Delete app.py

app.py

@@ -1,324 +0,0 @@
-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,
-    image_scale_factor: float, # New parameter for image scaling
-    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
-
-    # --- IMPLEMENTATION: Image Scaling based on user input ---
-    if image_scale_factor > 1.0:
-        try:
-            original_width, original_height = image.size
-            new_width = int(original_width * image_scale_factor)
-            new_height = int(original_height * image_scale_factor)
-            print(f"Scaling image from {image.size} to ({new_width}, {new_height}) with factor {image_scale_factor}.")
-            # Use a high-quality resampling filter for better results
-            image = image.resize((new_width, new_height), Image.Resampling.LANCZOS)
-        except Exception as e:
-            print(f"Error during image scaling: {e}")
-            # Continue with the original image if scaling fails
-            pass
-    # --- END IMPLEMENTATION ---
-
-    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"""
-        <div class="title" style="text-align: center">
-            <h1>Document Conversion with POINTS Reader 📖</h1>
-            <p style="font-size: 1.1em; color: #6b7280; margin-bottom: 0.6em;">
-                Using tencent/POINTS-Reader Multimodal for Image Content Extraction
-            </p>
-        </div>
-        """)
-
-        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):
-                    # --- NEW UI ELEMENT: Image Scaling Slider ---
-                    image_scale_factor = gr.Slider(
-                        minimum=1.0, 
-                        maximum=3.0, 
-                        value=1.0, 
-                        step=0.1, 
-                        label="Image Upscale Factor",
-                        info="Increases image size before processing. Can improve OCR on small text. Default: 1.0 (no change)."
-                    )
-                    # --- END NEW UI ELEMENT ---
-                    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.jpeg", 
-                                          "examples/2.jpeg", 
-                                          "examples/3.jpeg",
-                                          "examples/4.jpeg", 
-                                          "examples/5.jpeg"],
-                                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,
-            # --- UPDATE: Add the new slider to the inputs list ---
-            inputs=[image_input, prompt_input, image_scale_factor, 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)