Create app.py

app.py

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+#!/usr/bin/env python3
+import os
+import subprocess
+import sys
+import threading
+
+import spaces
+import torch
+
+import gradio as gr
+from PIL import Image
+from io import BytesIO
+import pypdfium2 as pdfium
+from transformers import (
+    LightOnOcrForConditionalGeneration,
+    LightOnOcrProcessor,
+    TextIteratorStreamer,
+)
+import re
+import base64
+from collections import OrderedDict
+
+# Model Registry with all supported models
+MODEL_REGISTRY = {
+    "LightOnOCR-2-1B (Best OCR)": {
+        "model_id": "lightonai/LightOnOCR-2-1B",
+        "has_bbox": False,
+        "description": "Best overall OCR performance",
+    },
+    "LightOnOCR-2-1B-base": {
+        "model_id": "lightonai/LightOnOCR-2-1B-base",
+        "has_bbox": False,
+        "description": "Base OCR model",
+    },
+    "LightOnOCR-2-1B-ocr-soup": {
+        "model_id": "lightonai/LightOnOCR-2-1B-ocr-soup",
+        "has_bbox": False,
+        "description": "OCR soup variant",
+    },
+    "LightOnOCR-2-1B-bbox (Best Bbox)": {
+        "model_id": "lightonai/LightOnOCR-2-1B-bbox",
+        "has_bbox": True,
+        "description": "Best bounding box detection",
+    },
+    "LightOnOCR-2-1B-bbox-base": {
+        "model_id": "lightonai/LightOnOCR-2-1B-bbox-base",
+        "has_bbox": True,
+        "description": "Base bounding box model",
+    },
+    "LightOnOCR-2-1B-bbox-soup": {
+        "model_id": "lightonai/LightOnOCR-2-1B-bbox-soup",
+        "has_bbox": True,
+        "description": "Bounding box soup variant",
+    },
+}
+
+DEFAULT_MODEL = "LightOnOCR-2-1B (Best OCR)"
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Choose best attention implementation based on device
+if device == "cuda":
+    attn_implementation = "sdpa"
+    dtype = torch.bfloat16
+    print("Using sdpa for GPU")
+else:
+    attn_implementation = "eager"  # Best for CPU
+    dtype = torch.float32
+    print("Using eager attention for CPU")
+
+
+class ModelManager:
+    """Manages model loading with LRU caching and GPU memory management."""
+
+    def __init__(self, max_cached=2):
+        self._cache = OrderedDict()  # {model_id: (model, processor)}
+        self._max_cached = max_cached
+
+    def get_model(self, model_name):
+        """Get model and processor, loading if necessary."""
+        config = MODEL_REGISTRY.get(model_name)
+        if config is None:
+            raise ValueError(f"Unknown model: {model_name}")
+
+        model_id = config["model_id"]
+
+        # Check cache
+        if model_id in self._cache:
+            # Move to end (most recently used)
+            self._cache.move_to_end(model_id)
+            print(f"Using cached model: {model_name}")
+            return self._cache[model_id]
+
+        # Evict oldest if cache is full
+        while len(self._cache) >= self._max_cached:
+            evicted_id, (evicted_model, _) = self._cache.popitem(last=False)
+            print(f"Evicting model from cache: {evicted_id}")
+            del evicted_model
+            if device == "cuda":
+                torch.cuda.empty_cache()
+
+        # Load new model
+        print(f"Loading model: {model_name} ({model_id})...")
+        hf_token = os.environ.get("HF_TOKEN")
+        model = LightOnOcrForConditionalGeneration.from_pretrained(
+            model_id,
+            attn_implementation=attn_implementation,
+            torch_dtype=dtype,
+            trust_remote_code=True,
+            token=hf_token
+        ).to(device).eval()
+
+        processor = LightOnOcrProcessor.from_pretrained(
+            model_id,
+            trust_remote_code=True,
+            token=hf_token
+        )
+
+        # Add to cache
+        self._cache[model_id] = (model, processor)
+        print(f"Model loaded successfully: {model_name}")
+
+        return model, processor
+
+    def get_model_info(self, model_name):
+        """Get model info without loading."""
+        return MODEL_REGISTRY.get(model_name)
+
+
+# Initialize model manager
+model_manager = ModelManager(max_cached=2)
+print("Model manager initialized. Models will be loaded on first use.")
