid image.\")\n",
|
||
|
|
"\n",
|
||
|
|
" messages = [\n",
|
||
|
|
" {\n",
|
||
|
|
" \"role\": \"user\",\n",
|
||
|
|
" \"content\": [\n",
|
||
|
|
" {\n",
|
||
|
|
" \"type\": media_type,\n",
|
||
|
|
" media_type: media_path\n",
|
||
|
|
" },\n",
|
||
|
|
" {\"type\": \"text\", \"text\": text_input},\n",
|
||
|
|
" ],\n",
|
||
|
|
" }\n",
|
||
|
|
" ]\n",
|
||
|
|
"\n",
|
||
|
|
" text = processor.apply_chat_template(\n",
|
||
|
|
" messages, tokenize=False, add_generation_prompt=True\n",
|
||
|
|
" )\n",
|
||
|
|
" image_inputs, _ = process_vision_info(messages)\n",
|
||
|
|
" inputs = processor(\n",
|
||
|
|
" text=[text],\n",
|
||
|
|
" images=image_inputs,\n",
|
||
|
|
" padding=True,\n",
|
||
|
|
" return_tensors=\"pt\",\n",
|
||
|
|
" ).to(\"cuda\")\n",
|
||
|
|
"\n",
|
||
|
|
" streamer = TextIteratorStreamer(\n",
|
||
|
|
" processor.tokenizer, skip_prompt=True, skip_special_tokens=True\n",
|
||
|
|
" )\n",
|
||
|
|
" generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024)\n",
|
||
|
|
"\n",
|
||
|
|
" thread = Thread(target=model.generate, kwargs=generation_kwargs)\n",
|
||
|
|
" thread.start()\n",
|
||
|
|
"\n",
|
||
|
|
" buffer = \"\"\n",
|
||
|
|
" for new_text in streamer:\n",
|
||
|
|
" buffer += new_text\n",
|
||
|
|
" # Remove <|im_end|> or similar tokens from the output\n",
|
||
|
|
" buffer = buffer.replace(\"<|im_end|>\", \"\")\n",
|
||
|
|
" yield buffer\n",
|
||
|
|
"\n",
|
||
|
|
"def format_plain_text(output_text):\n",
|
||
|
|
" \"\"\"Formats the output text as plain text without LaTeX delimiters.\"\"\"\n",
|
||
|
|
" # Remove LaTeX delimiters and convert to plain text\n",
|
||
|
|
" plain_text = output_text.replace(\"\\\\(\", \"\").replace(\"\\\\)\", \"\").replace(\"\\\\[\", \"\").replace(\"\\\\]\", \"\")\n",
|
||
|
|
" return plain_text\n",
|
||
|
|
"\n",
|
||
|
|
"def generate_document(media_path, output_text, file_format, font_size, line_spacing, alignment, image_size):\n",
|
||
|
|
" \"\"\"Generates a document with the input image and plain text output.\"\"\"\n",
|
||
|
|
" plain_text = format_plain_text(output_text)\n",
|
||
|
|
" if file_format == \"pdf\":\n",
|
||
|
|
" return generate_pdf(media_path, plain_text, font_size, line_spacing, alignment, image_size)\n",
|
||
|
|
" elif file_format == \"docx\":\n",
|
||
|
|
" return generate_docx(media_path, plain_text, font_size, line_spacing, alignment, image_size)\n",
|
||
|
|
"\n",
|
||
|
|
"def generate_pdf(media_path, plain_text, font_size, line_spacing, alignment, image_size):\n",
|
||
|
|
" \"\"\"Generates a PDF document.\"\"\"\n",
|
||
|
|
" filename = f\"output_{uuid.uuid4()}.pdf\"\n",
|
||
|
|
" doc = SimpleDocTemplate(\n",
|
||
|
|
" filename,\n",
|
||
|
|
" pagesize=A4,\n",
|
||
|
|
" rightMargin=inch,\n",
|
||
|
|
" leftMargin=inch,\n",
|
||
|
|
" topMargin=inch,\n",
|
||
|
|
" bottomMargin=inch\n",
|
||
|
|
" )\n",
|
||
|
|
" styles = getSampleStyleSheet()\n",
|
||
|
|
" styles[\"Normal\"].fontSize = int(font_size)\n",
|
||
|
|
