{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-b4-SW1aGOcF"
      },
      "source": [
        "# **Radiology Infer**\n",
        "\n",
        "Qwen2VLForConditionalGeneration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oDmd1ZObGSel",
        "outputId": "847931ae-b6fc-4b3b-e13e-849b9bb3fea1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: pydub, fpdf, uvicorn, tomlkit, semantic-version, ruff, reportlab, python-multipart, python-docx, markupsafe, jedi, ffmpy, av, aiofiles, starlette, qwen-vl-utils, safehttpx, gradio-client, fastapi, gradio, spaces\n",
            "  Attempting uninstall: markupsafe\n",
            "    Found existing installation: MarkupSafe 3.0.2\n",
            "    Uninstalling MarkupSafe-3.0.2:\n",
            "      Successfully uninstalled MarkupSafe-3.0.2\n",
            "Successfully installed aiofiles-23.2.1 av-14.0.1 fastapi-0.115.6 ffmpy-0.5.0 fpdf-1.7.2 gradio-5.12.0 gradio-client-1.5.4 jedi-0.19.2 markupsafe-2.1.5 pydub-0.25.1 python-docx-1.1.2 python-multipart-0.0.20 qwen-vl-utils-0.0.8 reportlab-4.2.5 ruff-0.9.2 safehttpx-0.1.6 semantic-version-2.10.0 spaces-0.32.0 starlette-0.41.3 tomlkit-0.13.2 uvicorn-0.34.0\n"
          ]
        }
      ],
      "source": [
        "!pip install gradio spaces transformers accelerate numpy requests torch torchvision qwen-vl-utils av ipython reportlab fpdf python-docx pillow huggingface_hub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
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          ]
        },
        "id": "ovBSsRFhGbs2",
        "outputId": "8715c0aa-4b72-424d-dee2-14f8c0372460"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading rad...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:104: UserWarning: \n",
            "Error while fetching `HF_TOKEN` secret value from your vault: 'Requesting secret HF_TOKEN timed out. Secrets can only be fetched when running from the Colab UI.'.\n",
            "You are not authenticated with the Hugging Face Hub in this notebook.\n",
            "If the error persists, please let us know by opening an issue on GitHub (https://github.com/huggingface/huggingface_hub/issues/new).\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.22k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2704a894e6c6483e96db9e06ab138bf0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/4.42G [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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              "model_id": "0893b272924c460582caa02ebf7fe377"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "`Qwen2VLRotaryEmbedding` can now be fully parameterized by passing the model config through the `config` argument. All other arguments will be removed in v4.46\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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        },
        {
          "output_type": "display_data",
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/2.78M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "768d8bb7265443b286131b928731133c"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "added_tokens.json:   0%|          | 0.00/408 [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b3fa86bb50bb4f5ab721686fc2a16948"
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        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "af3703e2d6244f66843b3e7528fdab97"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "chat_template.json:   0%|          | 0.00/1.05k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "21f3e28ddfa04f10a5cf9066dce3bd70"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running Gradio in a Colab notebook requires sharing 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://7dcd79a12a0fe02f48.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. 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://7dcd79a12a0fe02f48.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Keyboard interruption in main thread... closing server.\n",
            "Killing tunnel 127.0.0.1:7860 <> https://7dcd79a12a0fe02f48.gradio.live\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "# Authenticate with Hugging Face\n",
        "from huggingface_hub import login\n",
        "\n",
        "# Log in to Hugging Face using the provided token\n",
        "hf_token = '--xxx--'\n",
        "login(hf_token)\n",
        "\n",
        "#Demo\n",
        "import gradio as gr\n",
        "import spaces\n",
        "from transformers import Qwen2VLForConditionalGeneration, 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",
        "    \"rad\": \"prithivMLmods/Radiology-Infer-Mini\",\n",
        "}\n",
        "\n",
        "# Preload models and processors into CUDA\n",
        "models = {}\n",
        "processors = {}\n",
        "for name, model_id in MODEL_OPTIONS.items():\n",
        "    print(f\"Loading {name}...\")\n",
        "    models[name] = Qwen2VLForConditionalGeneration.from_pretrained(\n",
