import spaces import json import math import os import traceback from io import BytesIO from typing import Any, Dict, List, Optional, Tuple import re import time from threading import Thread from io import BytesIO import uuid import tempfile import gradio as gr import requests import torch from PIL import Image import fitz import numpy as np from transformers import AutoModelForCausalLM, AutoTokenizer, Qwen2VLImageProcessor from reportlab.lib.pagesizes import A4 from reportlab.lib.styles import getSampleStyleSheet from reportlab.platypus import SimpleDocTemplate, Image as RLImage, Paragraph, Spacer from reportlab.lib.units import inch # --- Constants and Model Setup --- MAX_INPUT_TOKEN_LENGTH = 4096 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print("CUDA_VISIBLE_DEVICES=", os.environ.get("CUDA_VISIBLE_DEVICES")) print("torch.__version__ =", torch.__version__) print("torch.version.cuda =", torch.version.cuda) print("cuda available:", torch.cuda.is_available()) print("cuda device count:", torch.cuda.device_count()) if torch.cuda.is_available(): print("current device:", torch.cuda.current_device()) print("device name:", torch.cuda.get_device_name(torch.cuda.current_device())) print("Using device:", device) # --- Model Loading: tencent/POINTS-Reader --- MODEL_PATH = 'tencent/POINTS-Reader' print(f"Loading model: {MODEL_PATH}") model = AutoModelForCausalLM.from_pretrained( MODEL_PATH, trust_remote_code=True, torch_dtype=torch.float16, device_map='auto' ) tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True) image_processor = Qwen2VLImageProcessor.from_pretrained(MODEL_PATH) print("Model loaded successfully.") # --- PDF Generation and Preview Utility Function --- def generate_and_preview_pdf(image: Image.Image, text_content: str, font_size: int, line_spacing: float, alignment: str, image_size: str): """ Generates a PDF, saves it, and then creates image previews of its pages. Returns the path to the PDF and a list of paths to the preview images. """ if image is None or not text_content or not text_content.strip(): raise gr.Error("Cannot generate PDF. Image or text content is missing.") # --- 1. Generate the PDF --- temp_dir = tempfile.gettempdir() pdf_filename = os.path.join(temp_dir, f"output_{uuid.uuid4()}.pdf") doc = SimpleDocTemplate( pdf_filename, pagesize=A4, rightMargin=inch, leftMargin=inch, topMargin=inch, bottomMargin=inch ) styles = getSampleStyleSheet() style_normal = styles["Normal"] style_normal.fontSize = int(font_size) style_normal.leading = int(font_size) * line_spacing style_normal.alignment = {"Left": 0, "Center": 1, "Right": 2, "Justified": 4}[alignment] story = [] img_buffer = BytesIO() image.save(img_buffer, format='PNG') img_buffer.seek(0) page_width, _ = A4 available_width = page_width - 2 * inch image_widths = { "Small": available_width * 0.3, "Medium": available_width * 0.6, "Large": available_width * 0.9, } img_width = image_widths[image_size] img = RLImage(img_buffer, width=img_width, height=image.height * (img_width / image.width)) story.append(img) story.append(Spacer(1, 12)) cleaned_text = re.sub(r'#+\s*', '', text_content).replace("*", "") text_paragraphs = cleaned_text.split('\n') for para in text_paragraphs: if para.strip(): story.append(Paragraph(para, style_normal)) doc.build(story) # --- 2. Render PDF pages as images for preview --- preview_images = [] try: pdf_doc = fitz.open(pdf_filename) for page_num in range(len(pdf_doc)): page = pdf_doc.load_page(page_num) pix = page.get_pixmap(dpi=150) preview_img_path = os.path.join(temp_dir, f"preview_{uuid.uuid4()}_p{page_num}.png") pix.save(preview_img_path) preview_images.append(preview_img_path) pdf_doc.close() except Exception as e: print(f"Error generating PDF preview: {e}") return pdf_filename, preview_images # --- Core Application Logic --- @spaces.GPU def process_document_stream( image: Image.Image, prompt_input: str, max_new_tokens: int, temperature: float, top_p: float, top_k: int, repetition_penalty: float ): """ Main function that handles model inference using tencent/POINTS-Reader. """ if image is None: yield "Please upload an image.", "" return if not prompt_input or not prompt_input.strip(): yield "Please enter a prompt.", "" return temp_image_path = None try: # --- FIX: Save the PIL Image to a temporary file --- # The model expects a file path, not a PIL object. temp_dir = tempfile.gettempdir() temp_image_path = os.path.join(temp_dir, f"temp_image_{uuid.uuid4()}.png") image.save(temp_image_path) # Prepare content for the model using the temporary file path content = [ dict(type='image', image=temp_image_path), dict(type='text', text=prompt_input) ] messages = [ { 'role': 'user', 'content': content } ] # Prepare generation configuration from UI inputs generation_config = { 'max_new_tokens': max_new_tokens, 'repetition_penalty': repetition_penalty, 'temperature': temperature, 'top_p': top_p, 'top_k': top_k, 'do_sample': True if temperature > 0 else False } # Run inference response = model.chat( messages, tokenizer, image_processor, generation_config ) # Yield the full response at once yield response, response except Exception as e: traceback.print_exc() yield f"An error occurred during processing: {str(e)}", "" finally: # --- Clean up the temporary image file --- if temp_image_path and os.path.exists(temp_image_path): os.remove(temp_image_path) # --- Gradio UI Definition --- def create_gradio_interface(): """Builds and returns the Gradio web interface.""" css = """ .main-container { max-width: 1400px; margin: 0 auto; } .process-button { border: none !important; color: white !important; font-weight: bold !important; background-color: blue !important;} .process-button:hover { background-color: darkblue !important; transform: translateY(-2px) !important; box-shadow: 0 4px 8px rgba(0,0,0,0.2) !important; } #gallery { min-height: 400px; } """ with gr.Blocks(theme="bethecloud/storj_theme", css=css) as demo: gr.HTML(f"""

