diff --git "a/nanonets-OCR/Multimodal_OCR_ReportLab_Nanonets_Cleared.ipynb" "b/nanonets-OCR/Multimodal_OCR_ReportLab_Nanonets_Cleared.ipynb" new file mode 100644--- /dev/null +++ "b/nanonets-OCR/Multimodal_OCR_ReportLab_Nanonets_Cleared.ipynb" @@ -0,0 +1,6374 @@ +{ + "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, + "referenced_widgets": [ + "00ca331f90124abbac54540843d4cfa1", + "1d8279597623483dabf8f70c9f4cfd8c", + "118486576f174fea9f05de472eb4e234", + "7dca3b00181e4aaba35c83d3c457a3ea", + "e2e8d1fe3155425082d61dce49d45cd8", + "58ffc803773146d7b3f91068cb808f06", + "4cf8655be9044f218ccbbb9fe7abf085", + "48394e8e14584e2da7d58ea7989a565d", + "00514e0d0748459fb6160d79b71015e5", + "02e0aa88e61b4576a1addea103d07621", + "832ca92fc97c4aac960642d7507af03c", + "86143a6dd5a94ff18e8d58f0c35e685c", + "60ec551c0a8c4c9eb82eba715c607c04", + "3778b3b0c2264d06a4ed68634b57fc3e", + "2d5bb7f785f44db99d3215930ab1ef3d", + 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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": { + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "00514e0d0748459fb6160d79b71015e5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": 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