├── README.md ├── Florence-2-Models-Image-Caption └── Florence2_Models.ipynb ├── LICENSE ├── Megalodon-OCR-Sync-0713-ColabNotebook └── Megalodon_OCR_Sync_0713_ReportLab.ipynb ├── Qwen2.5-VL-3B-Instruct └── Qwen2_5_VL_3B_Instruct_ReportLab.ipynb ├── Behemoth-3B-070225-post0.1 └── Behemoth_3B_070225_post0_1_ReportLab.ipynb └── Qwen2-VL-OCR-2B-Instruct └── Qwen2_VL_OCR_2B_Instruct.ipynb /README.md: -------------------------------------------------------------------------------- 1 | # **[OCR-ReportLab-Notebooks](https://huggingface.co/prithivMLmods/OCR-ReportLab-Notebooks/tree/main)** 2 | 3 | ![1.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/ksAGbgk8Gm4qstLB7bE4q.png) 4 | 5 | > [!note] 6 | **OCR-ReportLab** is a collection of Colab notebooks designed to perform Optical Character Recognition (OCR) on images and generate DOCX or PDF documents containing both the original image and the extracted text. It supports multiple state-of-the-art vision-language models for experimentation and practical use. 7 | 8 | ## Notebooks 9 | 10 | You can launch and run the following notebooks directly in Google Colab: 11 | 12 | - **Nanonets OCR:** [Open in Colab](https://colab.research.google.com/drive/1VvA-amvSVxGdWgIsh4_by6KWOtEs_Iqp) 13 | - **Monkey OCR:** [Open in Colab](https://colab.research.google.com/drive/1vPCojbmlXjDFUt06FJ1tjgnj_zWK4mUo) 14 | - **OCRFlux 3B:** [Open in Colab](https://colab.research.google.com/drive/1TDoCXzWdF2hxVLbISqW6DjXAzOyI7pzf) 15 | - **Typhoon OCR:** [Open in Colab](https://colab.research.google.com/drive/1_59zvLNnn1kvbiSFxzA1WiqhpbW8RKbz) 16 | 17 | ## Features 18 | 19 | - Extracts text from input images using various OCR models 20 | - Embeds the image and extracted text into DOCX or PDF formats 21 | - Designed for quick deployment via Google Colab 22 | 23 | ## Supported Models 24 | 25 | The repository currently supports the following OCR implementations: 26 | 27 | - **Nanonets OCR** 28 | - **Monkey OCR** 29 | - **OCRFlux 3B** 30 | - **Typhoon OCR 3B** 31 | 32 | ## Installation 33 | 34 | No installation is required. Simply click on the links above to run the notebooks in Google Colab. Make sure to upload your image file(s) when prompted and follow the instructions in the notebook. 35 | 36 | --- 37 | 38 | ## Other Images 39 | 40 | --- 41 | 42 | 43 | 44 | 48 | 52 | 53 |
45 | OCR 46 |

OCR

47 | 		49 | Caption 50 |

Caption

51 |
54 | 55 | --- 56 | 57 | ![222](https://github.com/user-attachments/assets/eae0f85d-2963-4edf-96e9-caedfe048c4f) 58 | 59 | --- 60 | 61 | ## Dependencies 62 | 63 | The notebooks are built using: 64 | 65 | - Python 66 | - PyTorch 67 | - Hugging Face Transformers 68 | - ReportLab 69 | - Gradio (for UI) 70 | - (Qwen2.5-VL based) 71 | 72 | All dependencies are automatically installed in the Colab environment. 73 | 74 | ## Author 75 | 76 | Created and maintained by [PRITHIVSAKTHIUR](https://github.com/PRITHIVSAKTHIUR) 77 | -------------------------------------------------------------------------------- /Florence-2-Models-Image-Caption/Florence2_Models.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "source": [ 6 | "***Florence-2 Models Image Captions : Image-to-Text***\n", 7 | "\n", 8 | "*notebook by : [prithivMLmods](https://huggingface.co/prithivMLmods)🤗 x ❤️*" 9 | ], 10 | "metadata": { 11 | "id": "0wlBVusvHBDY" 12 | } 13 | }, 14 | { 15 | "cell_type": "markdown", 16 | "source": [ 17 | "***Installing all necessary packages***" 18 | ], 19 | "metadata": { 20 | "id": "v_lhI9uSHcfX" 21 | } 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": null, 26 | "metadata": { 27 | "id": "d9NXmBEN4-5z" 28 | }, 29 | "outputs": [], 30 | "source": [ 31 | "%%capture\n", 32 | "!pip install transformers==4.48.0 timm\n", 33 | "!pip install huggingface_hub hf_xet\n", 34 | "#Hold tight, this will take around 3-5 minutes." 35 | ] 36 | }, 37 | { 38 | "cell_type": "markdown", 39 | "source": [ 40 | "***Run app 💨***" 41 | ], 42 | "metadata": { 43 | "id": "exk2jXyoHdwv" 44 | } 45 | }, 46 | { 47 | "cell_type": "code", 48 | "execution_count": null, 49 | "metadata": { 50 | "colab": { 51 | "background_save": true 52 | }, 53 | "id": "3Z7bnSM35Sfc" 54 | }, 55 | "outputs": [], 56 | "source": [ 57 | "import gradio as gr\n", 58 | "import subprocess\n", 59 | "import torch\n", 60 | "from PIL import Image\n", 61 | "from transformers import AutoProcessor, AutoModelForCausalLM\n", 62 | "\n", 63 | "#--------- Hold tight — installation takes only 2–3 minutes ---------#\n", 64 | "# Attempt to install flash-attn\n", 65 | "try:\n", 66 | " subprocess.run('pip install flash-attn==1.0.9 --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': \"TRUE\"}, check=True, shell=True)\n", 67 | "except subprocess.CalledProcessError as e:\n", 68 | " print(f\"Error installing flash-attn: {e}\")\n", 69 | " print(\"Continuing without flash-attn.\")\n", 70 | "#--------- Hold tight — installation takes only 2–3 minutes ---------#\n", 71 | "\n", 72 | "# Determine the device to use\n", 73 | "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", 74 | "\n", 75 | "# Load the base model and processor\n", 76 | "try:\n", 77 | " vision_language_model_base = AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True).to(device).eval()\n", 78 | " vision_language_processor_base = AutoProcessor.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True)\n", 79 | "except Exception as e:\n", 80 | " print(f\"Error loading base model: {e}\")\n", 81 | " vision_language_model_base = None\n", 82 | " vision_language_processor_base = None\n", 83 | "\n", 84 | "# Load the large model and processor\n", 85 | "try:\n", 86 | " vision_language_model_large = AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-large', trust_remote_code=True).to(device).eval()\n", 87 | " vision_language_processor_large = AutoProcessor.from_pretrained('microsoft/Florence-2-large', trust_remote_code=True)\n", 88 | "except Exception as e:\n", 89 | " print(f\"Error loading large model: {e}\")\n", 90 | " vision_language_model_large = None\n", 91 | " vision_language_processor_large = None\n", 92 | "\n", 93 | "def describe_image(uploaded_image, model_choice):\n", 94 | " \"\"\"\n", 95 | " Generates a detailed description of the input image using the selected model.\n", 96 | "\n", 97 | " Args:\n", 98 | " uploaded_image (PIL.Image.Image): The image to describe.\n", 99 | " model_choice (str): The model to use, either \"Base\" or \"Large\".\n", 100 | "\n", 101 | " Returns:\n", 102 | " str: A detailed textual description of the image or an error message.\n", 103 | " \"\"\"\n", 104 | " if uploaded_image is None:\n", 105 | " return \"Please upload an image.\"\n", 106 | "\n", 107 | " if model_choice == \"Florence-2-base\":\n", 108 | " if vision_language_model_base is None:\n", 109 | " return \"Base model failed to load.\"\n", 110 | " model = vision_language_model_base\n", 111 | " processor = vision_language_processor_base\n", 112 | " elif model_choice == \"Florence-2-large\":\n", 113 | " if vision_language_model_large is None:\n", 114 | " return \"Large model failed to load.\"\n", 115 | " model = vision_language_model_large\n", 116 | " processor = vision_language_processor_large\n", 117 | " else:\n", 118 | " return \"Invalid model choice.\"\n", 119 | "\n", 120 | " if not isinstance(uploaded_image, Image.Image):\n", 121 | " uploaded_image = Image.fromarray(uploaded_image)\n", 122 | "\n", 123 | " inputs = processor(text=\"\", images=uploaded_image, return_tensors=\"pt\").to(device)\n", 124 | " with torch.no_grad():\n", 125 | " generated_ids = model.generate(\n", 126 | " input_ids=inputs[\"input_ids\"],\n", 127 | " pixel_values=inputs[\"pixel_values\"],\n", 128 | " max_new_tokens=1024,\n", 129 | " early_stopping=False,\n", 130 | " do_sample=False,\n", 131 | " num_beams=3,\n", 132 | " )\n", 133 | " generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]\n", 134 | " processed_description = processor.post_process_generation(\n", 135 | " generated_text,\n", 136 | " task=\"\",\n", 137 | " image_size=(uploaded_image.width, uploaded_image.height)\n", 138 | " )\n", 139 | " image_description = processed_description[\"\"]\n", 140 | " print(\"\\nImage description generated!:\", image_description)\n", 141 | " return image_description\n", 142 | "\n", 143 | "# Description for the interface\n", 144 | "description = \"> Select the model to use for generating the image description. 