Document Conversion with POINTS Reader 📖

Using tencent/POINTS-Reader Multimodal for Image Content Extraction

""") with gr.Row(): # Left Column (Inputs) with gr.Column(scale=1): gr.Textbox( label="Model in Use ⚡", value="tencent/POINTS-Reader", interactive=False ) prompt_input = gr.Textbox( label="Query Input", placeholder="✦︎ Enter the prompt", value="Perform OCR on the image precisely.", ) image_input = gr.Image(label="Upload Image", type="pil", sources=['upload']) with gr.Accordion("Advanced Settings", open=False): max_new_tokens = gr.Slider(minimum=512, maximum=8192, value=2048, step=256, label="Max New Tokens") temperature = gr.Slider(label="Temperature", minimum=0.1, maximum=1.0, step=0.05, value=0.7) top_p = gr.Slider(label="Top-p (nucleus sampling)", minimum=0.05, maximum=1.0, step=0.05, value=0.8) top_k = gr.Slider(label="Top-k", minimum=1, maximum=100, step=1, value=20) repetition_penalty = gr.Slider(label="Repetition penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.05) gr.Markdown("### PDF Export Settings") font_size = gr.Dropdown(choices=["8", "10", "12", "14", "16", "18"], value="12", label="Font Size") line_spacing = gr.Dropdown(choices=[1.0, 1.15, 1.5, 2.0], value=1.15, label="Line Spacing") alignment = gr.Dropdown(choices=["Left", "Center", "Right", "Justified"], value="Justified", label="Text Alignment") image_size = gr.Dropdown(choices=["Small", "Medium", "Large"], value="Medium", label="Image Size in PDF") process_btn = gr.Button("🚀 Process Image", variant="primary", elem_classes=["process-button"], size="lg") clear_btn = gr.Button("🗑️ Clear All", variant="secondary") # Right Column (Outputs) with gr.Column(scale=2): with gr.Tabs() as tabs: with gr.Tab("📝 Extracted Content"): raw_output_stream = gr.Textbox(label="Raw Model Output (max T ≤ 120s)", interactive=False, lines=15, show_copy_button=True) with gr.Row(): examples = gr.Examples( examples=["examples/1.png", "examples/2.png", "examples/3.png", "examples/4.png", "examples/5.png"], inputs=image_input, label="Examples" ) gr.Markdown("[Report-Bug💻](https://huggingface.co/spaces/prithivMLmods/POINTS-Reader-OCR/discussions) | [prithivMLmods🤗](https://huggingface.co/prithivMLmods)") with gr.Tab("📰 README.md"): with gr.Accordion("(Result.md)", open=True): markdown_output = gr.Markdown() with gr.Tab("📋 PDF Preview"): generate_pdf_btn = gr.Button("📄 Generate PDF & Render", variant="primary") pdf_output_file = gr.File(label="Download Generated PDF", interactive=False) pdf_preview_gallery = gr.Gallery(label="PDF Page Preview", show_label=True, elem_id="gallery", columns=2, object_fit="contain", height="auto") # Event Handlers def clear_all_outputs(): return None, "", "Raw output will appear here.", "", None, None process_btn.click( fn=process_document_stream, inputs=[image_input, prompt_input, max_new_tokens, temperature, top_p, top_k, repetition_penalty], outputs=[raw_output_stream, markdown_output] ) generate_pdf_btn.click( fn=generate_and_preview_pdf, inputs=[image_input, raw_output_stream, font_size, line_spacing, alignment, image_size], outputs=[pdf_output_file, pdf_preview_gallery] ) clear_btn.click( clear_all_outputs, outputs=[image_input, prompt_input, raw_output_stream, markdown_output, pdf_output_file, pdf_preview_gallery] ) return demo if __name__ == "__main__": demo = create_gradio_interface() demo.queue(max_size=50).launch(share=True, show_error=True)