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Model: prithivMLmods/Qwen2-VL-OCR-2B-Instruct Source: Original Platform
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343
Qwen2vl With ReportLab Documentation/app.py
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343
Qwen2vl With ReportLab Documentation/app.py
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import gradio as gr
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import spaces
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from transformers import Qwen2VLForConditionalGeneration, AutoProcessor, TextIteratorStreamer
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from qwen_vl_utils import process_vision_info
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import torch
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from PIL import Image
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import os
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import uuid
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import io
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from threading import Thread
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from reportlab.lib.pagesizes import A4
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from reportlab.lib.styles import getSampleStyleSheet
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from reportlab.lib import colors
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from reportlab.platypus import SimpleDocTemplate, Image as RLImage, Paragraph, Spacer
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from reportlab.lib.units import inch
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from reportlab.pdfbase import pdfmetrics
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from reportlab.pdfbase.ttfonts import TTFont
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import docx
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from docx.enum.text import WD_ALIGN_PARAGRAPH
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# Define model options
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MODEL_OPTIONS = {
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"Qwen2VL Base": "Qwen/Qwen2-VL-2B-Instruct",
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"Latex OCR": "prithivMLmods/Qwen2-VL-OCR-2B-Instruct",
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"Math Prase": "prithivMLmods/Qwen2-VL-Math-Prase-2B-Instruct",
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"Text Analogy Ocrtest": "prithivMLmods/Qwen2-VL-Ocrtest-2B-Instruct"
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}
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# Preload models and processors into CUDA
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models = {}
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processors = {}
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for name, model_id in MODEL_OPTIONS.items():
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print(f"Loading {name}...")
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models[name] = Qwen2VLForConditionalGeneration.from_pretrained(
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model_id,
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trust_remote_code=True,
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torch_dtype=torch.float16
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).to("cuda").eval()
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processors[name] = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
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image_extensions = Image.registered_extensions()
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def identify_and_save_blob(blob_path):
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"""Identifies if the blob is an image and saves it."""
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try:
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with open(blob_path, 'rb') as file:
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blob_content = file.read()
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try:
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Image.open(io.BytesIO(blob_content)).verify() # Check if it's a valid image
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extension = ".png" # Default to PNG for saving
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media_type = "image"
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except (IOError, SyntaxError):
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raise ValueError("Unsupported media type. Please upload a valid image.")
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filename = f"temp_{uuid.uuid4()}_media{extension}"
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with open(filename, "wb") as f:
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f.write(blob_content)
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return filename, media_type
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except FileNotFoundError:
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raise ValueError(f"The file {blob_path} was not found.")
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except Exception as e:
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raise ValueError(f"An error occurred while processing the file: {e}")
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@spaces.GPU
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def qwen_inference(model_name, media_input, text_input=None):
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"""Handles inference for the selected model."""
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model = models[model_name]
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processor = processors[model_name]
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if isinstance(media_input, str):
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media_path = media_input
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if media_path.endswith(tuple([i for i in image_extensions.keys()])):
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media_type = "image"
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else:
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try:
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media_path, media_type = identify_and_save_blob(media_input)
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except Exception as e:
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raise ValueError("Unsupported media type. Please upload a valid image.")
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": media_type,
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media_type: media_path
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},
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{"type": "text", "text": text_input},
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],
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}
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]
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text = processor.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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image_inputs, _ = process_vision_info(messages)
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inputs = processor(
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text=[text],
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images=image_inputs,
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padding=True,
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return_tensors="pt",
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).to("cuda")
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streamer = TextIteratorStreamer(
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processor.tokenizer, skip_prompt=True, skip_special_tokens=True
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)
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generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024)
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thread = Thread(target=model.generate, kwargs=generation_kwargs)
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thread.start()
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buffer = ""
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for new_text in streamer:
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buffer += new_text
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# Remove <|im_end|> or similar tokens from the output
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buffer = buffer.replace("<|im_end|>", "")
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yield buffer
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def format_plain_text(output_text):
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"""Formats the output text as plain text without LaTeX delimiters."""
