diff --git "a/monkey-OCR/Multimodal_OCR_ReportLab_MonkeyOCR.ipynb" "b/monkey-OCR/Multimodal_OCR_ReportLab_MonkeyOCR.ipynb"
new file mode 100644--- /dev/null
+++ "b/monkey-OCR/Multimodal_OCR_ReportLab_MonkeyOCR.ipynb"
@@ -0,0 +1,6305 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uFovmijgUV1Z"
+      },
+      "source": [
+        "***Multimodal OCR ReportLab : MonkeyOCR***\n",
+        "\n",
+        "*notebook by : [prithivMLmods](https://huggingface.co/prithivMLmods)🤗*"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RugX4SGZV-8O"
+      },
+      "source": [
+        "***Installing all necessary packages***"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "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,
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+        "id": "tElKr2Fkp1bO",
+        "outputId": "feaa4d74-ec18-41fa-af84-23a5393ae3dc"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Loading MonkeyOCR🤗. Hold tight, this will take around 4-6 minutes..\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
+            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+            "You will be able to reuse this secret in all of your notebooks.\n",
+            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "config.json: 0.00B [00:00, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "6b4566c473ed473694a8cc4724c522b7"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "model.safetensors.index.json: 0.00B [00:00, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Fetching 2 files:   0%|          | 0/2 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "c500ed842c2147db82c2a5c96c9c8d19"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "model-00001-of-00002.safetensors:   0%|          | 0.00/5.00G [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "627c4c3dc0574db49d414de5fc91db99"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "model-00002-of-00002.safetensors:   0%|          | 0.00/2.51G [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "2d7dea9d16d74fd8b4b575ee4d73af28"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "generation_config.json:   0%|          | 0.00/249 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "07e60b489bb84e8e987c9167341d0adb"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "preprocessor_config.json:   0%|          | 0.00/575 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
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+              "model_id": "f1dcc832125b45689474e8fc9f6f13d4"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "tokenizer_config.json: 0.00B [00:00, ?B/s]"
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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "vocab.json: 0.00B [00:00, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "00d8c0d839394810aa7633feac0c6cbd"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "merges.txt: 0.00B [00:00, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "tokenizer.json:   0%|          | 0.00/11.4M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "added_tokens.json:   0%|          | 0.00/605 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "80afac13d48e48e987a9cd0dd00c92dc"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "special_tokens_map.json:   0%|          | 0.00/613 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "f2abbcdf4e9b4358ac0392773232e8f8"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "You have video processor config saved in `preprocessor.json` file which is deprecated. Video processor configs should be saved in their own `video_preprocessor.json` file. You can rename the file or load and save the processor back which renames it automatically. Loading from `preprocessor.json` will be removed in v5.0.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "chat_template.json: 0.00B [00:00, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "24e2fb8bfb034a8e925f276a32cb11a4"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "theme_schema%400.0.1.json: 0.00B [00:00, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "ec9aed76289c40ccbbc369123b8848c5"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "It looks like you are running Gradio on a hosted a Jupyter notebook. For the Gradio app to work, sharing must be enabled. Automatically setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
+            "\n",
+            "Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
+            "* Running on public URL: https://a876d6b2c555bb62b7.gradio.live\n",
+            "\n",
+            "This share link expires in 1 week. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "<div><iframe src=\"https://a876d6b2c555bb62b7.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "#Model used in the app: https://huggingface.co/echo840/MonkeyOCR\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",
+        "    \"MonkeyOCR\": \"echo840/MonkeyOCR\",\n",
+        "}\n",
+        "SUBFOLDER = \"Recognition\"\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",
+        "        subfolder=SUBFOLDER,\n",
+        "        torch_dtype=torch.float16\n",
+        "    ).to(\"cuda\").eval()\n",
+        "    processors[name] = AutoProcessor.from_pretrained(model_id, subfolder=SUBFOLDER, 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 : MonkeyOCR**\")\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=\"MonkeyOCR\"\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": {
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