diff --git "a/Chemical Identifier Demo Documentation/Chemical Identifier Demo Documentation.ipynb" "b/Chemical Identifier Demo Documentation/Chemical Identifier Demo Documentation.ipynb"
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "-b4-SW1aGOcF"
+ },
+ "source": [
+ "# **ChemQwen VL Chemical Identifier**\n",
+ "\n",
+ "Qwen2VLForConditionalGeneration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "oDmd1ZObGSel",
+ "outputId": "a2387c94-b73b-4fbd-bb0b-2b9ed6c2693e"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Requirement already satisfied: gradio in /usr/local/lib/python3.11/dist-packages (5.12.0)\n",
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+ "Requirement already satisfied: av in /usr/local/lib/python3.11/dist-packages (14.0.1)\n",
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+ "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas<3.0,>=1.0->gradio) (1.17.0)\n",
+ "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.11/dist-packages (from rich>=10.11.0->typer<1.0,>=0.12->gradio) (3.0.0)\n",
+ "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.11/dist-packages (from markdown-it-py>=2.2.0->rich>=10.11.0->typer<1.0,>=0.12->gradio) (0.1.2)\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": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000,
+ "referenced_widgets": [
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+ ]
+ },
+ "id": "ovBSsRFhGbs2",
+ "outputId": "b9929c12-469d-4cdb-d751-8cd1cb189e6f"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Loading ChemQwen...\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "942933334dd6498bac61554b53ced49d",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "config.json: 0%| | 0.00/1.22k [00:00"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Keyboard interruption in main thread... closing server.\n",
+ "Killing tunnel 127.0.0.1:7860 <> https://ff5d71f20d46028cd2.gradio.live\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": []
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "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 = '----xxxxx----'\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",
+ " \"ChemQwen\": \"prithivMLmods/ChemQwen-vL\",\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(\"# ChemQwen Chemical Identifier\")\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=\"ChemQwen\"\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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+ "accelerator": "GPU",
+ "colab": {
+ "gpuType": "T4",
+ "provenance": []
+ },
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