343 lines
14 KiB
Plaintext
343 lines
14 KiB
Plaintext
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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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"**Hoags-2B-Exp**\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": null,
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"metadata": {
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"id": "oDmd1ZObGSel"
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},
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"outputs": [],
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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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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "ovBSsRFhGbs2"
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},
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"outputs": [],
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"source": [
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"# Authenticate with Hugging Face\n",
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"from huggingface_hub import login\n",
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"\n",
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"# Log in to Hugging Face using the provided token\n",
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"hf_token = '---xxxx-xxx-xxx---'\n",
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"login(hf_token)\n",
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"\n",
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"#Demo\n",
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"import gradio as gr\n",
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"import spaces\n",
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"from transformers import Qwen2VLForConditionalGeneration, AutoProcessor, TextIteratorStreamer\n",
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"from qwen_vl_utils import process_vision_info\n",
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"import torch\n",
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"from PIL import Image\n",
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"import os\n",
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"import uuid\n",
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"import io\n",
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"from threading import Thread\n",
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"from reportlab.lib.pagesizes import A4\n",
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"from reportlab.lib.styles import getSampleStyleSheet\n",
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"from reportlab.lib import colors\n",
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"from reportlab.platypus import SimpleDocTemplate, Image as RLImage, Paragraph, Spacer\n",
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"from reportlab.lib.units import inch\n",
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"from reportlab.pdfbase import pdfmetrics\n",
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"from reportlab.pdfbase.ttfonts import TTFont\n",
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"import docx\n",
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"from docx.enum.text import WD_ALIGN_PARAGRAPH\n",
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"\n",
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"# Define model options\n",
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"MODEL_OPTIONS = {\n",
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" \"Hoags\": \"prithivMLmods/Hoags-2B-Exp\",\n",
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"}\n",
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"\n",
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"# Preload models and processors into CUDA\n",
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"models = {}\n",
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"processors = {}\n",
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"for name, model_id in MODEL_OPTIONS.items():\n",
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" print(f\"Loading {name}...\")\n",
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" models[name] = Qwen2VLForConditionalGeneration.from_pretrained(\n",
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" model_id,\n",
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" trust_remote_code=True,\n",
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" torch_dtype=torch.float16\n",
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" ).to(\"cuda\").eval()\n",
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" processors[name] = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)\n",
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"\n",
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"image_extensions = Image.registered_extensions()\n",
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"\n",
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"def identify_and_save_blob(blob_path):\n",
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" \"\"\"Identifies if the blob is an image and saves it.\"\"\"\n",
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" try:\n",
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" with open(blob_path, 'rb') as file:\n",
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" blob_content = file.read()\n",
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" try:\n",
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" Image.open(io.BytesIO(blob_content)).verify() # Check if it's a valid image\n",
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" extension = \".png\" # Default to PNG for saving\n",
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" media_type = \"image\"\n",
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" except (IOError, SyntaxError):\n",
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" raise ValueError(\"Unsupported media type. Please upload a valid image.\")\n",
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"\n",
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" filename = f\"temp_{uuid.uuid4()}_media{extension}\"\n",
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" with open(filename, \"wb\") as f:\n",
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" f.write(blob_content)\n",
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"\n",
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" return filename, media_type\n",
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"\n",
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" except FileNotFoundError:\n",
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" raise ValueError(f\"The file {blob_path} was not found.\")\n",
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" except Exception as e:\n",
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" raise ValueError(f\"An error occurred while processing the file: {e}\")\n",
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"\n",
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"@spaces.GPU\n",
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"def qwen_inference(model_name, media_input, text_input=None):\n",
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" \"\"\"Handles inference for the selected model.\"\"\"\n",
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" model = models[model_name]\n",
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" processor = processors[model_name]\n",
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"\n",
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" if isinstance(media_input, str):\n",
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" media_path = media_input\n",
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" if media_path.endswith(tuple([i for i in image_extensions.keys()])):\n",
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" media_type = \"image\"\n",
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" else:\n",
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" try:\n",
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" media_path, media_type = identify_and_save_blob(media_input)\n",
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" except Exception as e:\n",
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" raise ValueError(\"Unsupported media type. Please upload a valid image.\")\n",
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"\n",
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" messages = [\n",
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" {\n",
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" \"role\": \"user\",\n",
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" \"content\": [\n",