+
+
+def render_pdf_page(page, max_resolution=1540, scale=2.77):
+    """Render a PDF page to PIL Image."""
+    width, height = page.get_size()
+    pixel_width = width * scale
+    pixel_height = height * scale
+    resize_factor = min(1, max_resolution / pixel_width, max_resolution / pixel_height)
+    target_scale = scale * resize_factor
+    return page.render(scale=target_scale, rev_byteorder=True).to_pil()
+
+
+def process_pdf(pdf_path, page_num=1):
+    """Extract a specific page from PDF."""
+    pdf = pdfium.PdfDocument(pdf_path)
+    total_pages = len(pdf)
+    page_idx = min(max(int(page_num) - 1, 0), total_pages - 1)
+    
+    page = pdf[page_idx]
+    img = render_pdf_page(page)
+    
+    pdf.close()
+    return img, total_pages, page_idx + 1
+
+
+def clean_output_text(text):
+    """Remove chat template artifacts from output."""
+    # Remove common chat template markers
+    markers_to_remove = ["system", "user", "assistant"]
+    
+    # Split by lines and filter
+    lines = text.split('\n')
+    cleaned_lines = []
+    
+    for line in lines:
+        stripped = line.strip()
+        # Skip lines that are just template markers
+        if stripped.lower() not in markers_to_remove:
+            cleaned_lines.append(line)
+    
+    # Join back and strip leading/trailing whitespace
+    cleaned = '\n'.join(cleaned_lines).strip()
+    
+    # Alternative approach: if there's an "assistant" marker, take everything after it
+    if "assistant" in text.lower():
+        parts = text.split("assistant", 1)
+        if len(parts) > 1:
+            cleaned = parts[1].strip()
+    
+    return cleaned
+
+
+# Bbox parsing pattern: ![image](image_N.png) x1,y1,x2,y2
+BBOX_PATTERN = r'!\[image\]\((image_\d+\.png)\)\s+(\d+),(\d+),(\d+),(\d+)'
+
+
+def parse_bbox_output(text):
+    """Parse bbox output and return cleaned text with list of detections."""
+    detections = []
+    for match in re.finditer(BBOX_PATTERN, text):
+        image_ref, x1, y1, x2, y2 = match.groups()
+        detections.append({
+            "ref": image_ref,
+            "coords": (int(x1), int(y1), int(x2), int(y2))
+        })
+    # Clean text: remove coordinates, keep markdown image refs
+    cleaned = re.sub(BBOX_PATTERN, r'![image](\1)', text)
+    return cleaned, detections
+
+
+def crop_from_bbox(source_image, bbox, padding=5):
+    """Crop region from image based on normalized [0,1000] coords."""
+    w, h = source_image.size
+    x1, y1, x2, y2 = bbox["coords"]
+
+    # Convert to pixel coordinates (coords are normalized to 0-1000)
+    px1 = int(x1 * w / 1000)
+    py1 = int(y1 * h / 1000)
+    px2 = int(x2 * w / 1000)
+    py2 = int(y2 * h / 1000)
+
+    # Add padding, clamp to bounds
+    px1, py1 = max(0, px1 - padding), max(0, py1 - padding)
+    px2, py2 = min(w, px2 + padding), min(h, py2 + padding)
+
+    return source_image.crop((px1, py1, px2, py2))
+
+
+def image_to_data_uri(image):
+    """Convert PIL image to base64 data URI for markdown embedding."""
+    buffer = BytesIO()
+    image.save(buffer, format="PNG")
+    b64 = base64.b64encode(buffer.getvalue()).decode()
+    return f"data:image/png;base64,{b64}"
+
+
+def render_bbox_with_crops(raw_output, source_image):
+    """Replace markdown image placeholders with actual cropped images."""
+    cleaned, detections = parse_bbox_output(raw_output)
+
+    for bbox in detections:
+        try:
+            cropped = crop_from_bbox(source_image, bbox)
+            data_uri = image_to_data_uri(cropped)
+            # Replace ![image](image_N.png) with ![Cropped](data:...)