        "        model_id,\n",
        "        trust_remote_code=True,\n",
        "        torch_dtype=torch.float16\n",
        "    ).to(\"cuda\").eval()\n",
        "    processors[name] = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)\n",
        "\n",
        "image_extensions = Image.registered_extensions()\n",
        "\n",
        "def identify_and_save_blob(blob_path):\n",
        "    \"\"\"Identifies if the blob is an image and saves it.\"\"\"\n",
        "    try:\n",
        "        with open(blob_path, 'rb') as file:\n",
        "            blob_content = file.read()\n",
        "            try:\n",
        "                Image.open(io.BytesIO(blob_content)).verify()  # Check if it's a valid image\n",
        "                extension = \".png\"  # Default to PNG for saving\n",
        "                media_type = \"image\"\n",
        "            except (IOError, SyntaxError):\n",
        "                raise ValueError(\"Unsupported media type. Please upload a valid image.\")\n",
        "\n",
        "            filename = f\"temp_{uuid.uuid4()}_media{extension}\"\n",
        "            with open(filename, \"wb\") as f:\n",
        "                f.write(blob_content)\n",
        "\n",
        "            return filename, media_type\n",
        "\n",
        "    except FileNotFoundError:\n",
        "        raise ValueError(f\"The file {blob_path} was not found.\")\n",
        "    except Exception as e:\n",
        "        raise ValueError(f\"An error occurred while processing the file: {e}\")\n",
        "\n",
        "@spaces.GPU\n",
        "def qwen_inference(model_name, media_input, text_input=None):\n",
        "    \"\"\"Handles inference for the selected model.\"\"\"\n",
        "    model = models[model_name]\n",
        "    processor = processors[model_name]\n",
        "\n",
        "    if isinstance(media_input, str):\n",
        "        media_path = media_input\n",
        "        if media_path.endswith(tuple([i for i in image_extensions.keys()])):\n",
        "            media_type = \"image\"\n",
        "        else:\n",
        "            try:\n",
        "                media_path, media_type = identify_and_save_blob(media_input)\n",
        "            except Exception as e:\n",
        "                raise ValueError(\"Unsupported media type. Please upload a valid image.\")\n",
        "\n",
        "    messages = [\n",
        "        {\n",
        "            \"role\": \"user\",\n",
        "            \"content\": [\n",
        "                {\n",
        "                    \"type\": media_type,\n",
        "                    media_type: media_path\n",
        "                },\n",
        "                {\"type\": \"text\", \"text\": text_input},\n",
        "            ],\n",
        "        }\n",
        "    ]\n",
        "\n",
        "    text = processor.apply_chat_template(\n",
        "        messages, tokenize=False, add_generation_prompt=True\n",
        "    )\n",
        "    image_inputs, _ = process_vision_info(messages)\n",
        "    inputs = processor(\n",
        "        text=[text],\n",
        "        images=image_inputs,\n",
        "        padding=True,\n",
        "        return_tensors=\"pt\",\n",
        "    ).to(\"cuda\")\n",
        "\n",
        "    streamer = TextIteratorStreamer(\n",
        "        processor.tokenizer, skip_prompt=True, skip_special_tokens=True\n",
        "    )\n",
        "    generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024)\n",
        "\n",
        "    thread = Thread(target=model.generate, kwargs=generation_kwargs)\n",
        "    thread.start()\n",
        "\n",
        "    buffer = \"\"\n",
        "    for new_text in streamer:\n",
        "        buffer += new_text\n",
        "        # Remove <|im_end|> or similar tokens from the output\n",
        "        buffer = buffer.replace(\"<|im_end|>\", \"\")\n",
        "        yield buffer\n",
        "\n",
        "def format_plain_text(output_text):\n",
        "    \"\"\"Formats the output text as plain text without LaTeX delimiters.\"\"\"\n",
        "    # Remove LaTeX delimiters and convert to plain text\n",
        "    plain_text = output_text.replace(\"\\\\(\", \"\").replace(\"\\\\)\", \"\").replace(\"\\\\[\", \"\").replace(\"\\\\]\", \"\")\n",
        "    return plain_text\n",
        "\n",
        "def generate_document(media_path, output_text, file_format, font_size, line_spacing, alignment, image_size):\n",
        "    \"\"\"Generates a document with the input image and plain text output.\"\"\"\n",
        "    plain_text = format_plain_text(output_text)\n",
        "    if file_format == \"pdf\":\n",
        "        return generate_pdf(media_path, plain_text, font_size, line_spacing, alignment, image_size)\n",
        "    elif file_format == \"docx\":\n",
