{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "uFovmijgUV1Z" }, "source": [ "***Multimodal OCR ReportLab : Nanonets-3b***\n", "\n", "*notebook by : [prithivMLmods](https://huggingface.co/prithivMLmods)🤗 x ❤️*" ] }, { "cell_type": "markdown", "metadata": { "id": "RugX4SGZV-8O" }, "source": [ "***Installing all necessary packages***" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "l-NtFtjSpuJQ" }, "outputs": [], "source": [ "%%capture\n", "!pip install gradio transformers transformers-stream-generator qwen-vl-utils\n", "!pip install torchvision torch huggingface_hub spaces accelerate ipython\n", "!pip install pillow av python-docx requests numpy reportlab fpdf hf_xet\n", "#Hold tight, this will take around 3-5 minutes." ] }, { "cell_type": "markdown", "metadata": { "id": "mvoSnRZcVBu4" }, "source": [ "***Run app***" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, 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"source": [ "#Model used in the app: https://huggingface.co/nanonets/Nanonets-OCR-s\n", "#Architecture built on: Qwen2_5_VLForConditionalGeneration [qwen2_5_vl]\n", "import gradio as gr\n", "import spaces\n", "from transformers import Qwen2_5_VLForConditionalGeneration, 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", " \"Nanonets\": \"nanonets/Nanonets-OCR-s\",\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}🤗. Hold tight, this will take around 4-6 minutes..\")\n", " models[name] = Qwen2_5_VLForConditionalGeneration.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, theme=\"bethecloud/storj_theme\") as demo:\n", " gr.Markdown(\"# **Multimodal-OCR : Nanonets**\")\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=\"Nanonets\"\n", " )\n", " input_media = gr.File(\n", " label=\"Upload Image\", type=\"filepath\"\n", " )\n", " text_input = gr.Textbox(label=\"Question\", value=\"OCR the image precisely.\")\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=7)\n", "\n", " with gr.Accordion(\"Plain Text\", open=False):\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", " with gr.Accordion(\"Docx/PDF Settings\", open=False):\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=\"16\",\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=\"Medium\",\n", " label=\"Image Size\"\n", " )\n", " file_format = gr.Radio([\"pdf\", \"docx\"], label=\"File Format\", value=\"pdf\")\n", "\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": { 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