'Base' is smaller and faster, while 'Large' is more accurate but slower.\"\n", 145 | "if device == \"cpu\":\n", 146 | " description += \" Note: Running on CPU, which may be slow for large models.\"\n", 147 | "\n", 148 | "css = \"\"\"\n", 149 | ".submit-btn {\n", 150 | " background-color: #4682B4 !important;\n", 151 | " color: white !important;\n", 152 | "}\n", 153 | ".submit-btn:hover {\n", 154 | " background-color: #87CEEB !important;\n", 155 | "}\n", 156 | "\"\"\"\n", 157 | "\n", 158 | "# Create the Gradio interface with Blocks\n", 159 | "with gr.Blocks(theme=\"bethecloud/storj_theme\", css=css) as demo:\n", 160 | " gr.Markdown(\"# **[Florence-2 Models Image Captions](https://huggingface.co/collections/prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0)**\")\n", 161 | " gr.Markdown(description)\n", 162 | " with gr.Row():\n", 163 | " # Left column: Input image and Generate button\n", 164 | " with gr.Column():\n", 165 | " image_input = gr.Image(label=\"Upload Image\", type=\"pil\")\n", 166 | " generate_btn = gr.Button(\"Generate Caption\", elem_classes=\"submit-btn\")\n", 167 | " # Right column: Model choice, output, and examples\n", 168 | " with gr.Column():\n", 169 | " model_choice = gr.Radio([\"Florence-2-base\", \"Florence-2-large\"], label=\"Model Choice\", value=\"Florence-2-base\")\n", 170 | " with gr.Row():\n", 171 | " output = gr.Textbox(label=\"Generated Caption\", lines=4, show_copy_button=True)\n", 172 | " # Connect the button to the function\n", 173 | " generate_btn.click(fn=describe_image, inputs=[image_input, model_choice], outputs=output)\n", 174 | "\n", 175 | "# Launch the interface\n", 176 | "demo.launch(debug=True)" 177 | ] 178 | }, 179 | { 180 | "cell_type": "markdown", 181 | "source": [ 182 | "## **Demo Inference Screenshots**\n", 183 | "\n", 184 | "| ![Screenshot 2025-07-27 at 23-42-00 Gradio.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/Sg9YAC9iRXaYQKL7T1MRQ.png) | ![Screenshot 2025-07-27 at 23-37-02 Gradio.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/DSoE-iXiO4haktd65aQxr.png) |\n", 185 | "|:---------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------:|\n" 186 | ], 187 | "metadata": { 188 | "id": "NFKGwtueGfcW" 189 | } 190 | } 191 | ], 192 | "metadata": { 193 | "accelerator": "GPU", 194 | "colab": { 195 | "gpuType": "T4", 196 | "provenance": [] 197 | }, 198 | "kernelspec": { 199 | "display_name": "Python 3", 200 | "name": "python3" 201 | }, 202 | "language_info": { 203 | "name": "python" 204 | } 205 | }, 206 | "nbformat": 4, 207 | "nbformat_minor": 0 208 | } -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /Megalodon-OCR-Sync-0713-ColabNotebook/Megalodon_OCR_Sync_0713_ReportLab.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "id": "uFovmijgUV1Z" 7 | }, 8 | "source": [ 9 | "***Multimodal OCR ReportLab : Megalodon-OCR-Sync-0713***\n", 10 | "\n", 11 | "*notebook by : [prithivMLmods](https://huggingface.co/prithivMLmods)🤗 x ❤️*" 12 | ] 13 | }, 14 | { 15 | "cell_type": "markdown", 16 | "metadata": { 17 | "id": "RugX4SGZV-8O" 18 | }, 19 | "source": [ 20 | "***Installing all necessary packages***" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": null, 26 | "metadata": { 27 | "id": "l-NtFtjSpuJQ" 28 | }, 29 | "outputs": [], 30 | "source": [ 31 | "%%capture\n", 32 | "!pip install gradio transformers transformers-stream-generator qwen-vl-utils\n", 33 | "!pip install torchvision torch huggingface_hub spaces accelerate ipython\n", 34 | "!pip install pillow av python-docx requests numpy reportlab fpdf hf_xet\n", 35 | "#Hold tight, this will take around 3-5 minutes." 36 | ] 37 | }, 38 | { 39 | "cell_type": "markdown", 40 | "metadata": { 41 | "id": "mvoSnRZcVBu4" 42 | }, 43 | "source": [ 44 | "***Run app***" 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": null, 50 | "metadata": { 51 | "id": "tElKr2Fkp1bO" 52 | }, 53 | "outputs": [], 54 | "source": [ 55 | "#Model used in the app: https://huggingface.co/prithivMLmods/Megalodon-OCR-Sync-0713\n", 56 | "#Architecture built on: Qwen2_5_VLForConditionalGeneration [qwen2_5_vl]\n", 57 | "import gradio as gr\n", 58 | "import spaces\n", 59 | "from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor, TextIteratorStreamer\n", 60 | "from qwen_vl_utils import process_vision_info\n", 61 | "import torch\n", 62 | "from PIL import Image\n", 63 | "import os\n", 64 | "import uuid\n", 65 | "import io\n", 66 | "from threading import Thread\n", 67 | "from reportlab.lib.pagesizes import A4\n", 68 | "from reportlab.lib.styles import getSampleStyleSheet\n", 69 | "from reportlab.lib import colors\n", 70 | "from reportlab.platypus import SimpleDocTemplate, Image as RLImage, Paragraph, Spacer\n", 71 | "from reportlab.lib.units import inch\n", 72 | "from reportlab.pdfbase import pdfmetrics\n", 73 | "from reportlab.pdfbase.ttfonts import TTFont\n", 74 | "import docx\n", 75 | "from docx.enum.text import WD_ALIGN_PARAGRAPH\n", 76 | "\n", 77 | "# Define model options\n", 78 | "MODEL_OPTIONS = {\n", 79 | " \"Megalodon-OCR-Sync-0713\": \"prithivMLmods/Megalodon-OCR-Sync-0713\",\n", 80 | "}\n", 81 | "\n", 82 | "# Preload models and processors into CUDA\n", 83 | "models = {}\n", 84 | "processors = {}\n", 85 | "for name, model_id in MODEL_OPTIONS.items():\n", 86 | " print(f\"Loading {name}🤗. Hold tight, this will take around 4-6 minutes..\")\n", 87 | " models[name] = Qwen2_5_VLForConditionalGeneration.from_pretrained(\n", 88 | " model_id,\n", 89 | " trust_remote_code=True,\n", 90 | " torch_dtype=torch.float16\n", 91 | " ).to(\"cuda\").eval()\n", 92 | " processors[name] = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)\n", 93 | "\n", 94 | "image_extensions = Image.registered_extensions()\n", 95 | "\n", 96 | "def identify_and_save_blob(blob_path):\n", 97 | " \"\"\"Identifies if the blob is an image and saves it.\"\"\"\n", 98 | " try:\n", 99 | " with open(blob_path, 'rb') as file:\n", 100 | " blob_content = file.read()\n", 101 | " try:\n", 102 | " Image.open(io.BytesIO(blob_content)).verify() # Check if it's a valid image\n", 103 | " extension = \".png\" # Default to PNG for saving\n", 104 | " media_type = \"image\"\n", 105 | " except (IOError, SyntaxError):\n", 106 | " raise ValueError(\"Unsupported media type. Please upload a valid image.\")\n", 107 | "\n", 108 | " filename = f\"temp_{uuid.uuid4()}_media{extension}\"\n", 109 | " with open(filename, \"wb\") as f:\n", 110 | " f.write(blob_content)\n", 111 | "\n", 112 | " return filename, media_type\n", 113 | "\n", 114 | " except FileNotFoundError:\n", 115 | " raise ValueError(f\"The file {blob_path} was not found.\")\n", 116 | " except Exception as e:\n", 117 | " raise ValueError(f\"An error occurred while processing the file: {e}\")\n", 118 | "\n", 119 | "@spaces.GPU\n", 120 | "def qwen_inference(model_name, media_input, text_input=None):\n", 121 | " \"\"\"Handles inference for the selected model.\"\"\"\n", 122 | " model = models[model_name]\n", 123 | " processor = processors[model_name]\n", 124 | "\n", 125 | " if isinstance(media_input, str):\n", 126 | " media_path = media_input\n", 127 | " if media_path.endswith(tuple([i for i in image_extensions.keys()])):\n", 128 | " media_type = \"image\"\n", 129 | " else:\n", 130 | " try:\n", 131 | " media_path, media_type = identify_and_save_blob(media_input)\n", 132 | " except Exception as e:\n", 133 | " raise ValueError(\"Unsupported media type. Please upload a valid image.\")\n", 134 | "\n", 135 | " messages = [\n", 136 | " {\n", 137 | " \"role\": \"user\",\n", 138 | " \"content\": [\n", 139 | " {\n", 140 | " \"type\": media_type,\n", 141 | " media_type: media_path\n", 142 | " },\n", 143 | " {\"type\": \"text\", \"text\": text_input},\n", 144 | " ],\n", 145 | " }\n", 146 | " ]\n", 147 | "\n", 148 | " text = processor.apply_chat_template(\n", 149 | " messages, tokenize=False, add_generation_prompt=True\n", 150 | " )\n", 151 | " image_inputs, _ = process_vision_info(messages)\n", 152 | " inputs = processor(\n", 153 | " text=[text],\n", 154 | " images=image_inputs,\n", 155 | " padding=True,\n", 156 | " return_tensors=\"pt\",\n", 157 | " ).to(\"cuda\")\n", 158 | "\n", 159 | " streamer = TextIteratorStreamer(\n", 160 | " processor.tokenizer, skip_prompt=True, skip_special_tokens=True\n", 161 | " )\n", 162 | " generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024)\n", 163 | "\n", 164 | " thread = Thread(target=model.generate, kwargs=generation_kwargs)\n", 165 | " thread.start()\n", 166 | "\n", 167 | " buffer = \"\"\n", 168 | " for new_text in streamer:\n", 169 | " buffer += new_text\n", 170 | " # Remove <|im_end|> or similar tokens from the output\n", 171 | " buffer = buffer.replace(\"<|im_end|>\", \"\")\n", 172 | " yield buffer\n", 173 | "\n", 174 | "def format_plain_text(output_text):\n", 175 | " \"\"\"Formats the output text as plain text without LaTeX delimiters.