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# Remove LaTeX delimiters and convert to plain text
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plain_text = output_text.replace("\\(", "").replace("\\)", "").replace("\\[", "").replace("\\]", "")
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return plain_text
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def generate_document(media_path, output_text, file_format, font_choice, font_size, line_spacing, alignment, image_size):
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"""Generates a document with the input image and plain text output."""
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plain_text = format_plain_text(output_text)
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if file_format == "pdf":
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return generate_pdf(media_path, plain_text, font_choice, font_size, line_spacing, alignment, image_size)
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elif file_format == "docx":
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return generate_docx(media_path, plain_text, font_choice, font_size, line_spacing, alignment, image_size)
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def generate_pdf(media_path, plain_text, font_choice, font_size, line_spacing, alignment, image_size):
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"""Generates a PDF document."""
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filename = f"output_{uuid.uuid4()}.pdf"
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doc = SimpleDocTemplate(
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filename,
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pagesize=A4,
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rightMargin=inch,
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leftMargin=inch,
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topMargin=inch,
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bottomMargin=inch
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)
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styles = getSampleStyleSheet()
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styles["Normal"].fontName = font_choice
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styles["Normal"].fontSize = int(font_size)
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styles["Normal"].leading = int(font_size) * line_spacing
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styles["Normal"].alignment = {
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"Left": 0,
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"Center": 1,
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"Right": 2,
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"Justified": 4
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}[alignment]
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# Register font
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font_path = f"font/{font_choice}"
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pdfmetrics.registerFont(TTFont(font_choice, font_path))
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story = []
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# Add image with size adjustment
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image_sizes = {
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"Small": (200, 200),
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"Medium": (400, 400),
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"Large": (600, 600)
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}
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img = RLImage(media_path, width=image_sizes[image_size][0], height=image_sizes[image_size][1])
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story.append(img)
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story.append(Spacer(1, 12))
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# Add plain text output
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text = Paragraph(plain_text, styles["Normal"])
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story.append(text)
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doc.build(story)
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return filename
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def generate_docx(media_path, plain_text, font_choice, font_size, line_spacing, alignment, image_size):
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"""Generates a DOCX document."""
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filename = f"output_{uuid.uuid4()}.docx"
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doc = docx.Document()
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# Add image with size adjustment
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image_sizes = {
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"Small": docx.shared.Inches(2),
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"Medium": docx.shared.Inches(4),
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"Large": docx.shared.Inches(6)
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}
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doc.add_picture(media_path, width=image_sizes[image_size])
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doc.add_paragraph()
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# Add plain text output
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paragraph = doc.add_paragraph()
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paragraph.paragraph_format.line_spacing = line_spacing
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paragraph.paragraph_format.alignment = {
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"Left": WD_ALIGN_PARAGRAPH.LEFT,
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"Center": WD_ALIGN_PARAGRAPH.CENTER,
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"Right": WD_ALIGN_PARAGRAPH.RIGHT,
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"Justified": WD_ALIGN_PARAGRAPH.JUSTIFY
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}[alignment]
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run = paragraph.add_run(plain_text)
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run.font.name = font_choice
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run.font.size = docx.shared.Pt(int(font_size))
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doc.save(filename)
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return filename
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# CSS for output styling
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css = """
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#output {
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height: 500px;
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overflow: auto;
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border: 1px solid #ccc;
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}
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.submit-btn {
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background-color: #cf3434 !important;
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color: white !important;
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}
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.submit-btn:hover {
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background-color: #ff2323 !important;
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}
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.download-btn {
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background-color: #35a6d6 !important;
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color: white !important;
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}
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.download-btn:hover {
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background-color: #22bcff !important;
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}
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"""
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# Gradio app setup
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with gr.Blocks(css=css) as demo:
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gr.Markdown("# Qwen2VL Models: Vision and Language Processing")
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with gr.Tab(label="Image Input"):
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with gr.Row():
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with gr.Column():
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model_choice = gr.Dropdown(
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label="Model Selection",
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choices=list(MODEL_OPTIONS.keys()),
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value="Latex OCR"
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)
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input_media = gr.File(
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label="Upload Image", type="filepath"
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)
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text_input = gr.Textbox(label="Question", placeholder="Ask a question about the image...")
