Update app.py

app.py

@@ -1,70 +1,285 @@
+#!/usr/bin/env python3
+import subprocess
+import sys
+
+# Install flash-attn for GPU only
+import torch
+if torch.cuda.is_available():
+    print("CUDA detected - installing flash-attn for optimal GPU performance...")
+    subprocess.run(
+        "pip install flash-attn --no-build-isolation",
+        env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
+        shell=True,
+    )
+
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
-    """
-    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-    """
-    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
-
-    messages = [{"role": "system", "content": system_message}]
-
-    messages.extend(history)
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-chatbot = gr.ChatInterface(
-    respond,
-    type="messages",
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+import spaces
+from PIL import Image
+from io import BytesIO
+import pypdfium2 as pdfium
+from transformers import (
+    LightOnOCRForConditionalGeneration,
+    LightOnOCRProcessor,
+)
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Choose best attention implementation based on device
+if device == "cuda":
+    attn_implementation = "flash_attention_2"  # Best for GPU
+    dtype = torch.bfloat16
+    print("Using flash_attention_2 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
+
 
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
+@spaces.GPU
+def extract_text_from_image(image, temperature=0.2):
+    """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
+    inputs = {k: v.to(device) if isinstance(v, torch.Tensor) else v for k, v in inputs.items()}
+    
+    # Generate text with appropriate settings
+    with torch.no_grad():  # Disable gradients for inference
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=2048,
+            temperature=temperature if temperature > 0 else 0.0,
+            use_cache=True,
+            do_sample=temperature > 0,
+        )
+    
+    # Decode the output
+    output_text = processor.decode(outputs[0], skip_special_tokens=True)
+    
+    return output_text
+
+
+def process_input(file_input, temperature, page_num):
+    """Process uploaded file (image or PDF) and extract text."""
+    if file_input is None:
+        return "Please upload an image or PDF first.", "", "", None, gr.update()
+    
+    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:
+            return f"Error processing PDF: {str(e)}", "", "", None, gr.update()
+    # Handle image files
+    else:
+        try:
+            image_to_process = Image.open(file_path)
+            page_info = "Processing image"
+        except Exception as e:
+            return f"Error opening image: {str(e)}", "", "", None, gr.update()
+    
+    try:
+        # Extract text using LightOnOCR
+        extracted_text = extract_text_from_image(image_to_process, temperature)
+        
+        return extracted_text, extracted_text, page_info, image_to_process, gr.update()
+        
+    except Exception as e:
+        error_msg = f"Error during text extraction: {str(e)}"
+        return 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 (LightOnOCR + Zero GPU)
+
+**💡 How to use:**
+1. Upload an image or PDF
+2. For PDFs: select which page to extract (1-20)
+3. Adjust temperature if needed (0.0 for deterministic, higher for more varied output)
+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"
+            )
+            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,
+                show_copy_button=True
+            )
+    
+    # Event handlers
+    submit_btn.click(
+        fn=process_input,
+        inputs=[file_input, temperature, num_pages],
+        outputs=[output_text, 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()
+```
+
+**Key improvements:**
+
+1. **Conditional flash-attn installation**: Only installs flash-attn when CUDA is available
+2. **Automatic attention selection**:
+   - **GPU**: `flash_attention_2` (fastest and most memory-efficient)
+   - **CPU**: `eager` (standard PyTorch attention, best for CPU)
+3. **Appropriate dtype**: `bfloat16` for GPU, `float32` for CPU
+4. **Performance optimizations**:
+   - Added `torch.no_grad()` context for inference
+   - Proper temperature handling (0.0 for greedy decoding)
+5. **UI feedback**: Shows device and attention implementation in the interface
+
+**Requirements.txt:**
+```
+gradio
+torch
+transformers>=4.37.0
+pypdfium2
+pillow
+spaces