Create LightOCR_ClinicalNER_Test

LightOCR_ClinicalNER_Test

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+#!/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()