\"\"\"\n", 176 | " # Remove LaTeX delimiters and convert to plain text\n", 177 | " plain_text = output_text.replace(\"\\\\(\", \"\").replace(\"\\\\)\", \"\").replace(\"\\\\[\", \"\").replace(\"\\\\]\", \"\")\n", 178 | " return plain_text\n", 179 | "\n", 180 | "def generate_document(media_path, output_text, file_format, font_size, line_spacing, alignment, image_size):\n", 181 | " \"\"\"Generates a document with the input image and plain text output.\"\"\"\n", 182 | " plain_text = format_plain_text(output_text)\n", 183 | " if file_format == \"pdf\":\n", 184 | " return generate_pdf(media_path, plain_text, font_size, line_spacing, alignment, image_size)\n", 185 | " elif file_format == \"docx\":\n", 186 | " return generate_docx(media_path, plain_text, font_size, line_spacing, alignment, image_size)\n", 187 | "\n", 188 | "def generate_pdf(media_path, plain_text, font_size, line_spacing, alignment, image_size):\n", 189 | " \"\"\"Generates a PDF document.\"\"\"\n", 190 | " filename = f\"output_{uuid.uuid4()}.pdf\"\n", 191 | " doc = SimpleDocTemplate(\n", 192 | " filename,\n", 193 | " pagesize=A4,\n", 194 | " rightMargin=inch,\n", 195 | " leftMargin=inch,\n", 196 | " topMargin=inch,\n", 197 | " bottomMargin=inch\n", 198 | " )\n", 199 | " styles = getSampleStyleSheet()\n", 200 | " styles[\"Normal\"].fontSize = int(font_size)\n", 201 | " styles[\"Normal\"].leading = int(font_size) * line_spacing\n", 202 | " styles[\"Normal\"].alignment = {\n", 203 | " \"Left\": 0,\n", 204 | " \"Center\": 1,\n", 205 | " \"Right\": 2,\n", 206 | " \"Justified\": 4\n", 207 | " }[alignment]\n", 208 | "\n", 209 | " story = []\n", 210 | "\n", 211 | " # Add image with size adjustment\n", 212 | " image_sizes = {\n", 213 | " \"Small\": (200, 200),\n", 214 | " \"Medium\": (400, 400),\n", 215 | " \"Large\": (600, 600)\n", 216 | " }\n", 217 | " img = RLImage(media_path, width=image_sizes[image_size][0], height=image_sizes[image_size][1])\n", 218 | " story.append(img)\n", 219 | " story.append(Spacer(1, 12))\n", 220 | "\n", 221 | " # Add plain text output\n", 222 | " text = Paragraph(plain_text, styles[\"Normal\"])\n", 223 | " story.append(text)\n", 224 | "\n", 225 | " doc.build(story)\n", 226 | " return filename\n", 227 | "\n", 228 | "def generate_docx(media_path, plain_text, font_size, line_spacing, alignment, image_size):\n", 229 | " \"\"\"Generates a DOCX document.\"\"\"\n", 230 | " filename = f\"output_{uuid.uuid4()}.docx\"\n", 231 | " doc = docx.Document()\n", 232 | "\n", 233 | " # Add image with size adjustment\n", 234 | " image_sizes = {\n", 235 | " \"Small\": docx.shared.Inches(2),\n", 236 | " \"Medium\": docx.shared.Inches(4),\n", 237 | " \"Large\": docx.shared.Inches(6)\n", 238 | " }\n", 239 | " doc.add_picture(media_path, width=image_sizes[image_size])\n", 240 | " doc.add_paragraph()\n", 241 | "\n", 242 | " # Add plain text output\n", 243 | " paragraph = doc.add_paragraph()\n", 244 | " paragraph.paragraph_format.line_spacing = line_spacing\n", 245 | " paragraph.paragraph_format.alignment = {\n", 246 | " \"Left\": WD_ALIGN_PARAGRAPH.LEFT,\n", 247 | " \"Center\": WD_ALIGN_PARAGRAPH.CENTER,\n", 248 | " \"Right\": WD_ALIGN_PARAGRAPH.RIGHT,\n", 249 | " \"Justified\": WD_ALIGN_PARAGRAPH.JUSTIFY\n", 250 | " }[alignment]\n", 251 | " run = paragraph.add_run(plain_text)\n", 252 | " run.font.size = docx.shared.Pt(int(font_size))\n", 253 | "\n", 254 | " doc.save(filename)\n", 255 | " return filename\n", 256 | "\n", 257 | "# CSS for output styling\n", 258 | "css = \"\"\"\n", 259 | " #output {\n", 260 | " height: 500px;\n", 261 | " overflow: auto;\n", 262 | " border: 1px solid #ccc;\n", 263 | " }\n", 264 | ".submit-btn {\n", 265 | " background-color: #cf3434 !important;\n", 266 | " color: white !important;\n", 267 | "}\n", 268 | ".submit-btn:hover {\n", 269 | " background-color: #ff2323 !important;\n", 270 | "}\n", 271 | ".download-btn {\n", 272 | " background-color: #35a6d6 !important;\n", 273 | " color: white !important;\n", 274 | "}\n", 275 | ".download-btn:hover {\n", 276 | " background-color: #22bcff !important;\n", 277 | "}\n", 278 | "\"\"\"\n", 279 | "\n", 280 | "# Gradio app setup\n", 281 | "with gr.Blocks(css=css, theme=\"bethecloud/storj_theme\") as demo:\n", 282 | " gr.Markdown(\"# **Multimodal-OCR : Megalodon-OCR-Sync-0713**\")\n", 283 | "\n", 284 | " with gr.Tab(label=\"Image Input\"):\n", 285 | "\n", 286 | " with gr.Row():\n", 287 | " with gr.Column():\n", 288 | " model_choice = gr.Dropdown(\n", 289 | " label=\"Model Selection\",\n", 290 | " choices=list(MODEL_OPTIONS.keys()),\n", 291 | " value=\"Megalodon-OCR-Sync-0713\"\n", 292 | " )\n", 293 | " input_media = gr.File(\n", 294 | " label=\"Upload Image\", type=\"filepath\"\n", 295 | " )\n", 296 | " text_input = gr.Textbox(label=\"Question\", value=\"OCR the image precisely.\")\n", 297 | " submit_btn = gr.Button(value=\"Submit\", elem_classes=\"submit-btn\")\n", 298 | "\n", 299 | " with gr.Column():\n", 300 | " output_text = gr.Textbox(label=\"Output Text\", lines=7)\n", 301 | "\n", 302 | " with gr.Accordion(\"Plain Text\", open=False):\n", 303 | " plain_text_output = gr.Textbox(label=\"Standardized Plain Text\", lines=10)\n", 304 | "\n", 305 | " submit_btn.click(\n", 306 | " qwen_inference, [model_choice, input_media, text_input], [output_text]\n", 307 | " ).then(\n", 308 | " lambda output_text: format_plain_text(output_text), [output_text], [plain_text_output]\n", 309 | " )\n", 310 | "\n", 311 | " with gr.Accordion(\"Docx/PDF Settings\", open=False):\n", 312 | " with gr.Row():\n", 313 | " with gr.Column():\n", 314 | " line_spacing = gr.Dropdown(\n", 315 | " choices=[0.5, 1.0, 1.15, 1.5, 2.0, 2.5, 3.0],\n", 316 | " value=1.5,\n", 317 | " label=\"Line Spacing\"\n", 318 | " )\n", 319 | " font_size = gr.Dropdown(\n", 320 | " choices=[\"8\", \"10\", \"12\", \"14\", \"16\", \"18\", \"20\", \"22\", \"24\"],\n", 321 | " value=\"16\",\n", 322 | " label=\"Font Size\"\n", 323 | " )\n", 324 | " alignment = gr.Dropdown(\n", 325 | " choices=[\"Left\", \"Center\", \"Right\", \"Justified\"],\n", 326 | " value=\"Justified\",\n", 327 | " label=\"Text Alignment\"\n", 328 | " )\n", 329 | " image_size = gr.Dropdown(\n", 330 | " choices=[\"Small\", \"Medium\", \"Large\"],\n", 331 | " value=\"Medium\",\n", 332 | " label=\"Image Size\"\n", 333 | " )\n", 334 | " file_format = gr.Radio([\"pdf\", \"docx\"], label=\"File Format\", value=\"pdf\")\n", 335 | "\n", 336 | " get_document_btn = gr.Button(value=\"Get Document\", elem_classes=\"download-btn\")\n", 337 | "\n", 338 | " get_document_btn.click(\n", 339 | " generate_document, [input_media, output_text, file_format, font_size, line_spacing, alignment, image_size], gr.File(label=\"Download Document\")\n", 340 | " )\n", 341 | "\n", 342 | "demo.launch(debug=True)" 343 | ] 344 | } 345 | ], 346 | "metadata": { 347 | "accelerator": "GPU", 348 | "colab": { 349 | "gpuType": "T4", 350 | "provenance": [] 351 | }, 352 | "kernelspec": { 353 | "display_name": "Python 3", 354 | "name": "python3" 355 | }, 356 | "language_info": { 357 | "name": "python" 358 | } 359 | }, 360 | "nbformat": 4, 361 | "nbformat_minor": 0 362 | } 363 | -------------------------------------------------------------------------------- /Qwen2.5-VL-3B-Instruct/Qwen2_5_VL_3B_Instruct_ReportLab.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "id": "uFovmijgUV1Z" 7 | }, 8 | "source": [ 9 | "***Multimodal ReportLab : Qwen2.5-VL-3B-Instruct***\n", 10 | "\n", 11 | "*notebook by : [prithivMLmods](https://huggingface.co/prithivMLmods)🤗 x ❤️*" 12 | ] 13 | }, 14 | { 15 | "cell_type": "markdown", 16 | "metadata": { 17 | "id": "RugX4SGZV-8O" 18 | }, 19 | "source": [ 20 | "***Installing all necessary packages***" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": null, 26 | "metadata": { 27 | "id": "l-NtFtjSpuJQ" 28 | }, 29 | "outputs": [], 30 | "source": [ 31 | "%%capture\n", 32 | "!pip install gradio transformers transformers-stream-generator qwen-vl-utils\n", 33 | "!pip install torchvision torch huggingface_hub spaces accelerate ipython\n", 34 | "!pip install pillow av python-docx requests numpy reportlab fpdf hf_xet\n", 35 | "#Hold tight, this will take around 3-5 minutes." 