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" {\n",
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" \"type\": media_type,\n",
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" media_type: media_path\n",
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" },\n",
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" {\"type\": \"text\", \"text\": text_input},\n",
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" ],\n",
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" }\n",
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" ]\n",
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"\n",
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" text = processor.apply_chat_template(\n",
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" messages, tokenize=False, add_generation_prompt=True\n",
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" )\n",
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" image_inputs, _ = process_vision_info(messages)\n",
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" inputs = processor(\n",
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" text=[text],\n",
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" images=image_inputs,\n",
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" padding=True,\n",
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" return_tensors=\"pt\",\n",
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" ).to(\"cuda\")\n",
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"\n",
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" streamer = TextIteratorStreamer(\n",
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" processor.tokenizer, skip_prompt=True, skip_special_tokens=True\n",
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" )\n",
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" generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024)\n",
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"\n",
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" thread = Thread(target=model.generate, kwargs=generation_kwargs)\n",
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" thread.start()\n",
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"\n",
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" buffer = \"\"\n",
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" for new_text in streamer:\n",
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" buffer += new_text\n",
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" # Remove <|im_end|> or similar tokens from the output\n",
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" buffer = buffer.replace(\"<|im_end|>\", \"\")\n",
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" yield buffer\n",
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"\n",
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"def format_plain_text(output_text):\n",
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" \"\"\"Formats the output text as plain text without LaTeX delimiters.\"\"\"\n",
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" # Remove LaTeX delimiters and convert to plain text\n",
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" plain_text = output_text.replace(\"\\\\(\", \"\").replace(\"\\\\)\", \"\").replace(\"\\\\[\", \"\").replace(\"\\\\]\", \"\")\n",
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" return plain_text\n",
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"\n",
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"def generate_document(media_path, output_text, file_format, font_size, line_spacing, alignment, image_size):\n",
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" \"\"\"Generates a document with the input image and plain text output.\"\"\"\n",
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" plain_text = format_plain_text(output_text)\n",
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" if file_format == \"pdf\":\n",
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" return generate_pdf(media_path, plain_text, font_size, line_spacing, alignment, image_size)\n",
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" elif file_format == \"docx\":\n",
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" return generate_docx(media_path, plain_text, font_size, line_spacing, alignment, image_size)\n",
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"\n",
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"def generate_pdf(media_path, plain_text, font_size, line_spacing, alignment, image_size):\n",
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" \"\"\"Generates a PDF document.\"\"\"\n",
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" filename = f\"output_{uuid.uuid4()}.pdf\"\n",
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" doc = SimpleDocTemplate(\n",
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" filename,\n",
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" pagesize=A4,\n",
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" rightMargin=inch,\n",
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" leftMargin=inch,\n",
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" topMargin=inch,\n",
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" bottomMargin=inch\n",
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" )\n",
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" styles = getSampleStyleSheet()\n",
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" styles[\"Normal\"].fontSize = int(font_size)\n",
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" styles[\"Normal\"].leading = int(font_size) * line_spacing\n",
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" styles[\"Normal\"].alignment = {\n",
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" \"Left\": 0,\n",
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" \"Center\": 1,\n",
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" \"Right\": 2,\n",
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" \"Justified\": 4\n",
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" }[alignment]\n",
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"\n",
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" story = []\n",
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"\n",
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" # Add image with size adjustment\n",
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" image_sizes = {\n",
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" \"Small\": (200, 200),\n",
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" \"Medium\": (400, 400),\n",
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" \"Large\": (600, 600)\n",
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" }\n",
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" img = RLImage(media_path, width=image_sizes[image_size][0], height=image_sizes[image_size][1])\n",
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" story.append(img)\n",
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" story.append(Spacer(1, 12))\n",
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"\n",
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" # Add plain text output\n",
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" text = Paragraph(plain_text, styles[\"Normal\"])\n",
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" story.append(text)\n",
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"\n",
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" doc.build(story)\n",
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" return filename\n",
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"\n",
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"def generate_docx(media_path, plain_text, font_size, line_spacing, alignment, image_size):\n",
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" \"\"\"Generates a DOCX document.\"\"\"\n",
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" filename = f\"output_{uuid.uuid4()}.docx\"\n",
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" doc = docx.Document()\n",
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"\n",
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" # Add image with size adjustment\n",
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" image_sizes = {\n",
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" \"Small\": docx.shared.Inches(2),\n",
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" \"Medium\": docx.shared.Inches(4),\n",
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" \"Large\": docx.shared.Inches(6)\n",
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" }\n",
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" doc.add_picture(media_path, width=image_sizes[image_size])\n",
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" doc.add_paragraph()\n",
