LightOCR_ClinicalNER_Test

#!/usr/bin/env python3
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,
)

from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

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")

# Initialize the LightOnOCR model and processor
print(f"Loading model on {device} with {attn_implementation} attention...")
model = LightOnOCRForConditionalGeneration.from_pretrained(
    "lightonai/LightOnOCR-1B-1025",
    attn_implementation=attn_implementation,
    torch_dtype=dtype,
    trust_remote_code=True
).to(device).eval()

processor = LightOnOCRProcessor.from_pretrained(
    "lightonai/LightOnOCR-1B-1025",
    trust_remote_code=True
)
print("Model loaded successfully!")


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


@spaces.GPU
def extract_text_from_image(image, temperature=0.2, stream=False):
    """Extract text from image using LightOnOCR model."""
    # 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)

        #########  clinical NER  ##############

        tokenizer = AutoTokenizer.from_pretrained("samrawal/bert-base-uncased_clinical-ner")
        model = AutoModelForTokenClassification.from_pretrained("samrawal/bert-base-uncased_clinical-ner")
        ner = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")

        
        Clinical NER process
        entities = ner(cleaned_text)
        medications = []
        for ent in entities:
            if ent["entity_group"] == "treatment":
                word = ent["word"]
                if word.startswith("##") and medications:
                    medications[-1] += word[2:]
                else:
                    medications.append(word)
        medications_str = ", ".join(set(medications)) if medications else "None detected"
        
        yield cleaned_text
        yield medications_s
        
    


def process_input(file_input, 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
    
    try:
        # Extract text using LightOnOCR with optional streaming
        for extracted_text, medications in extract_text_from_image(image_to_process, temperature, stream=enable_streaming):
            yield extracted_text, medications, 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)


# Create Gradio interface
with gr.Blocks(title="📖 Image/PDF OCR with LightOnOCR", theme=gr.themes.Soft()) as demo:
    gr.Markdown(f"""
# 📖 Image/PDF to Text Extraction with LightOnOCR

**💡 How to use:**
1. Upload an image or PDF
2. For PDFs: select which page to extract (1-20)
3. Adjust temperature if needed
4. Click "Extract Text"

**Note:** The Markdown rendering for tables may not always be perfect. Check the raw output for complex tables!

**Model:** LightOnOCR-1B-1025 by LightOn AI  
**Device:** {device.upper()}  
**Attention:** {attn_implementation}
""")
    
    with gr.Row():
        with gr.Column(scale=1):
            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...*"
            )
            medications_output = gr.Textbox(
    label="💊 Extracted Medicines/Drugs",
    placeholder="Medicine/drug names will appear here...",
    lines=2,
    max_lines=5,
    interactive=False,
    show_copy_button=True
)
    
    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,
                show_copy_button=True
            )
    
    # Event handlers
    submit_btn.click(
    fn=process_input,
    inputs=[file_input, temperature, num_pages, enable_streaming],
    outputs=[output_text, medications_output, raw_output, page_info, rendered_image, num_pages]
)
    
    file_input.change(
        fn=update_slider,
        inputs=[file_input],
        outputs=[num_pages]
    )
    
    clear_btn.click(
        fn=lambda: (None, "*Extracted text will appear here...*", "", "", None, 1),
        outputs=[file_input, output_text, raw_output, page_info, rendered_image, num_pages]
    )


if __name__ == "__main__":
    demo.launch()