36 | ] 37 | }, 38 | { 39 | "cell_type": "markdown", 40 | "metadata": { 41 | "id": "mvoSnRZcVBu4" 42 | }, 43 | "source": [ 44 | "***Run app***" 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": null, 50 | "metadata": { 51 | "id": "tElKr2Fkp1bO" 52 | }, 53 | "outputs": [], 54 | "source": [ 55 | "# ================================================\n", 56 | "# Model Configuration\n", 57 | "# ================================================\n", 58 | "\n", 59 | "# Model used in the app:\n", 60 | "# https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct\n", 61 | "\n", 62 | "# Architecture built on:\n", 63 | "# Qwen2_5_VLForConditionalGeneration [qwen2_5_vl]\n", 64 | "import gradio as gr\n", 65 | "import spaces\n", 66 | "from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor, TextIteratorStreamer\n", 67 | "from qwen_vl_utils import process_vision_info\n", 68 | "import torch\n", 69 | "from PIL import Image\n", 70 | "import os\n", 71 | "import uuid\n", 72 | "import io\n", 73 | "from threading import Thread\n", 74 | "from reportlab.lib.pagesizes import A4\n", 75 | "from reportlab.lib.styles import getSampleStyleSheet\n", 76 | "from reportlab.lib import colors\n", 77 | "from reportlab.platypus import SimpleDocTemplate, Image as RLImage, Paragraph, Spacer\n", 78 | "from reportlab.lib.units import inch\n", 79 | "from reportlab.pdfbase import pdfmetrics\n", 80 | "from reportlab.pdfbase.ttfonts import TTFont\n", 81 | "import docx\n", 82 | "from docx.enum.text import WD_ALIGN_PARAGRAPH\n", 83 | "\n", 84 | "# Define model options\n", 85 | "MODEL_OPTIONS = {\n", 86 | " \"Qwen2.5-VL-3B-Instruct\": \"Qwen/Qwen2.5-VL-3B-Instruct\",\n", 87 | "}\n", 88 | "\n", 89 | "# Preload models and processors into CUDA\n", 90 | "models = {}\n", 91 | "processors = {}\n", 92 | "for name, model_id in MODEL_OPTIONS.items():\n", 93 | " print(f\"Loading {name}🤗. Hold tight, this will take around 4-6 minutes..\")\n", 94 | " models[name] = Qwen2_5_VLForConditionalGeneration.from_pretrained(\n", 95 | " model_id,\n", 96 | " trust_remote_code=True,\n", 97 | " torch_dtype=torch.float16\n", 98 | " ).to(\"cuda\").eval()\n", 99 | " processors[name] = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)\n", 100 | "\n", 101 | "image_extensions = Image.registered_extensions()\n", 102 | "\n", 103 | "def identify_and_save_blob(blob_path):\n", 104 | " \"\"\"Identifies if the blob is an image and saves it.\"\"\"\n", 105 | " try:\n", 106 | " with open(blob_path, 'rb') as file:\n", 107 | " blob_content = file.read()\n", 108 | " try:\n", 109 | " Image.open(io.BytesIO(blob_content)).verify() # Check if it's a valid image\n", 110 | " extension = \".png\" # Default to PNG for saving\n", 111 | " media_type = \"image\"\n", 112 | " except (IOError, SyntaxError):\n", 113 | " raise ValueError(\"Unsupported media type. Please upload a valid image.\")\n", 114 | "\n", 115 | " filename = f\"temp_{uuid.uuid4()}_media{extension}\"\n", 116 | " with open(filename, \"wb\") as f:\n", 117 | " f.write(blob_content)\n", 118 | "\n", 119 | " return filename, media_type\n", 120 | "\n", 121 | " except FileNotFoundError:\n", 122 | " raise ValueError(f\"The file {blob_path} was not found.\")\n", 123 | " except Exception as e:\n", 124 | " raise ValueError(f\"An error occurred while processing the file: {e}\")\n", 125 | "\n", 126 | "@spaces.GPU\n", 127 | "def qwen_inference(model_name, media_input, text_input=None):\n", 128 | " \"\"\"Handles inference for the selected model.\"\"\"\n", 129 | " model = models[model_name]\n", 130 | " processor = processors[model_name]\n", 131 | "\n", 132 | " if isinstance(media_input, str):\n", 133 | " media_path = media_input\n", 134 | " if media_path.endswith(tuple([i for i in image_extensions.keys()])):\n", 135 | " media_type = \"image\"\n", 136 | " else:\n", 137 | " try:\n", 138 | " media_path, media_type = identify_and_save_blob(media_input)\n", 139 | " except Exception as e:\n", 140 | " raise ValueError(\"Unsupported media type. Please upload a valid image.\")\n", 141 | "\n", 142 | " messages = [\n", 143 | " {\n", 144 | " \"role\": \"user\",\n", 145 | " \"content\": [\n", 146 | " {\n", 147 | " \"type\": media_type,\n", 148 | " media_type: media_path\n", 149 | " },\n", 150 | " {\"type\": \"text\", \"text\": text_input},\n", 151 | " ],\n", 152 | " }\n", 153 | " ]\n", 154 | "\n", 155 | " text = processor.apply_chat_template(\n", 156 | " messages, tokenize=False, add_generation_prompt=True\n", 157 | " )\n", 158 | " image_inputs, _ = process_vision_info(messages)\n", 159 | " inputs = processor(\n", 160 | " text=[text],\n", 161 | " images=image_inputs,\n", 162 | " padding=True,\n", 163 | " return_tensors=\"pt\",\n", 164 | " ).to(\"cuda\")\n", 165 | "\n", 166 | " streamer = TextIteratorStreamer(\n", 167 | " processor.tokenizer, skip_prompt=True, skip_special_tokens=True\n", 168 | " )\n", 169 | " generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024)\n", 170 | "\n", 171 | " thread = Thread(target=model.generate, kwargs=generation_kwargs)\n", 172 | " thread.start()\n", 173 | "\n", 174 | " buffer = \"\"\n", 175 | " for new_text in streamer:\n", 176 | " buffer += new_text\n", 177 | " # Remove <|im_end|> or similar tokens from the output\n", 178 | " buffer = buffer.replace(\"<|im_end|>\", \"\")\n", 179 | " yield buffer\n", 180 | "\n", 181 | "def format_plain_text(output_text):\n", 182 | " \"\"\"Formats the output text as plain text without LaTeX delimiters.\"\"\"\n", 183 | " # Remove LaTeX delimiters and convert to plain text\n", 184 | " plain_text = output_text.replace(\"\\\\(\", \"\").replace(\"\\\\)\", \"\").replace(\"\\\\[\", \"\").replace(\"\\\\]\", \"\")\n", 185 | " return plain_text\n", 186 | "\n", 187 | "def generate_document(media_path, output_text, file_format, font_size, line_spacing, alignment, image_size):\n", 188 | " \"\"\"Generates a document with the input image and plain text output.\"\"\"\n", 189 | " plain_text = format_plain_text(output_text)\n", 190 | " if file_format == \"pdf\":\n", 191 | " return generate_pdf(media_path, plain_text, font_size, line_spacing, alignment, image_size)\n", 192 | " elif file_format == \"docx\":\n", 193 | " return generate_docx(media_path, plain_text, font_size, line_spacing, alignment, image_size)\n", 194 | "\n", 195 | "def generate_pdf(media_path, plain_text, font_size, line_spacing, alignment, image_size):\n", 196 | " \"\"\"Generates a PDF document.\"\"\"\n", 197 | " filename = f\"output_{uuid.uuid4()}.pdf\"\n", 198 | " doc = SimpleDocTemplate(\n", 199 | " filename,\n", 200 | " pagesize=A4,\n", 201 | " rightMargin=inch,\n", 202 | " leftMargin=inch,\n", 203 | " topMargin=inch,\n", 204 | " bottomMargin=inch\n", 205 | " )\n", 206 | " styles = getSampleStyleSheet()\n", 207 | " styles[\"Normal\"].fontSize = int(font_size)\n", 208 | " styles[\"Normal\"].leading = int(font_size) * line_spacing\n", 209 | " styles[\"Normal\"].alignment = {\n", 210 | " \"Left\": 0,\n", 211 | " \"Center\": 1,\n", 212 | " \"Right\": 2,\n", 213 | " \"Justified\": 4\n", 214 | " }[alignment]\n", 215 | "\n", 216 | " story = []\n", 217 | "\n", 218 | " # Add image with size adjustment\n", 219 | " image_sizes = {\n", 220 | " \"Small\": (200, 200),\n", 221 | " \"Medium\": (400, 400),\n", 222 | " \"Large\": (600, 600)\n", 223 | " }\n", 224 | " img = RLImage(media_path, width=image_sizes[image_size][0], height=image_sizes[image_size][1])\n", 225 | " story.append(img)\n", 226 | " story.append(Spacer(1, 12))\n", 227 | "\n", 228 | " # Add plain text output\n", 229 | " text = Paragraph(plain_text, styles[\"Normal\"])\n", 230 | " story.append(text)\n", 231 | "\n", 232 | " doc.build(story)\n", 233 | " return filename\n", 234 | "\n", 235 | "def generate_docx(media_path, plain_text, font_size, line_spacing, alignment, image_size):\n", 236 | " \"\"\"Generates a DOCX document.