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Running on Zero
Running on Zero
| """Qwen2.5-VL Smart Document, Blueprint & Invoice OCR Studio on ZeroGPU.""" | |
| import os | |
| os.environ.setdefault("PYTORCH_ALLOC_CONF", "backend:cudaMallocAsync") | |
| os.environ.setdefault("PYTORCH_CUDA_ALLOC_CONF", "backend:cudaMallocAsync") | |
| import spaces # Must be imported before torch | |
| import gradio as gr | |
| import PIL.Image | |
| import torch | |
| from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration | |
| MODEL_ID = "Qwen/Qwen2.5-VL-3B-Instruct" | |
| EXTRACTION_PRESETS = { | |
| "Full Clean Markdown + Tables (Default)": ( | |
| "Extract all text, headings, and tables from this image into clean, well-formatted GitHub Flavored Markdown. " | |
| "Preserve exact numbers, part numbers, prices, and table columns." | |
| ), | |
| "Structured Invoice / Receipt JSON": ( | |
| "Extract all fields from this invoice or receipt as valid JSON with keys: vendor, date, invoice_number, " | |
| "line_items (description, quantity, unit_price, total), subtotal, tax, and total_amount." | |
| ), | |
| "Electrical Panel / Blueprint / Spec Analysis": ( | |
| "Analyze this electrical diagram, panel schedule, or technical blueprint. List all visible circuits, " | |
| "breaker ratings, wire gauges, load calculations, labels, and technical notes in structured Markdown." | |
| ), | |
| "Custom Question / Prompt": "", | |
| } | |
| print(f"[vision-ocr-studio] Preloading {MODEL_ID} at module scope for ZeroGPU...", flush=True) | |
| model = Qwen2_5_VLForConditionalGeneration.from_pretrained( | |
| MODEL_ID, | |
| torch_dtype=torch.bfloat16, | |
| ) | |
| model.to("cuda") | |
| model.eval() | |
| processor = AutoProcessor.from_pretrained(MODEL_ID) | |
| print("[vision-ocr-studio] Qwen2.5-VL OCR engine ready on ZeroGPU!", flush=True) | |
| def analyze_document( | |
| image_path: str | None, | |
| preset: str = "Full Clean Markdown + Tables (Default)", | |
| custom_instruction: str = "", | |
| max_tokens: int = 1024, | |
| ) -> str: | |
| """Run vision-language OCR or structured extraction on an uploaded document/image.""" | |
| if not image_path: | |
| raise gr.Error("Please upload a document, blueprint, invoice, or screenshot image first.") | |
| instruction = (custom_instruction or "").strip() or EXTRACTION_PRESETS.get( | |
| preset, EXTRACTION_PRESETS["Full Clean Markdown + Tables (Default)"] | |
| ) | |
| img = PIL.Image.open(image_path).convert("RGB") | |
| if max(img.size) > 1600: | |
| scale = 1600.0 / max(img.size) | |
| img = img.resize((int(img.width * scale), int(img.height * scale)), PIL.Image.Resampling.LANCZOS) | |
| messages = [ | |
| { | |
| "role": "user", | |
| "content": [ | |
| {"type": "image", "image": img}, | |
| {"type": "text", "text": instruction}, | |
| ], | |
| } | |
| ] | |
| text_prompt = processor.apply_chat_template( | |
| messages, tokenize=False, add_generation_prompt=True | |
| ) | |
| inputs = processor( | |
| text=[text_prompt], | |
| images=[img], | |
| padding=True, | |
| return_tensors="pt", | |
| ).to("cuda") | |
| with torch.no_grad(): | |
| generated_ids = model.generate(**inputs, max_new_tokens=int(max_tokens)) | |
| trimmed_ids = [ | |
| out_ids[len(in_ids):] | |
| for in_ids, out_ids in zip(inputs.input_ids, generated_ids) | |
| ] | |
| output_text = processor.batch_decode( | |
| trimmed_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False | |
| )[0] | |
| return output_text.strip() | |
| with gr.Blocks(title="Smart Document, Blueprint & Invoice OCR Studio", fill_width=True) as demo: | |
| gr.Markdown( | |
| "# 📄 Smart Document, Blueprint & Invoice OCR Studio\n" | |
| "Powered by **[Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct)** on **ZeroGPU (`zero-a10g`)** at **$0/hour**—extract clean **Markdown, JSON, tables, invoices, or electrical specs** from any image." | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| img_in = gr.Image( | |
| label="Upload Document, Invoice, Electrical Schedule, Blueprint, or Screenshot", | |
| type="filepath", | |
| ) | |
| preset_dd = gr.Dropdown( | |
| choices=list(EXTRACTION_PRESETS), | |
| value="Full Clean Markdown + Tables (Default)", | |
| label="Extraction Mode", | |
| ) | |
| custom_box = gr.Textbox( | |
| label="Custom Instruction / Question (Optional — overrides preset when filled)", | |
| lines=3, | |
| placeholder="e.g., Extract all circuit breaker amps and wire sizes into a JSON array...", | |
| ) | |
| max_tok = gr.Slider(256, 2048, value=1024, step=128, label="Max Output Tokens") | |
| extract_btn = gr.Button("📄 Extract & Analyze Document (ZeroGPU)", variant="primary") | |
| with gr.Column(scale=1): | |
| md_out = gr.Textbox( | |
| label="Extracted Output (Markdown / JSON)", | |
| lines=18, | |
| buttons=["copy"], | |
| ) | |
| extract_btn.click( | |
| fn=analyze_document, | |
| inputs=[img_in, preset_dd, custom_box, max_tok], | |
| outputs=md_out, | |
| api_name="extract_document", | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch(theme=gr.themes.Ocean(), mcp_server=True) | |