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| import gradio as gr | |
| from transformers import AutoProcessor, AutoModelForImageTextToText | |
| from PIL import Image | |
| import base64 | |
| from io import BytesIO | |
| import os | |
| # ----------------------------- | |
| # Load model and processor once | |
| # ----------------------------- | |
| processor = AutoProcessor.from_pretrained("ds4sd/SmolDocling-256M-preview") | |
| model = AutoModelForImageTextToText.from_pretrained("ds4sd/SmolDocling-256M-preview") | |
| # ----------------------------- | |
| # Image conversion helper | |
| # ----------------------------- | |
| def convert_to_pil(image_input): | |
| """ | |
| Convert base64, dict, or file path to PIL.Image. | |
| Handles: | |
| - "data:image/png;base64,...." | |
| - plain base64 | |
| - {"type": "image", "data": "..."} | |
| - file path | |
| """ | |
| # Case 1: dict input (Perplexity/Claude format) | |
| if isinstance(image_input, dict) and "data" in image_input: | |
| image_input = image_input["data"] | |
| # Case 2: base64 string with prefix | |
| if isinstance(image_input, str) and image_input.startswith("data:image"): | |
| base64_str = image_input.split(",", 1)[1] | |
| image_data = base64.b64decode(base64_str) | |
| return Image.open(BytesIO(image_data)) | |
| # Case 3: plain base64 string (no prefix) | |
| if isinstance(image_input, str) and "," in image_input and len(image_input) > 100: | |
| try: | |
| image_data = base64.b64decode(image_input) | |
| return Image.open(BytesIO(image_data)) | |
| except Exception: | |
| pass | |
| # Case 4: local file path | |
| if isinstance(image_input, str) and os.path.exists(image_input): | |
| return Image.open(image_input) | |
| raise ValueError("Could not convert image input to PIL.Image") | |
| # ----------------------------- | |
| # Core function | |
| # ----------------------------- | |
| def smoldocling_readimage(image: Image.Image, prompt_text: str) -> str: | |
| """ | |
| Run SmolDocling image-to-text conversion. | |
| """ | |
| messages = [ | |
| {"role": "user", "content": [{"type": "image"}, {"type": "text", "text": prompt_text}]} | |
| ] | |
| prompt = processor.apply_chat_template(messages, add_generation_prompt=True) | |
| inputs = processor(text=prompt, images=[image], return_tensors="pt") | |
| outputs = model.generate(**inputs, max_new_tokens=1024) | |
| prompt_length = inputs.input_ids.shape[1] | |
| generated = outputs[:, prompt_length:] | |
| result = processor.batch_decode(generated, skip_special_tokens=False)[0] | |
| return result.replace("<end_of_utterance>", "").strip() | |
| # ----------------------------- | |
| # Wrapper for MCP schema compatibility | |
| # ----------------------------- | |
| def smoldocling_entry(image, prompt_text: str) -> str: | |
| """ | |
| Entry point for MCP tool. | |
| Accepts any of: | |
| - base64 string | |
| - dict {"type": "image", "data": "data:image/png;base64,..."} | |
| - file path | |
| """ | |
| pil_image = convert_to_pil(image) | |
| return smoldocling_readimage(pil_image, prompt_text) | |
| # ----------------------------- | |
| # Gradio MCP App (Headless) | |
| # ----------------------------- | |
| with gr.Blocks() as demo: | |
| gr.Markdown( | |
| """ | |
| ### 📄 SmolDocling MCP Tool | |
| This is a **headless MCP tool** for document image conversion. | |
| It supports input as: | |
| - Base64-encoded images | |
| - Perplexity/Claude `{"type": "image", "data": "..."}` objects | |
| - Local file paths (for testing) | |
| """ | |
| ) | |
| # Expose MCP tool | |
| gr.api(smoldocling_entry) | |
| # Launch MCP server mode | |
| _, url, _ = demo.launch(mcp_server=True) | |
| print(f"✅ MCP Server running at: {url}") |