\"\"\"\n", 237 | " filename = f\"output_{uuid.uuid4()}.docx\"\n", 238 | " doc = docx.Document()\n", 239 | "\n", 240 | " # Add image with size adjustment\n", 241 | " image_sizes = {\n", 242 | " \"Small\": docx.shared.Inches(2),\n", 243 | " \"Medium\": docx.shared.Inches(4),\n", 244 | " \"Large\": docx.shared.Inches(6)\n", 245 | " }\n", 246 | " doc.add_picture(media_path, width=image_sizes[image_size])\n", 247 | " doc.add_paragraph()\n", 248 | "\n", 249 | " # Add plain text output\n", 250 | " paragraph = doc.add_paragraph()\n", 251 | " paragraph.paragraph_format.line_spacing = line_spacing\n", 252 | " paragraph.paragraph_format.alignment = {\n", 253 | " \"Left\": WD_ALIGN_PARAGRAPH.LEFT,\n", 254 | " \"Center\": WD_ALIGN_PARAGRAPH.CENTER,\n", 255 | " \"Right\": WD_ALIGN_PARAGRAPH.RIGHT,\n", 256 | " \"Justified\": WD_ALIGN_PARAGRAPH.JUSTIFY\n", 257 | " }[alignment]\n", 258 | " run = paragraph.add_run(plain_text)\n", 259 | " run.font.size = docx.shared.Pt(int(font_size))\n", 260 | "\n", 261 | " doc.save(filename)\n", 262 | " return filename\n", 263 | "\n", 264 | "# CSS for output styling\n", 265 | "css = \"\"\"\n", 266 | " #output {\n", 267 | " height: 500px;\n", 268 | " overflow: auto;\n", 269 | " border: 1px solid #ccc;\n", 270 | " }\n", 271 | ".submit-btn {\n", 272 | " background-color: #cf3434 !important;\n", 273 | " color: white !important;\n", 274 | "}\n", 275 | ".submit-btn:hover {\n", 276 | " background-color: #ff2323 !important;\n", 277 | "}\n", 278 | ".download-btn {\n", 279 | " background-color: #35a6d6 !important;\n", 280 | " color: white !important;\n", 281 | "}\n", 282 | ".download-btn:hover {\n", 283 | " background-color: #22bcff !important;\n", 284 | "}\n", 285 | "\"\"\"\n", 286 | "\n", 287 | "# Gradio app setup\n", 288 | "with gr.Blocks(css=css, theme=\"bethecloud/storj_theme\") as demo:\n", 289 | " gr.Markdown(\"# **MultimodalVLM : Qwen2.5-VL-3B-Instruct**\")\n", 290 | "\n", 291 | " with gr.Tab(label=\"Image Input\"):\n", 292 | "\n", 293 | " with gr.Row():\n", 294 | " with gr.Column():\n", 295 | " model_choice = gr.Dropdown(\n", 296 | " label=\"Model Selection\",\n", 297 | " choices=list(MODEL_OPTIONS.keys()),\n", 298 | " value=\"Qwen2.5-VL-3B-Instruct\"\n", 299 | " )\n", 300 | " input_media = gr.File(\n", 301 | " label=\"Upload Image\", type=\"filepath\"\n", 302 | " )\n", 303 | " text_input = gr.Textbox(label=\"Question\", value=\"Explain the content precisely.\")\n", 304 | " submit_btn = gr.Button(value=\"Submit\", elem_classes=\"submit-btn\")\n", 305 | "\n", 306 | " with gr.Column():\n", 307 | " output_text = gr.Textbox(label=\"Output Text\", lines=7)\n", 308 | "\n", 309 | " with gr.Accordion(\"Plain Text\", open=False):\n", 310 | " plain_text_output = gr.Textbox(label=\"Standardized Plain Text\", lines=10)\n", 311 | "\n", 312 | " submit_btn.click(\n", 313 | " qwen_inference, [model_choice, input_media, text_input], [output_text]\n", 314 | " ).then(\n", 315 | " lambda output_text: format_plain_text(output_text), [output_text], [plain_text_output]\n", 316 | " )\n", 317 | "\n", 318 | " with gr.Accordion(\"Docx/PDF Settings\", open=False):\n", 319 | " with gr.Row():\n", 320 | " with gr.Column():\n", 321 | " line_spacing = gr.Dropdown(\n", 322 | " choices=[0.5, 1.0, 1.15, 1.5, 2.0, 2.5, 3.0],\n", 323 | " value=1.5,\n", 324 | " label=\"Line Spacing\"\n", 325 | " )\n", 326 | " font_size = gr.Dropdown(\n", 327 | " choices=[\"8\", \"10\", \"12\", \"14\", \"16\", \"18\", \"20\", \"22\", \"24\"],\n", 328 | " value=\"16\",\n", 329 | " label=\"Font Size\"\n", 330 | " )\n", 331 | " alignment = gr.Dropdown(\n", 332 | " choices=[\"Left\", \"Center\", \"Right\", \"Justified\"],\n", 333 | " value=\"Justified\",\n", 334 | " label=\"Text Alignment\"\n", 335 | " )\n", 336 | " image_size = gr.Dropdown(\n", 337 | " choices=[\"Small\", \"Medium\", \"Large\"],\n", 338 | " value=\"Medium\",\n", 339 | " label=\"Image Size\"\n", 340 | " )\n", 341 | " file_format = gr.Radio([\"pdf\", \"docx\"], label=\"File Format\", value=\"pdf\")\n", 342 | "\n", 343 | " get_document_btn = gr.Button(value=\"Get Document\", elem_classes=\"download-btn\")\n", 344 | "\n", 345 | " get_document_btn.click(\n", 346 | " generate_document, [input_media, output_text, file_format, font_size, line_spacing, alignment, image_size], gr.File(label=\"Download Document\")\n", 347 | " )\n", 348 | "\n", 349 | "demo.launch(debug=True)" 350 | ] 351 | } 352 | ], 353 | "metadata": { 354 | "accelerator": "GPU", 355 | "colab": { 356 | "gpuType": "T4", 357 | "provenance": [] 358 | }, 359 | "kernelspec": { 360 | "display_name": "Python 3", 361 | "name": "python3" 362 | }, 363 | "language_info": { 364 | "name": "python" 365 | } 366 | }, 367 | "nbformat": 4, 368 | "nbformat_minor": 0 369 | } -------------------------------------------------------------------------------- /Behemoth-3B-070225-post0.1/Behemoth_3B_070225_post0_1_ReportLab.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "id": "uFovmijgUV1Z" 7 | }, 8 | "source": [ 9 | "***Multimodal Caption ReportLab : Behemoth-3B-070225-post0.1***\n", 10 | "\n", 11 | "*notebook by : [prithivMLmods](https://huggingface.co/prithivMLmods)🤗 x ❤️*" 12 | ] 13 | }, 14 | { 15 | "cell_type": "markdown", 16 | "metadata": { 17 | "id": "RugX4SGZV-8O" 18 | }, 19 | "source": [ 20 | "***Installing all necessary packages***" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": null, 26 | "metadata": { 27 | "id": "l-NtFtjSpuJQ" 28 | }, 29 | "outputs": [], 30 | "source": [ 31 | "%%capture\n", 32 | "!pip install gradio transformers transformers-stream-generator qwen-vl-utils\n", 33 | "!pip install torchvision torch huggingface_hub spaces accelerate ipython\n", 34 | "!pip install pillow av python-docx requests numpy reportlab fpdf hf_xet\n", 35 | "#Hold tight, this will take around 3-5 minutes." 36 | ] 37 | }, 38 | { 39 | "cell_type": "markdown", 40 | "metadata": { 41 | "id": "mvoSnRZcVBu4" 42 | }, 43 | "source": [ 44 | "***Run app***" 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": null, 50 | "metadata": { 51 | "id": "tElKr2Fkp1bO" 52 | }, 53 | "outputs": [], 54 | "source": [ 55 | "# ================================================\n", 56 | "# Model Configuration\n", 57 | "# ================================================\n", 58 | "\n", 59 | "# Model used in the app:\n", 60 | "# https://huggingface.co/prithivMLmods/Behemoth-3B-070225-post0.1\n", 61 | "\n", 62 | "# Architecture built on:\n", 63 | "# Qwen2_5_VLForConditionalGeneration [qwen2_5_vl]\n", 64 | "import gradio as gr\n", 65 | "import spaces\n", 66 | "from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor, TextIteratorStreamer\n", 67 | "from qwen_vl_utils import process_vision_info\n", 68 | "import torch\n", 69 | "from PIL import Image\n", 70 | "import os\n", 71 | "import uuid\n", 72 | "import io\n", 73 | "from threading import Thread\n", 74 | "from reportlab.lib.pagesizes import A4\n", 75 | "from reportlab.lib.styles import getSampleStyleSheet\n", 76 | "from reportlab.lib import colors\n", 77 | "from reportlab.platypus import SimpleDocTemplate, Image as RLImage, Paragraph, Spacer\n", 78 | "from reportlab.lib.units import inch\n", 79 | "from reportlab.pdfbase import pdfmetrics\n", 80 | "from reportlab.pdfbase.ttfonts import TTFont\n", 81 | "import docx\n", 82 | "from docx.enum.text import WD_ALIGN_PARAGRAPH\n", 83 | "\n", 84 | "# Define model options\n", 85 | "MODEL_OPTIONS = {\n", 86 | " \"Behemoth-3B-070225-post0.1\": \"prithivMLmods/Behemoth-3B-070225-post0.1\",\n", 87 | "}\n", 88 | "\n", 89 | "# Preload models and processors into CUDA\n", 90 | "models = {}\n", 91 | "processors = {}\n", 92 | "for name, model_id in MODEL_OPTIONS.items():\n", 93 | " print(f\"Loading {name}🤗. Hold tight, this will take around 4-6 minutes..\")\n", 94 | " models[name] = Qwen2_5_VLForConditionalGeneration.from_pretrained(\n", 95 | " model_id,\n", 96 | " trust_remote_code=True,\n", 97 | " torch_dtype=torch.float16\n", 98 | " ).to(\"cuda\").eval()\n", 99 | " processors[name] = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)\n", 100 | "\n", 101 | "image_extensions = Image.registered_extensions()\n", 102 | "\n", 103 | "def identify_and_save_blob(blob_path):\n", 104 | " \"\"\"Identifies if the blob is an image and saves it.