" styles[\"Normal\"].leading = int(font_size) * line_spacing\n",
|
||
|
|
" styles[\"Normal\"].alignment = {\n",
|
||
|
|
" \"Left\": 0,\n",
|
||
|
|
" \"Center\": 1,\n",
|
||
|
|
" \"Right\": 2,\n",
|
||
|
|
" \"Justified\": 4\n",
|
||
|
|
" }[alignment]\n",
|
||
|
|
"\n",
|
||
|
|
" story = []\n",
|
||
|
|
"\n",
|
||
|
|
" # Add image with size adjustment\n",
|
||
|
|
" image_sizes = {\n",
|
||
|
|
" \"Small\": (200, 200),\n",
|
||
|
|
" \"Medium\": (400, 400),\n",
|
||
|
|
" \"Large\": (600, 600)\n",
|
||
|
|
" }\n",
|
||
|
|
" img = RLImage(media_path, width=image_sizes[image_size][0], height=image_sizes[image_size][1])\n",
|
||
|
|
" story.append(img)\n",
|
||
|
|
" story.append(Spacer(1, 12))\n",
|
||
|
|
"\n",
|
||
|
|
" # Add plain text output\n",
|
||
|
|
" text = Paragraph(plain_text, styles[\"Normal\"])\n",
|
||
|
|
" story.append(text)\n",
|
||
|
|
"\n",
|
||
|
|
" doc.build(story)\n",
|
||
|
|
" return filename\n",
|
||
|
|
"\n",
|
||
|
|
"def generate_docx(media_path, plain_text, font_size, line_spacing, alignment, image_size):\n",
|
||
|
|
" \"\"\"Generates a DOCX document.\"\"\"\n",
|
||
|
|
" filename = f\"output_{uuid.uuid4()}.docx\"\n",
|
||
|
|
" doc = docx.Document()\n",
|
||
|
|
"\n",
|
||
|
|
" # Add image with size adjustment\n",
|
||
|
|
" image_sizes = {\n",
|
||
|
|
" \"Small\": docx.shared.Inches(2),\n",
|
||
|
|
" \"Medium\": docx.shared.Inches(4),\n",
|
||
|
|
" \"Large\": docx.shared.Inches(6)\n",
|
||
|
|
" }\n",
|
||
|
|
" doc.add_picture(media_path, width=image_sizes[image_size])\n",
|
||
|
|
" doc.add_paragraph()\n",
|
||
|
|
"\n",
|
||
|
|
" # Add plain text output\n",
|
||
|
|
" paragraph = doc.add_paragraph()\n",
|
||
|
|
" paragraph.paragraph_format.line_spacing = line_spacing\n",
|
||
|
|
" paragraph.paragraph_format.alignment = {\n",
|
||
|
|
" \"Left\": WD_ALIGN_PARAGRAPH.LEFT,\n",
|
||
|
|
" \"Center\": WD_ALIGN_PARAGRAPH.CENTER,\n",
|
||
|
|
" \"Right\": WD_ALIGN_PARAGRAPH.RIGHT,\n",
|
||
|
|
" \"Justified\": WD_ALIGN_PARAGRAPH.JUSTIFY\n",
|
||
|
|
" }[alignment]\n",
|
||
|
|
" run = paragraph.add_run(plain_text)\n",
|
||
|
|
" run.font.size = docx.shared.Pt(int(font_size))\n",
|
||
|
|
"\n",
|
||
|
|
" doc.save(filename)\n",
|
||
|
|
" return filename\n",
|
||
|
|
"\n",
|
||
|
|
"# CSS for output styling\n",
|
||
|
|
"css = \"\"\"\n",
|
||
|
|
" #output {\n",
|
||
|
|
" height: 500px;\n",
|
||
|
|
" overflow: auto;\n",
|
||
|
|
" border: 1px solid #ccc;\n",
|
||
|
|
" }\n",
|
||
|
|
".submit-btn {\n",
|
||
|
|
" background-color: #cf3434 !important;\n",
|
||
|
|
" color: white !important;\n",
|
||
|
|
"}\n",
|
||
|
|
".submit-btn:hover {\n",
|
||
|
|
" background-color: #ff2323 !important;\n",
|
||
|
|
"}\n",
|
||
|
|
".download-btn {\n",
|
||
|
|
" background-color: #35a6d6 !important;\n",
|
||
|
|
" color: white !important;\n",
|
||
|
|
"}\n",
|
||
|
|
".download-btn:hover {\n",
|
||
|
|
" background-color: #22bcff !important;\n",
|
||
|
|
"}\n",
|
||
|
|
"\"\"\"\n",
|
||
|
|
"\n",