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"\n",
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" # Add plain text output\n",
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" paragraph = doc.add_paragraph()\n",
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" paragraph.paragraph_format.line_spacing = line_spacing\n",
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" paragraph.paragraph_format.alignment = {\n",
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" \"Left\": WD_ALIGN_PARAGRAPH.LEFT,\n",
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" \"Center\": WD_ALIGN_PARAGRAPH.CENTER,\n",
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" \"Right\": WD_ALIGN_PARAGRAPH.RIGHT,\n",
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" \"Justified\": WD_ALIGN_PARAGRAPH.JUSTIFY\n",
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" }[alignment]\n",
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" run = paragraph.add_run(plain_text)\n",
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" run.font.size = docx.shared.Pt(int(font_size))\n",
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"\n",
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" doc.save(filename)\n",
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" return filename\n",
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"\n",
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"# CSS for output styling\n",
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"css = \"\"\"\n",
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" #output {\n",
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" height: 500px;\n",
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" overflow: auto;\n",
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" border: 1px solid #ccc;\n",
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" }\n",
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".submit-btn {\n",
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" background-color: #cf3434 !important;\n",
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" color: white !important;\n",
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"}\n",
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".submit-btn:hover {\n",
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" background-color: #ff2323 !important;\n",
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"}\n",
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".download-btn {\n",
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" background-color: #35a6d6 !important;\n",
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" color: white !important;\n",
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"}\n",
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".download-btn:hover {\n",
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" background-color: #22bcff !important;\n",
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"}\n",
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"\"\"\"\n",
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"\n",
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"# Gradio app setup\n",
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"with gr.Blocks(css=css) as demo:\n",
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" gr.Markdown(\"# Hoags-2B-Exp\")\n",
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"\n",
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" with gr.Tab(label=\"Image Input\"):\n",
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"\n",
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" with gr.Row():\n",
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" with gr.Column():\n",
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" model_choice = gr.Dropdown(\n",
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" label=\"Model Selection\",\n",
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" choices=list(MODEL_OPTIONS.keys()),\n",
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" value=\"Hoags\"\n",
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" )\n",
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" input_media = gr.File(\n",
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" label=\"Upload Image\", type=\"filepath\"\n",
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" )\n",
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" text_input = gr.Textbox(label=\"Question\", placeholder=\"Ask a question about the image...\")\n",
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" submit_btn = gr.Button(value=\"Submit\", elem_classes=\"submit-btn\")\n",
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"\n",
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" with gr.Column():\n",
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" output_text = gr.Textbox(label=\"Output Text\", lines=10)\n",
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" plain_text_output = gr.Textbox(label=\"Standardized Plain Text\", lines=10)\n",
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"\n",
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" submit_btn.click(\n",
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" qwen_inference, [model_choice, input_media, text_input], [output_text]\n",
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" ).then(\n",
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" lambda output_text: format_plain_text(output_text), [output_text], [plain_text_output]\n",
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" )\n",
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"\n",
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" # Add examples directly usable by clicking\n",
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" with gr.Row():\n",
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" with gr.Column():\n",
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" line_spacing = gr.Dropdown(\n",
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" choices=[0.5, 1.0, 1.15, 1.5, 2.0, 2.5, 3.0],\n",
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" value=1.5,\n",
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" label=\"Line Spacing\"\n",
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" )\n",
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" font_size = gr.Dropdown(\n",
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" choices=[\"8\", \"10\", \"12\", \"14\", \"16\", \"18\", \"20\", \"22\", \"24\"],\n",
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" value=\"18\",\n",
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" label=\"Font Size\"\n",
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" )\n",
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" alignment = gr.Dropdown(\n",
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" choices=[\"Left\", \"Center\", \"Right\", \"Justified\"],\n",
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" value=\"Justified\",\n",
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" label=\"Text Alignment\"\n",
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" )\n",
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" image_size = gr.Dropdown(\n",
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" choices=[\"Small\", \"Medium\", \"Large\"],\n",
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" value=\"Small\",\n",
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" label=\"Image Size\"\n",
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" )\n",
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" file_format = gr.Radio([\"pdf\", \"docx\"], label=\"File Format\", value=\"pdf\")\n",
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" get_document_btn = gr.Button(value=\"Get Document\", elem_classes=\"download-btn\")\n",
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"\n",
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" get_document_btn.click(\n",
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" generate_document, [input_media, output_text, file_format, font_size, line_spacing, alignment, image_size], gr.File(label=\"Download Document\")\n",
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" )\n",
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"\n",
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"demo.launch(debug=True)"
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]
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}
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|
],
|
||
|
|
"metadata": {
|
||
|
|
"accelerator": "GPU",
|
||
|
|
"colab": {
|
||
|
|
"gpuType": "T4",
|
||
|
|
"provenance": []
|
||
|
|
},
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"name": "python"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 0
|
||
|
|
}
|