\"\"\"\n", 105 | " try:\n", 106 | " with open(blob_path, 'rb') as file:\n", 107 | " blob_content = file.read()\n", 108 | " try:\n", 109 | " Image.open(io.BytesIO(blob_content)).verify() # Check if it's a valid image\n", 110 | " extension = \".png\" # Default to PNG for saving\n", 111 | " media_type = \"image\"\n", 112 | " except (IOError, SyntaxError):\n", 113 | " raise ValueError(\"Unsupported media type. Please upload a valid image.\")\n", 114 | "\n", 115 | " filename = f\"temp_{uuid.uuid4()}_media{extension}\"\n", 116 | " with open(filename, \"wb\") as f:\n", 117 | " f.write(blob_content)\n", 118 | "\n", 119 | " return filename, media_type\n", 120 | "\n", 121 | " except FileNotFoundError:\n", 122 | " raise ValueError(f\"The file {blob_path} was not found.\")\n", 123 | " except Exception as e:\n", 124 | " raise ValueError(f\"An error occurred while processing the file: {e}\")\n", 125 | "\n", 126 | "@spaces.GPU\n", 127 | "def qwen_inference(model_name, media_input, text_input=None):\n", 128 | " \"\"\"Handles inference for the selected model.\"\"\"\n", 129 | " model = models[model_name]\n", 130 | " processor = processors[model_name]\n", 131 | "\n", 132 | " if isinstance(media_input, str):\n", 133 | " media_path = media_input\n", 134 | " if media_path.endswith(tuple([i for i in image_extensions.keys()])):\n", 135 | " media_type = \"image\"\n", 136 | " else:\n", 137 | " try:\n", 138 | " media_path, media_type = identify_and_save_blob(media_input)\n", 139 | " except Exception as e:\n", 140 | " raise ValueError(\"Unsupported media type. Please upload a valid image.\")\n", 141 | "\n", 142 | " messages = [\n", 143 | " {\n", 144 | " \"role\": \"user\",\n", 145 | " \"content\": [\n", 146 | " {\n", 147 | " \"type\": media_type,\n", 148 | " media_type: media_path\n", 149 | " },\n", 150 | " {\"type\": \"text\", \"text\": text_input},\n", 151 | " ],\n", 152 | " }\n", 153 | " ]\n", 154 | "\n", 155 | " text = processor.apply_chat_template(\n", 156 | " messages, tokenize=False, add_generation_prompt=True\n", 157 | " )\n", 158 | " image_inputs, _ = process_vision_info(messages)\n", 159 | " inputs = processor(\n", 160 | " text=[text],\n", 161 | " images=image_inputs,\n", 162 | " padding=True,\n", 163 | " return_tensors=\"pt\",\n", 164 | " ).to(\"cuda\")\n", 165 | "\n", 166 | " streamer = TextIteratorStreamer(\n", 167 | " processor.tokenizer, skip_prompt=True, skip_special_tokens=True\n", 168 | " )\n", 169 | " generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024)\n", 170 | "\n", 171 | " thread = Thread(target=model.generate, kwargs=generation_kwargs)\n", 172 | " thread.start()\n", 173 | "\n", 174 | " buffer = \"\"\n", 175 | " for new_text in streamer:\n", 176 | " buffer += new_text\n", 177 | " # Remove <|im_end|> or similar tokens from the output\n", 178 | " buffer = buffer.replace(\"<|im_end|>\", \"\")\n", 179 | " yield buffer\n", 180 | "\n", 181 | "def format_plain_text(output_text):\n", 182 | " \"\"\"Formats the output text as plain text without LaTeX delimiters.\"\"\"\n", 183 | " # Remove LaTeX delimiters and convert to plain text\n", 184 | " plain_text = output_text.replace(\"\\\\(\", \"\").replace(\"\\\\)\", \"\").replace(\"\\\\[\", \"\").replace(\"\\\\]\", \"\")\n", 185 | " return plain_text\n", 186 | "\n", 187 | "def generate_document(media_path, output_text, file_format, font_size, line_spacing, alignment, image_size):\n", 188 | " \"\"\"Generates a document with the input image and plain text output.\"\"\"\n", 189 | " plain_text = format_plain_text(output_text)\n", 190 | " if file_format == \"pdf\":\n", 191 | " return generate_pdf(media_path, plain_text, font_size, line_spacing, alignment, image_size)\n", 192 | " elif file_format == \"docx\":\n", 193 | " return generate_docx(media_path, plain_text, font_size, line_spacing, alignment, image_size)\n", 194 | "\n", 195 | "def generate_pdf(media_path, plain_text, font_size, line_spacing, alignment, image_size):\n", 196 | " \"\"\"Generates a PDF document.\"\"\"\n", 197 | " filename = f\"output_{uuid.uuid4()}.pdf\"\n", 198 | " doc = SimpleDocTemplate(\n", 199 | " filename,\n", 200 | " pagesize=A4,\n", 201 | " rightMargin=inch,\n", 202 | " leftMargin=inch,\n", 203 | " topMargin=inch,\n", 204 | " bottomMargin=inch\n", 205 | " )\n", 206 | " styles = getSampleStyleSheet()\n", 207 | " styles[\"Normal\"].fontSize = int(font_size)\n", 208 | " styles[\"Normal\"].leading = int(font_size) * line_spacing\n", 209 | " styles[\"Normal\"].alignment = {\n", 210 | " \"Left\": 0,\n", 211 | " \"Center\": 1,\n", 212 | " \"Right\": 2,\n", 213 | " \"Justified\": 4\n", 214 | " }[alignment]\n", 215 | "\n", 216 | " story = []\n", 217 | "\n", 218 | " # Add image with size adjustment\n", 219 | " image_sizes = {\n", 220 | " \"Small\": (200, 200),\n", 221 | " \"Medium\": (400, 400),\n", 222 | " \"Large\": (600, 600)\n", 223 | " }\n", 224 | " img = RLImage(media_path, width=image_sizes[image_size][0], height=image_sizes[image_size][1])\n", 225 | " story.append(img)\n", 226 | " story.append(Spacer(1, 12))\n", 227 | "\n", 228 | " # Add plain text output\n", 229 | " text = Paragraph(plain_text, styles[\"Normal\"])\n", 230 | " story.append(text)\n", 231 | "\n", 232 | " doc.build(story)\n", 233 | " return filename\n", 234 | "\n", 235 | "def generate_docx(media_path, plain_text, font_size, line_spacing, alignment, image_size):\n", 236 | " \"\"\"Generates a DOCX document.\"\"\"\n", 237 | " filename = f\"output_{uuid.uuid4()}.docx\"\n", 238 | " doc = docx.Document()\n", 239 | "\n", 240 | " # Add image with size adjustment\n", 241 | " image_sizes = {\n", 242 | " \"Small\": docx.shared.Inches(2),\n", 243 | " \"Medium\": docx.shared.Inches(4),\n", 244 | " \"Large\": docx.shared.Inches(6)\n", 245 | " }\n", 246 | " doc.add_picture(media_path, width=image_sizes[image_size])\n", 247 | " doc.add_paragraph()\n", 248 | "\n", 249 | " # Add plain text output\n", 250 | " paragraph = doc.add_paragraph()\n", 251 | " paragraph.paragraph_format.line_spacing = line_spacing\n", 252 | " paragraph.paragraph_format.alignment = {\n", 253 | " \"Left\": WD_ALIGN_PARAGRAPH.LEFT,\n", 254 | " \"Center\": WD_ALIGN_PARAGRAPH.CENTER,\n", 255 | " \"Right\": WD_ALIGN_PARAGRAPH.RIGHT,\n", 256 | " \"Justified\": WD_ALIGN_PARAGRAPH.JUSTIFY\n", 257 | " }[alignment]\n", 258 | " run = paragraph.add_run(plain_text)\n", 259 | " run.font.size = docx.shared.Pt(int(font_size))\n", 260 | "\n", 261 | " doc.save(filename)\n", 262 | " return filename\n", 263 | "\n", 264 | "# CSS for output styling\n", 265 | "css = \"\"\"\n", 266 | " #output {\n", 267 | " height: 500px;\n", 268 | " overflow: auto;\n", 269 | " border: 1px solid #ccc;\n", 270 | " }\n", 271 | ".submit-btn {\n", 272 | " background-color: #cf3434 !important;\n", 273 | " color: white !important;\n", 274 | "}\n", 275 | ".submit-btn:hover {\n", 276 | " background-color: #ff2323 !important;\n", 277 | "}\n", 278 | ".download-btn {\n", 279 | " background-color: #35a6d6 !important;\n", 280 | " color: white !important;\n", 281 | "}\n", 282 | ".download-btn:hover {\n", 283 | " background-color: #22bcff !important;\n", 284 | "}\n", 285 | "\"\"\"\n", 286 | "\n", 287 | "# Gradio app setup\n", 288 | "with gr.Blocks(css=css, theme=\"bethecloud/storj_theme\") as demo:\n", 289 | " gr.Markdown(\"# **Multimodal-Caption : Behemoth-3B-070225-post0.1**\")\n", 290 | "\n", 291 | " with gr.Tab(label=\"Image Input\"):\n", 292 | "\n", 293 | " with gr.Row():\n", 294 | " with gr.Column():\n", 295 | " model_choice = gr.Dropdown(\n", 296 | " label=\"Model Selection\",\n", 297 | " choices=list(MODEL_OPTIONS.keys()),\n", 298 | " value=\"Behemoth-3B-070225-post0.1\"\n", 299 | " )\n", 300 | " input_media = gr.File(\n", 301 | " label=\"Upload Image\", type=\"filepath\"\n", 302 | " )\n", 303 | " text_input = gr.Textbox(label=\"Question\", value=\"OCR the image precisely.