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submit_btn = gr.Button(value="Submit", elem_classes="submit-btn")
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with gr.Column():
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output_text = gr.Textbox(label="Output Text", lines=10)
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plain_text_output = gr.Textbox(label="Standardized Plain Text", lines=10)
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submit_btn.click(
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qwen_inference, [model_choice, input_media, text_input], [output_text]
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).then(
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lambda output_text: format_plain_text(output_text), [output_text], [plain_text_output]
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)
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# Add examples directly usable by clicking
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with gr.Row():
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gr.Examples(
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examples=[
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["examples/1.png", "summarize the letter", "Text Analogy Ocrtest"],
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["examples/2.jpg", "Summarize the full image in detail", "Latex OCR"],
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["examples/3.png", "Describe the photo", "Qwen2VL Base"],
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["examples/4.png", "summarize and solve the problem", "Math Prase"],
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],
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inputs=[input_media, text_input, model_choice],
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outputs=[output_text, plain_text_output],
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fn=lambda img, question, model: qwen_inference(model, img, question),
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cache_examples=False,
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)
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with gr.Row():
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with gr.Column():
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line_spacing = gr.Dropdown(
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choices=[0.5, 1.0, 1.15, 1.5, 2.0, 2.5, 3.0],
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value=1.5,
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label="Line Spacing"
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)
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font_size = gr.Dropdown(
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choices=["8", "10", "12", "14", "16", "18", "20", "22", "24"],
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value="18",
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label="Font Size"
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)
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font_choice = gr.Dropdown(
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choices=[
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"DejaVuMathTeXGyre.ttf",
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"FiraCode-Medium.ttf",
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"InputMono-Light.ttf",
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"JetBrainsMono-Thin.ttf",
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"ProggyCrossed Regular Mac.ttf",
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"SourceCodePro-Black.ttf",
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"arial.ttf",
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"calibri.ttf",
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"mukta-malar-extralight.ttf",
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"noto-sans-arabic-medium.ttf",
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"times new roman.ttf",
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"ANGSA.ttf",
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"Book-Antiqua.ttf",
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"CONSOLA.TTF",
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"COOPBL.TTF",
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"Rockwell-Bold.ttf",
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"Candara Light.TTF",
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"Carlito-Regular.ttf Carlito-Regular.ttf",
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"Castellar.ttf",
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"Courier New.ttf",
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"LSANS.TTF",
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"Lucida Bright Regular.ttf",
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"TRTempusSansITC.ttf",
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"Verdana.ttf",
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"bell-mt.ttf",
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"eras-itc-light.ttf",
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"fonnts.com-aptos-light.ttf",
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"georgia.ttf",
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"segoeuithis.ttf",
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"youyuan.TTF",
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"TfPonetoneExpanded-7BJZA.ttf",
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],
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value="youyuan.TTF",
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label="Font Choice"
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)
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alignment = gr.Dropdown(
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choices=["Left", "Center", "Right", "Justified"],
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value="Justified",
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label="Text Alignment"
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)
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image_size = gr.Dropdown(
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choices=["Small", "Medium", "Large"],
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value="Small",
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label="Image Size"
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)
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file_format = gr.Radio(["pdf", "docx"], label="File Format", value="pdf")
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get_document_btn = gr.Button(value="Get Document", elem_classes="download-btn")
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get_document_btn.click(
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generate_document, [input_media, output_text, file_format, font_choice, font_size, line_spacing, alignment, image_size], gr.File(label="Download Document")
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)
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demo.launch(debug=True)
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