+            cleaned = cleaned.replace(
+                f"![image]({bbox['ref']})",
+                f"![Cropped region]({data_uri})"
+            )
+        except Exception as e:
+            print(f"Error cropping bbox {bbox}: {e}")
+            # Keep original reference if cropping fails
+            continue
+
+    return cleaned
+
+
+@spaces.GPU
+def extract_text_from_image(image, model_name, temperature=0.2, stream=False):
+    """Extract text from image using LightOnOCR model."""
+    # Get model and processor from cache or load
+    model, processor = model_manager.get_model(model_name)
+
+    # Prepare the chat format
+    chat = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image", "url": image},
+            ],
+        }
+    ]
+
+    # Apply chat template and tokenize
+    inputs = processor.apply_chat_template(
+        chat,
+        add_generation_prompt=True,
+        tokenize=True,
+        return_dict=True,
+        return_tensors="pt"
+    )
+
+    # Move inputs to device AND convert to the correct dtype
+    inputs = {
+        k: v.to(device=device, dtype=dtype) if isinstance(v, torch.Tensor) and v.dtype in [torch.float32, torch.float16, torch.bfloat16]
+        else v.to(device) if isinstance(v, torch.Tensor)
+        else v
+        for k, v in inputs.items()
+    }
+
+    generation_kwargs = dict(
+        **inputs,
+        max_new_tokens=2048,
+        temperature=temperature if temperature > 0 else 0.0,
+        use_cache=True,
+        do_sample=temperature > 0,
+    )
+
+    if stream:
+        # Setup streamer for streaming generation
+        streamer = TextIteratorStreamer(
+            processor.tokenizer,
+            skip_prompt=True,
+            skip_special_tokens=True
+        )
+        generation_kwargs["streamer"] = streamer
+
+        # Run generation in a separate thread
+        thread = threading.Thread(target=model.generate, kwargs=generation_kwargs)
+        thread.start()
+
+        # Yield chunks as they arrive
+        full_text = ""
+        for new_text in streamer:
+            full_text += new_text
+            # Clean the accumulated text
+            cleaned_text = clean_output_text(full_text)
+            yield cleaned_text
+
+        thread.join()
+    else:
+        # Non-streaming generation
+        with torch.no_grad():
+            outputs = model.generate(**generation_kwargs)
+
+        # Decode the output
+        output_text = processor.decode(outputs[0], skip_special_tokens=True)
+
+        # Clean the output
+        cleaned_text = clean_output_text(output_text)
+
+        yield cleaned_text
+
+
+def process_input(file_input, model_name, temperature, page_num, enable_streaming):
+    """Process uploaded file (image or PDF) and extract text with optional streaming."""
+    if file_input is None:
+        yield "Please upload an image or PDF first.", "", "", None, gr.update()
+        return
+
+    image_to_process = None
+    page_info = ""
+
+    file_path = file_input if isinstance(file_input, str) else file_input.name
+
+    # Handle PDF files
+    if file_path.lower().endswith('.pdf'):
+        try:
+            image_to_process, total_pages, actual_page = process_pdf(file_path, int(page_num))
+            page_info = f"Processing page {actual_page} of {total_pages}"
+        except Exception as e:
+            yield f"Error processing PDF: {str(e)}", "", "", None, gr.update()
+            return
+    # Handle image files
+    else:
+        try:
+            image_to_process = Image.open(file_path)
+            page_info = "Processing image"
+        except Exception as e:
+            yield f"Error opening image: {str(e)}", "", "", None, gr.update()
+            return
+
+    # Check if model has bbox capability
+    model_info = MODEL_REGISTRY.get(model_name, {})
+    has_bbox = model_info.get("has_bbox", False)
+
+    try:
+        # Extract text using LightOnOCR with optional streaming
+        for extracted_text in extract_text_from_image(image_to_process, model_name, temperature, stream=enable_streaming):
+            # For bbox models, render cropped images inline
+            if has_bbox:
+                rendered_text = render_bbox_with_crops(extracted_text, image_to_process)
+            else:
+                rendered_text = extracted_text
+            yield rendered_text, extracted_text, page_info, image_to_process, gr.update()
+
+    except Exception as e:
+        error_msg = f"Error during text extraction: {str(e)}"
+        yield error_msg, error_msg, page_info, image_to_process, gr.update()
+
+
+def update_slider(file_input):
+    """Update page slider based on PDF page count."""