\")\n", 304 | " submit_btn = gr.Button(value=\"Submit\", elem_classes=\"submit-btn\")\n", 305 | "\n", 306 | " with gr.Column():\n", 307 | " output_text = gr.Textbox(label=\"Output Text\", lines=7)\n", 308 | "\n", 309 | " with gr.Accordion(\"Plain Text\", open=False):\n", 310 | " plain_text_output = gr.Textbox(label=\"Standardized Plain Text\", lines=10)\n", 311 | "\n", 312 | " submit_btn.click(\n", 313 | " qwen_inference, [model_choice, input_media, text_input], [output_text]\n", 314 | " ).then(\n", 315 | " lambda output_text: format_plain_text(output_text), [output_text], [plain_text_output]\n", 316 | " )\n", 317 | "\n", 318 | " with gr.Accordion(\"Docx/PDF Settings\", open=False):\n", 319 | " with gr.Row():\n", 320 | " with gr.Column():\n", 321 | " line_spacing = gr.Dropdown(\n", 322 | " choices=[0.5, 1.0, 1.15, 1.5, 2.0, 2.5, 3.0],\n", 323 | " value=1.5,\n", 324 | " label=\"Line Spacing\"\n", 325 | " )\n", 326 | " font_size = gr.Dropdown(\n", 327 | " choices=[\"8\", \"10\", \"12\", \"14\", \"16\", \"18\", \"20\", \"22\", \"24\"],\n", 328 | " value=\"16\",\n", 329 | " label=\"Font Size\"\n", 330 | " )\n", 331 | " alignment = gr.Dropdown(\n", 332 | " choices=[\"Left\", \"Center\", \"Right\", \"Justified\"],\n", 333 | " value=\"Justified\",\n", 334 | " label=\"Text Alignment\"\n", 335 | " )\n", 336 | " image_size = gr.Dropdown(\n", 337 | " choices=[\"Small\", \"Medium\", \"Large\"],\n", 338 | " value=\"Medium\",\n", 339 | " label=\"Image Size\"\n", 340 | " )\n", 341 | " file_format = gr.Radio([\"pdf\", \"docx\"], label=\"File Format\", value=\"pdf\")\n", 342 | "\n", 343 | " get_document_btn = gr.Button(value=\"Get Document\", elem_classes=\"download-btn\")\n", 344 | "\n", 345 | " get_document_btn.click(\n", 346 | " generate_document, [input_media, output_text, file_format, font_size, line_spacing, alignment, image_size], gr.File(label=\"Download Document\")\n", 347 | " )\n", 348 | "\n", 349 | "demo.launch(debug=True)" 350 | ] 351 | }, 352 | { 353 | "cell_type": "markdown", 354 | "source": [ 355 | "## **Demo Inference**\n", 356 | "\n", 357 | "\n", 358 | "| Preview |\n", 359 | "|:--:|\n", 360 | "| ![Image 1](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/mOHuzqK2dUYAX03WZEkL7.jpeg) |\n", 361 | "| *Movie Still / Poster* |\n", 362 | "| ![Image 2](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/BA9p11MXXii5nJJbSR-Sg.png) |\n", 363 | "| *Gradio UI Screenshot* |\n" 364 | ], 365 | "metadata": { 366 | "id": "ynyjulRaDl3m" 367 | } 368 | } 369 | ], 370 | "metadata": { 371 | "accelerator": "GPU", 372 | "colab": { 373 | "gpuType": "T4", 374 | "provenance": [] 375 | }, 376 | "kernelspec": { 377 | "display_name": "Python 3", 378 | "name": "python3" 379 | }, 380 | "language_info": { 381 | "name": "python" 382 | } 383 | }, 384 | "nbformat": 4, 385 | "nbformat_minor": 0 386 | } -------------------------------------------------------------------------------- /Qwen2-VL-OCR-2B-Instruct/Qwen2_VL_OCR_2B_Instruct.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "id": "uFovmijgUV1Z" 7 | }, 8 | "source": [ 9 | "***Multimodal OCR ReportLab : [Qwen2-VL-OCR-2B-Instruct](https://huggingface.co/prithivMLmods/Qwen2-VL-OCR-2B-Instruct)***\n", 10 | "\n", 11 | "*notebook by : [prithivMLmods](https://huggingface.co/prithivMLmods)🤗*\n", 12 | "\n", 13 | " The Qwen2-VL-OCR-2B-Instruct model is a fine-tuned version of Qwen/Qwen2-VL-2B-Instruct, tailored for tasks that involve Optical Character Recognition (OCR), image-to-text conversion, and math problem solving with LaTeX formatting. This model integrates a conversational approach with visual and textual understanding to handle multi-modal tasks effectively." 14 | ] 15 | }, 16 | { 17 | "cell_type": "markdown", 18 | "metadata": { 19 | "id": "RugX4SGZV-8O" 20 | }, 21 | "source": [ 22 | "***Installing all necessary packages***" 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": null, 28 | "metadata": { 29 | "id": "l-NtFtjSpuJQ" 30 | }, 31 | "outputs": [], 32 | "source": [ 33 | "%%capture\n", 34 | "!pip install gradio transformers transformers-stream-generator qwen-vl-utils\n", 35 | "!pip install torchvision torch huggingface_hub spaces accelerate ipython\n", 36 | "!pip install pillow av python-docx requests numpy reportlab fpdf hf_xet\n", 37 | "#Hold tight, this will take around 3-5 minutes." 38 | ] 39 | }, 40 | { 41 | "cell_type": "markdown", 42 | "metadata": { 43 | "id": "mvoSnRZcVBu4" 44 | }, 45 | "source": [ 46 | "***Run app***" 47 | ] 48 | }, 49 | { 50 | "cell_type": "code", 51 | 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"import uuid\n", 173 | "import io\n", 174 | "from threading import Thread\n", 175 | "from reportlab.lib.pagesizes import A4\n", 176 | "from reportlab.lib.styles import getSampleStyleSheet\n", 177 | "from reportlab.lib import colors\n", 178 | "from reportlab.platypus import SimpleDocTemplate, Image as RLImage, Paragraph, Spacer\n", 179 | "from reportlab.lib.units import inch\n", 180 | "from reportlab.pdfbase import pdfmetrics\n", 181 | "from reportlab.pdfbase.ttfonts import TTFont\n", 182 | "import docx\n", 183 | "from docx.enum.text import WD_ALIGN_PARAGRAPH\n", 184 | "\n", 185 | "# Define model options\n", 186 | "MODEL_OPTIONS = {\n", 187 | " \"Qwen2-VL-OCR-2B-Instruct\": \"prithivMLmods/Qwen2-VL-OCR-2B-Instruct\",\n", 188 | "}\n", 189 | "\n", 190 | "# Preload models and processors into CUDA\n", 191 | "models = {}\n", 192 | "processors = {}\n", 193 | "for name, model_id in MODEL_OPTIONS.items():\n", 194 | " print(f\"Loading {name}🤗. Hold tight, this will take around 4-6 minutes..\")\n", 195 | " models[name] = Qwen2VLForConditionalGeneration.from_pretrained(\n", 196 | " model_id,\n", 197 | " trust_remote_code=True,\n", 198 | " torch_dtype=torch.float16\n", 199 | " ).to(\"cuda\").eval()\n", 200 | " processors[name] = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)\n", 201 | "\n", 202 | "image_extensions = Image.registered_extensions()\n", 203 | "\n", 204 | "def identify_and_save_blob(blob_path):\n", 205 | " \"\"\"Identifies if the blob is an image and saves it.\"\"\"\n", 206 | " try:\n", 207 | " with open(blob_path, 'rb') as file:\n", 208 | " blob_content = file.read()\n", 209 | " try:\n", 210 | " Image.open(io.BytesIO(blob_content)).verify() # Check if it's a valid image\n", 211 | " extension = \".png\" # Default to PNG for saving\n", 212 | " media_type = \"image\"\n", 213 | " except (IOError, SyntaxError):\n", 214 | " raise ValueError(\"Unsupported media type. Please upload a valid image.\")\n", 215 | "\n", 216 | " filename = f\"temp_{uuid.uuid4()}_media{extension}\"\n", 217 | " with open(filename, \"wb\") as f:\n", 218 | " f.write(blob_content)\n", 219 | "\n", 220 | " return filename, media_type\n", 221 | "\n", 222 | " except FileNotFoundError:\n", 223 | " raise ValueError(f\"The file {blob_path} was not found.\")\n", 224 | " except Exception as e:\n", 225 | " raise ValueError(f\"An error occurred while processing the file: {e}\")\n", 226 | "\n", 227 | "@spaces.GPU\n", 228 | "def qwen_inference(model_name, media_input, text_input=None):\n", 229 | " \"\"\"Handles inference for the selected model.\"\"\"\n", 230 | " model = models[model_name]\n", 231 | " processor = processors[model_name]\n", 232 | "\n", 233 | " if isinstance(media_input, str):\n", 234 | " media_path = media_input\n", 235 | " if media_path.endswith(tuple([i for i in image_extensions.keys()])):\n", 236 | " media_type = \"image\"\n", 237 | " else:\n", 238 | " try:\n", 239 | " media_path, media_type = identify_and_save_blob(media_input)\n", 240 | " except Exception as e:\n", 241 | " raise ValueError(\"Unsupported media type. Please upload a valid image.\")\n", 242 | "\n", 243 | " messages = [\n", 244 | " {\n", 245 | " \"role\": \"user\",\n", 246 | " \"content\": [\n", 247 | " {\n", 248 | " \"type\": media_type,\n", 249 | " media_type: media_path\n", 250 | " },\n", 251 | " {\"type\": \"text\", \"text\": text_input},\n", 252 | " ],\n", 253 | " }\n", 254 | " ]\n", 255 | "\n", 256 | " text = processor.apply_chat_template(\n", 257 | " messages, tokenize=False, add_generation_prompt=True\n", 258 | " )\n", 259 | " image_inputs, _ = process_vision_info(messages)\n", 260 | " inputs = processor(\n", 261 | " text=[text],\n", 262 | " images=image_inputs,\n", 263 | " padding=True,\n", 264 | " return_tensors=\"pt\",\n", 265 | " ).to(\"cuda\")\n", 266 | "\n", 267 | " streamer = TextIteratorStreamer(\n", 268 | " processor.tokenizer, skip_prompt=True, skip_special_tokens=True\n", 269 | " )\n", 270 | " generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024)\n", 271 | "\n", 272 | " thread = Thread(target=model.generate, kwargs=generation_kwargs)\n", 273 | " thread.start()\n", 274 | "\n", 275 | " buffer = \"\"\n", 276 | " for new_text in streamer:\n", 277 | " buffer += new_text\n", 278 | " # Remove <|im_end|> or similar tokens from the output\n", 279 | " buffer = buffer.replace(\"<|im_end|>\", \"\")\n", 280 | " yield buffer\n", 281 | "\n", 282 | "def format_plain_text(output_text):\n", 283 | " \"\"\"Formats the output text as plain text without LaTeX delimiters.