|
||
|
|
"# Gradio app setup\n",
|
||
|
|
"with gr.Blocks(css=css) as demo:\n",
|
||
|
|
" gr.Markdown(\"# Latexmind\")\n",
|
||
|
|
"\n",
|
||
|
|
" with gr.Tab(label=\"Image Input\"):\n",
|
||
|
|
"\n",
|
||
|
|
" with gr.Row():\n",
|
||
|
|
" with gr.Column():\n",
|
||
|
|
" model_choice = gr.Dropdown(\n",
|
||
|
|
" label=\"Model Selection\",\n",
|
||
|
|
" choices=list(MODEL_OPTIONS.keys()),\n",
|
||
|
|
" value=\"latexmind\"\n",
|
||
|
|
" )\n",
|
||
|
|
" input_media = gr.File(\n",
|
||
|
|
" label=\"Upload Image\", type=\"filepath\"\n",
|
||
|
|
" )\n",
|
||
|
|
" text_input = gr.Textbox(label=\"Question\", placeholder=\"Ask a question about the image...\")\n",
|
||
|
|
" submit_btn = gr.Button(value=\"Submit\", elem_classes=\"submit-btn\")\n",
|
||
|
|
"\n",
|
||
|
|
" with gr.Column():\n",
|
||
|
|
" output_text = gr.Textbox(label=\"Output Text\", lines=10)\n",
|
||
|
|
" plain_text_output = gr.Textbox(label=\"Standardized Plain Text\", lines=10)\n",
|
||
|
|
"\n",
|
||
|
|
" submit_btn.click(\n",
|
||
|
|
" qwen_inference, [model_choice, input_media, text_input], [output_text]\n",
|
||
|
|
" ).then(\n",
|
||
|
|
" lambda output_text: format_plain_text(output_text), [output_text], [plain_text_output]\n",
|
||
|
|
" )\n",
|
||
|
|
"\n",
|
||
|
|
" # Add examples directly usable by clicking\n",
|
||
|
|
" with gr.Row():\n",
|
||
|
|
" with gr.Column():\n",
|
||
|
|
" line_spacing = gr.Dropdown(\n",
|
||
|
|
" choices=[0.5, 1.0, 1.15, 1.5, 2.0, 2.5, 3.0],\n",
|
||
|
|
" value=1.5,\n",
|
||
|
|
" label=\"Line Spacing\"\n",
|
||
|
|
" )\n",
|
||
|
|
" font_size = gr.Dropdown(\n",
|
||
|
|
" choices=[\"8\", \"10\", \"12\", \"14\", \"16\", \"18\", \"20\", \"22\", \"24\"],\n",
|
||
|
|
" value=\"18\",\n",
|
||
|
|
" label=\"Font Size\"\n",
|
||
|
|
" )\n",
|
||
|
|
" alignment = gr.Dropdown(\n",
|
||
|
|
" choices=[\"Left\", \"Center\", \"Right\", \"Justified\"],\n",
|
||
|
|
" value=\"Justified\",\n",
|
||
|
|
" label=\"Text Alignment\"\n",
|
||
|
|
" )\n",
|
||
|
|
" image_size = gr.Dropdown(\n",
|
||
|
|
" choices=[\"Small\", \"Medium\", \"Large\"],\n",
|
||
|
|
" value=\"Small\",\n",
|
||
|
|
" label=\"Image Size\"\n",
|
||
|
|
" )\n",
|
||
|
|
" file_format = gr.Radio([\"pdf\", \"docx\"], label=\"File Format\", value=\"pdf\")\n",
|
||
|
|
" get_document_btn = gr.Button(value=\"Get Document\", elem_classes=\"download-btn\")\n",
|
||
|
|
"\n",
|
||
|
|
" get_document_btn.click(\n",
|
||
|
|
" generate_document, [input_media, output_text, file_format, font_size, line_spacing, alignment, image_size], gr.File(label=\"Download Document\")\n",
|
||
|
|
" )\n",
|
||
|
|
"\n",
|
||
|
|
"demo.launch(debug=True)"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"accelerator": "GPU",
|
||
|
|
"colab": {
|
||
|
|
"gpuType": "L4",
|
||
|
|
"machine_shape": "hm",
|
||
|
|
"provenance": []
|
||
|
|
},
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"name": "python"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 0
|
||
|
|
}
|