        "        return generate_docx(media_path, plain_text, font_size, line_spacing, alignment, image_size)\n",
        "\n",
        "def generate_pdf(media_path, plain_text, font_size, line_spacing, alignment, image_size):\n",
        "    \"\"\"Generates a PDF document.\"\"\"\n",
        "    filename = f\"output_{uuid.uuid4()}.pdf\"\n",
        "    doc = SimpleDocTemplate(\n",
        "        filename,\n",
        "        pagesize=A4,\n",
        "        rightMargin=inch,\n",
        "        leftMargin=inch,\n",
        "        topMargin=inch,\n",
        "        bottomMargin=inch\n",
        "    )\n",
        "    styles = getSampleStyleSheet()\n",
        "    styles[\"Normal\"].fontSize = int(font_size)\n",
        "    styles[\"Normal\"].leading = int(font_size) * line_spacing\n",
        "    styles[\"Normal\"].alignment = {\n",
        "        \"Left\": 0,\n",
        "        \"Center\": 1,\n",
        "        \"Right\": 2,\n",
        "        \"Justified\": 4\n",
        "    }[alignment]\n",
        "\n",
        "    story = []\n",
        "\n",
        "    # Add image with size adjustment\n",
        "    image_sizes = {\n",
        "        \"Small\": (200, 200),\n",
        "        \"Medium\": (400, 400),\n",
        "        \"Large\": (600, 600)\n",
        "    }\n",
        "    img = RLImage(media_path, width=image_sizes[image_size][0], height=image_sizes[image_size][1])\n",
        "    story.append(img)\n",
        "    story.append(Spacer(1, 12))\n",
        "\n",
        "    # Add plain text output\n",
        "    text = Paragraph(plain_text, styles[\"Normal\"])\n",
        "    story.append(text)\n",
        "\n",
        "    doc.build(story)\n",
        "    return filename\n",
        "\n",
        "def generate_docx(media_path, plain_text, font_size, line_spacing, alignment, image_size):\n",
        "    \"\"\"Generates a DOCX document.\"\"\"\n",
        "    filename = f\"output_{uuid.uuid4()}.docx\"\n",
        "    doc = docx.Document()\n",
        "\n",
        "    # Add image with size adjustment\n",
        "    image_sizes = {\n",
        "        \"Small\": docx.shared.Inches(2),\n",
        "        \"Medium\": docx.shared.Inches(4),\n",
        "        \"Large\": docx.shared.Inches(6)\n",
        "    }\n",
        "    doc.add_picture(media_path, width=image_sizes[image_size])\n",
        "    doc.add_paragraph()\n",
        "\n",
        "    # Add plain text output\n",
        "    paragraph = doc.add_paragraph()\n",
        "    paragraph.paragraph_format.line_spacing = line_spacing\n",
        "    paragraph.paragraph_format.alignment = {\n",
        "        \"Left\": WD_ALIGN_PARAGRAPH.LEFT,\n",
        "        \"Center\": WD_ALIGN_PARAGRAPH.CENTER,\n",
        "        \"Right\": WD_ALIGN_PARAGRAPH.RIGHT,\n",
        "        \"Justified\": WD_ALIGN_PARAGRAPH.JUSTIFY\n",
        "    }[alignment]\n",
        "    run = paragraph.add_run(plain_text)\n",
        "    run.font.size = docx.shared.Pt(int(font_size))\n",
        "\n",
        "    doc.save(filename)\n",
        "    return filename\n",
        "\n",
        "# CSS for output styling\n",
        "css = \"\"\"\n",
        "  #output {\n",
        "    height: 500px;\n",
        "    overflow: auto;\n",
        "    border: 1px solid #ccc;\n",
        "  }\n",
        ".submit-btn {\n",
        "    background-color: #cf3434  !important;\n",
        "    color: white !important;\n",
        "}\n",
        ".submit-btn:hover {\n",
        "    background-color: #ff2323 !important;\n",
        "}\n",
        ".download-btn {\n",
        "    background-color: #35a6d6 !important;\n",
        "    color: white !important;\n",
        "}\n",
        ".download-btn:hover {\n",
        "    background-color: #22bcff !important;\n",
        "}\n",
        "\"\"\"\n",
        "\n",
        "# Gradio app setup\n",
        "with gr.Blocks(css=css) as demo:\n",
        "    gr.Markdown(\"# Rad-Reasoner\")\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=\"rad\"\n",
        "                )\n",
        "                input_media = gr.File(\n",
        "                    label=\"Upload Image\", type=\"filepath\"\n",
        "                )\n",
        "                text_input = gr.Textbox(label=\"Question\", placeholder=\"Ask a question about the image...\")\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=10)\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",
        "        # Add examples directly usable by clicking\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=\"18\",\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=\"Small\",\n",
        "                    label=\"Image Size\"\n",
        "                )\n",
        "                file_format = gr.Radio([\"pdf\", \"docx\"], label=\"File Format\", value=\"pdf\")\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)"
      ]
    }
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