\"\"\"\n", 284 | " # Remove LaTeX delimiters and convert to plain text\n", 285 | " plain_text = output_text.replace(\"\\\\(\", \"\").replace(\"\\\\)\", \"\").replace(\"\\\\[\", \"\").replace(\"\\\\]\", \"\")\n", 286 | " return plain_text\n", 287 | "\n", 288 | "def generate_document(media_path, output_text, file_format, font_size, line_spacing, alignment, image_size):\n", 289 | " \"\"\"Generates a document with the input image and plain text output.\"\"\"\n", 290 | " plain_text = format_plain_text(output_text)\n", 291 | " if file_format == \"pdf\":\n", 292 | " return generate_pdf(media_path, plain_text, font_size, line_spacing, alignment, image_size)\n", 293 | " elif file_format == \"docx\":\n", 294 | " return generate_docx(media_path, plain_text, font_size, line_spacing, alignment, image_size)\n", 295 | "\n", 296 | "def generate_pdf(media_path, plain_text, font_size, line_spacing, alignment, image_size):\n", 297 | " \"\"\"Generates a PDF document.\"\"\"\n", 298 | " filename = f\"output_{uuid.uuid4()}.pdf\"\n", 299 | " doc = SimpleDocTemplate(\n", 300 | " filename,\n", 301 | " pagesize=A4,\n", 302 | " rightMargin=inch,\n", 303 | " leftMargin=inch,\n", 304 | " topMargin=inch,\n", 305 | " bottomMargin=inch\n", 306 | " )\n", 307 | " styles = getSampleStyleSheet()\n", 308 | " styles[\"Normal\"].fontSize = int(font_size)\n", 309 | " styles[\"Normal\"].leading = int(font_size) * line_spacing\n", 310 | " styles[\"Normal\"].alignment = {\n", 311 | " \"Left\": 0,\n", 312 | " \"Center\": 1,\n", 313 | " \"Right\": 2,\n", 314 | " \"Justified\": 4\n", 315 | " }[alignment]\n", 316 | "\n", 317 | " story = []\n", 318 | "\n", 319 | " # Add image with size adjustment\n", 320 | " image_sizes = {\n", 321 | " \"Small\": (200, 200),\n", 322 | " \"Medium\": (400, 400),\n", 323 | " \"Large\": (600, 600)\n", 324 | " }\n", 325 | " img = RLImage(media_path, width=image_sizes[image_size][0], height=image_sizes[image_size][1])\n", 326 | " story.append(img)\n", 327 | " story.append(Spacer(1, 12))\n", 328 | "\n", 329 | " # Add plain text output\n", 330 | " text = Paragraph(plain_text, styles[\"Normal\"])\n", 331 | " story.append(text)\n", 332 | "\n", 333 | " doc.build(story)\n", 334 | " return filename\n", 335 | "\n", 336 | "def generate_docx(media_path, plain_text, font_size, line_spacing, alignment, image_size):\n", 337 | " \"\"\"Generates a DOCX document.\"\"\"\n", 338 | " filename = f\"output_{uuid.uuid4()}.docx\"\n", 339 | " doc = docx.Document()\n", 340 | "\n", 341 | " # Add image with size adjustment\n", 342 | " image_sizes = {\n", 343 | " \"Small\": docx.shared.Inches(2),\n", 344 | " \"Medium\": docx.shared.Inches(4),\n", 345 | " \"Large\": docx.shared.Inches(6)\n", 346 | " }\n", 347 | " doc.add_picture(media_path, width=image_sizes[image_size])\n", 348 | " doc.add_paragraph()\n", 349 | "\n", 350 | " # Add plain text output\n", 351 | " paragraph = doc.add_paragraph()\n", 352 | " paragraph.paragraph_format.line_spacing = line_spacing\n", 353 | " paragraph.paragraph_format.alignment = {\n", 354 | " \"Left\": WD_ALIGN_PARAGRAPH.LEFT,\n", 355 | " \"Center\": WD_ALIGN_PARAGRAPH.CENTER,\n", 356 | " \"Right\": WD_ALIGN_PARAGRAPH.RIGHT,\n", 357 | " \"Justified\": WD_ALIGN_PARAGRAPH.JUSTIFY\n", 358 | " }[alignment]\n", 359 | " run = paragraph.add_run(plain_text)\n", 360 | " run.font.size = docx.shared.Pt(int(font_size))\n", 361 | "\n", 362 | " doc.save(filename)\n", 363 | " return filename\n", 364 | "\n", 365 | "# CSS for output styling\n", 366 | "css = \"\"\"\n", 367 | " #output {\n", 368 | " height: 500px;\n", 369 | " overflow: auto;\n", 370 | " border: 1px solid #ccc;\n", 371 | " }\n", 372 | ".submit-btn {\n", 373 | " background-color: #cf3434 !important;\n", 374 | " color: white !important;\n", 375 | "}\n", 376 | ".submit-btn:hover {\n", 377 | " background-color: #ff2323 !important;\n", 378 | "}\n", 379 | ".download-btn {\n", 380 | " background-color: #35a6d6 !important;\n", 381 | " color: white !important;\n", 382 | "}\n", 383 | ".download-btn:hover {\n", 384 | " background-color: #22bcff !important;\n", 385 | "}\n", 386 | "\"\"\"\n", 387 | "\n", 388 | "# Gradio app setup\n", 389 | "with gr.Blocks(css=css, theme=\"bethecloud/storj_theme\") as demo:\n", 390 | " gr.Markdown(\"# **Multimodal-OCR : Qwen2-VL-OCR-2B-Instruct**\")\n", 391 | "\n", 392 | " with gr.Tab(label=\"Image Input\"):\n", 393 | "\n", 394 | " with gr.Row():\n", 395 | " with gr.Column():\n", 396 | " model_choice = gr.Dropdown(\n", 397 | " label=\"Model Selection\",\n", 398 | " choices=list(MODEL_OPTIONS.keys()),\n", 399 | " value=\"Qwen2-VL-OCR-2B-Instruct\"\n", 400 | " )\n", 401 | " input_media = gr.File(\n", 402 | " label=\"Upload Image\", type=\"filepath\"\n", 403 | " )\n", 404 | " text_input = gr.Textbox(label=\"Question\", value=\"OCR the image precisely.\")\n", 405 | " submit_btn = gr.Button(value=\"Submit\", elem_classes=\"submit-btn\")\n", 406 | "\n", 407 | " with gr.Column():\n", 408 | " output_text = gr.Textbox(label=\"Output Text\", lines=7)\n", 409 | "\n", 410 | " with gr.Accordion(\"Plain Text\", open=False):\n", 411 | " plain_text_output = gr.Textbox(label=\"Standardized Plain Text\", lines=10)\n", 412 | "\n", 413 | " submit_btn.click(\n", 414 | " qwen_inference, [model_choice, input_media, text_input], [output_text]\n", 415 | " ).then(\n", 416 | " lambda output_text: format_plain_text(output_text), [output_text], [plain_text_output]\n", 417 | " )\n", 418 | "\n", 419 | " with gr.Accordion(\"Docx/PDF Settings\", open=False):\n", 420 | " with gr.Row():\n", 421 | " with gr.Column():\n", 422 | " line_spacing = gr.Dropdown(\n", 423 | " choices=[0.5, 1.0, 1.15, 1.5, 2.0, 2.5, 3.0],\n", 424 | " value=1.5,\n", 425 | " label=\"Line Spacing\"\n", 426 | " )\n", 427 | " font_size = gr.Dropdown(\n", 428 | " choices=[\"8\", \"10\", \"12\", \"14\", \"16\", \"18\", \"20\", \"22\", \"24\"],\n", 429 | " value=\"16\",\n", 430 | " label=\"Font Size\"\n", 431 | " )\n", 432 | " alignment = gr.Dropdown(\n", 433 | " choices=[\"Left\", \"Center\", \"Right\", \"Justified\"],\n", 434 | " value=\"Justified\",\n", 435 | " label=\"Text Alignment\"\n", 436 | " )\n", 437 | " image_size = gr.Dropdown(\n", 438 | " choices=[\"Small\", \"Medium\", \"Large\"],\n", 439 | " value=\"Medium\",\n", 440 | " label=\"Image Size\"\n", 441 | " )\n", 442 | " file_format = gr.Radio([\"pdf\", \"docx\"], label=\"File Format\", value=\"pdf\")\n", 443 | "\n", 444 | " get_document_btn = gr.Button(value=\"Get Document\", elem_classes=\"download-btn\")\n", 445 | "\n", 446 | " get_document_btn.click(\n", 447 | " generate_document, [input_media, output_text, file_format, font_size, line_spacing, alignment, image_size], gr.File(label=\"Download Document\")\n", 448 | " )\n", 449 | "\n", 450 | "demo.launch(debug=True)" 451 | ] 452 | }, 453 | { 454 | "cell_type": "markdown", 455 | "metadata": { 456 | "id": "iujTALhGd33H" 457 | }, 458 | "source": [ 459 | "**Demo Inference**\n", 460 | "\n", 461 | "`Query Input : OCR the Image Precisely`\n", 462 | "\n", 463 | "\n", 464 | " \n", 465 | " \n", 468 | " \n", 471 | " \n", 472 | "
\n", 466 | " \"Image\n", 467 | " \n", 469 | " \"Image\n", 470 | "
\n" 473 | ] 474 | } 475 | ], 476 | "metadata": { 477 | "accelerator": "GPU", 478 | "colab": { 479 | "gpuType": "T4", 480 | "provenance": [] 481 | }, 482 | "kernelspec": { 483 | "display_name": "Python 3", 484 | "name": "python3" 485 | }, 486 | "language_info": { 487 | "name": "python" 488 | }, 489 | "widgets": { 490 | "application/vnd.jupyter.widget-state+json": { 491 | "06bcc05a336c4bc8a16fd64b8c93e90a": { 492 | "model_module": "@jupyter-widgets/base", 493 | "model_module_version": "1.2.0", 494 | "model_name": "LayoutModel", 495 | "state": { 496 | "_model_module": "@jupyter-widgets/base", 497 | "_model_module_version": "1.2.0", 498 | "_model_name": "LayoutModel", 499 | "_view_count": null, 500 | "_view_module": "@jupyter-widgets/base", 501 | "_view_module_version": "1.2.0", 502 | "_view_name": "LayoutView", 503 | "align_content": null, 504 | "align_items": null, 505 | "align_self": null, 506 | "border": null, 507 | "bottom": null, 508 | "display": null, 509 | "flex": null, 510 | 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