+    if file_input is None:
+        return gr.update(maximum=20, value=1)
+    
+    file_path = file_input if isinstance(file_input, str) else file_input.name
+    
+    if file_path.lower().endswith('.pdf'):
+        try:
+            pdf = pdfium.PdfDocument(file_path)
+            total_pages = len(pdf)
+            pdf.close()
+            return gr.update(maximum=total_pages, value=1)
+        except:
+            return gr.update(maximum=20, value=1)
+    else:
+        return gr.update(maximum=1, value=1)
+
+
+# Helper function to get model info text
+def get_model_info_text(model_name):
+    """Return formatted model info string."""
+    info = MODEL_REGISTRY.get(model_name, {})
+    has_bbox = "Yes - will show cropped regions inline" if info.get("has_bbox", False) else "No"
+    return f"**Description:** {info.get('description', 'N/A')}\n**Bounding Box Detection:** {has_bbox}"
+
+
+# Create Gradio interface
+with gr.Blocks(title="LightOnOCR-2 Multi-Model OCR") as demo:
+    gr.Markdown(f"""
+# LightOnOCR-2 Multi-Model OCR
+
+**How to use:**
+1. Select a model (OCR models for text extraction, Bbox models for region detection)
+2. Upload an image or PDF
+3. For PDFs: select which page to extract
+4. Click "Extract Text"
+
+**Note:** Bbox models output cropped regions inline. Check raw output for coordinates.
+
+**Device:** {device.upper()} | **Attention:** {attn_implementation}
+""")
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            model_selector = gr.Dropdown(
+                choices=list(MODEL_REGISTRY.keys()),
+                value=DEFAULT_MODEL,
+                label="Model",
+                info="Select OCR model variant"
+            )
+            model_info = gr.Markdown(
+                value=get_model_info_text(DEFAULT_MODEL),
+                label="Model Info"
+            )
+            file_input = gr.File(
+                label="Upload Image or PDF",
+                file_types=[".pdf", ".png", ".jpg", ".jpeg"],
+                type="filepath"
+            )
+            rendered_image = gr.Image(
+                label="Preview",
+                type="pil",
+                height=400,
+                interactive=False
+            )
+            num_pages = gr.Slider(
+                minimum=1,
+                maximum=20,
+                value=1,
+                step=1,
+                label="PDF: Page Number",
+                info="Select which page to extract"
+            )
+            page_info = gr.Textbox(
+                label="Processing Info",
+                value="",
+                interactive=False
+            )
+            temperature = gr.Slider(
+                minimum=0.0,
+                maximum=1.0,
+                value=0.2,
+                step=0.05,
+                label="Temperature",
+                info="0.0 = deterministic, Higher = more varied"
+            )
+            enable_streaming = gr.Checkbox(
+                label="Enable Streaming",
+                value=True,
+                info="Show text progressively as it's generated"
+            )
+            submit_btn = gr.Button("Extract Text", variant="primary")
+            clear_btn = gr.Button("Clear", variant="secondary")
+        
+        with gr.Column(scale=2):
+            output_text = gr.Markdown(
+                label="📄 Extracted Text (Rendered)",
+                value="*Extracted text will appear here...*"
+            )
+    
+    with gr.Row():
+        with gr.Column():
+            raw_output = gr.Textbox(
+                label="Raw Markdown Output",
+                placeholder="Raw text will appear here...",
+                lines=20,
+                max_lines=30
+            )
+    
+    # Event handlers
+    submit_btn.click(
+        fn=process_input,
+        inputs=[file_input, model_selector, temperature, num_pages, enable_streaming],
+        outputs=[output_text, raw_output, page_info, rendered_image, num_pages]
+    )
+
+    file_input.change(
+        fn=update_slider,
+        inputs=[file_input],
+        outputs=[num_pages]
+    )
+
+    model_selector.change(
+        fn=get_model_info_text,
+        inputs=[model_selector],
+        outputs=[model_info]
+    )
+
+    clear_btn.click(
+        fn=lambda: (None, DEFAULT_MODEL, get_model_info_text(DEFAULT_MODEL), "*Extracted text will appear here...*", "", "", None, 1),
+        outputs=[file_input, model_selector, model_info, output_text, raw_output, page_info, rendered_image, num_pages]
+    )
+
+
+if __name__ == "__main__":
+    demo.launch(theme=gr.themes.Soft())