""" MOUSE Workflow - Visual Workflow Builder with UI Execution @Powered by VIDraft ✓ Visual workflow designer with drag-and-drop ✓ Import/Export JSON with copy-paste support ✓ Auto-generate UI from workflow for end-user execution """ import os, json, typing, tempfile, traceback import gradio as gr from gradio_workflowbuilder import WorkflowBuilder # Optional imports for LLM APIs try: from openai import OpenAI OPENAI_AVAILABLE = True except ImportError: OPENAI_AVAILABLE = False print("OpenAI library not available. Install with: pip install openai") # Anthropic 관련 코드 주석 처리 # try: # import anthropic # ANTHROPIC_AVAILABLE = True # except ImportError: # ANTHROPIC_AVAILABLE = False # print("Anthropic library not available. Install with: pip install anthropic") ANTHROPIC_AVAILABLE = False try: import requests REQUESTS_AVAILABLE = True except ImportError: REQUESTS_AVAILABLE = False print("Requests library not available. Install with: pip install requests") # ------------------------------------------------------------------- # 🛠️ 헬퍼 함수들 # ------------------------------------------------------------------- def export_pretty(data: typing.Dict[str, typing.Any]) -> str: return json.dumps(data, indent=2, ensure_ascii=False) if data else "No workflow to export" def export_file(data: typing.Dict[str, typing.Any]) -> typing.Optional[str]: """워크플로우를 JSON 파일로 내보내기""" if not data: return None fd, path = tempfile.mkstemp(suffix=".json", prefix="workflow_") try: with os.fdopen(fd, "w", encoding="utf-8") as f: json.dump(data, f, ensure_ascii=False, indent=2) return path except Exception as e: print(f"Error exporting file: {e}") return None def load_json_from_text_or_file(json_text: str, file_obj) -> typing.Tuple[typing.Dict[str, typing.Any], str]: """텍스트 또는 파일에서 JSON 로드""" # 파일이 있으면 파일 우선 if file_obj is not None: try: with open(file_obj.name, "r", encoding="utf-8") as f: json_text = f.read() except Exception as e: return None, f"❌ Error reading file: {str(e)}" # JSON 텍스트가 없거나 비어있으면 if not json_text or json_text.strip() == "": return None, "No JSON data provided" try: # JSON 파싱 data = json.loads(json_text.strip()) # 데이터 검증 if not isinstance(data, dict): return None, "Invalid format: not a dictionary" # 필수 필드 확인 if 'nodes' not in data: data['nodes'] = [] if 'edges' not in data: data['edges'] = [] nodes_count = len(data.get('nodes', [])) edges_count = len(data.get('edges', [])) return data, f"✅ Loaded: {nodes_count} nodes, {edges_count} edges" except json.JSONDecodeError as e: return None, f"❌ JSON parsing error: {str(e)}" except Exception as e: return None, f"❌ Error: {str(e)}" def create_sample_workflow(example_type="basic"): """샘플 워크플로우 생성""" if example_type == "basic": # 기본 예제: 간단한 Q&A return { "nodes": [ { "id": "input_1", "type": "ChatInput", "position": {"x": 100, "y": 200}, "data": { "label": "User Question", "template": { "input_value": {"value": "What is the capital of Korea?"} } } }, { "id": "llm_1", "type": "llmNode", "position": {"x": 400, "y": 200}, "data": { "label": "AI Processing", "template": { "provider": {"value": "OpenAI"}, "model": {"value": "gpt-4.1-mini"}, "temperature": {"value": 0.7}, "system_prompt": {"value": "You are a helpful assistant."} } } }, { "id": "output_1", "type": "ChatOutput", "position": {"x": 700, "y": 200}, "data": {"label": "Answer"} } ], "edges": [ {"id": "e1", "source": "input_1", "target": "llm_1"}, {"id": "e2", "source": "llm_1", "target": "output_1"} ] } elif example_type == "vidraft": # VIDraft 예제 return { "nodes": [ { "id": "input_1", "type": "ChatInput", "position": {"x": 100, "y": 200}, "data": { "label": "User Input", "template": { "input_value": {"value": "AI와 머신러닝의 차이점을 설명해주세요."} } } }, { "id": "llm_1", "type": "llmNode", "position": {"x": 400, "y": 200}, "data": { "label": "VIDraft AI (Gemma)", "template": { "provider": {"value": "VIDraft"}, "model": {"value": "Gemma-3-r1984-27B"}, "temperature": {"value": 0.8}, "system_prompt": {"value": "당신은 전문적이고 친절한 AI 교육자입니다. 복잡한 개념을 쉽게 설명해주세요."} } } }, { "id": "output_1", "type": "ChatOutput", "position": {"x": 700, "y": 200}, "data": {"label": "AI Explanation"} } ], "edges": [ {"id": "e1", "source": "input_1", "target": "llm_1"}, {"id": "e2", "source": "llm_1", "target": "output_1"} ] } elif example_type == "multi_input": # 다중 입력 예제 return { "nodes": [ { "id": "name_input", "type": "textInput", "position": {"x": 100, "y": 100}, "data": { "label": "Your Name", "template": { "input_value": {"value": "John"} } } }, { "id": "topic_input", "type": "textInput", "position": {"x": 100, "y": 250}, "data": { "label": "Topic", "template": { "input_value": {"value": "Python programming"} } } }, { "id": "level_input", "type": "textInput", "position": {"x": 100, "y": 400}, "data": { "label": "Skill Level", "template": { "input_value": {"value": "beginner"} } } }, { "id": "combiner", "type": "textNode", "position": {"x": 350, "y": 250}, "data": { "label": "Combine Inputs", "template": { "text": {"value": "Create a personalized learning plan"} } } }, { "id": "llm_1", "type": "llmNode", "position": {"x": 600, "y": 250}, "data": { "label": "Generate Learning Plan", "template": { "provider": {"value": "OpenAI"}, "model": {"value": "gpt-4.1-mini"}, "temperature": {"value": 0.7}, "system_prompt": {"value": "You are an expert educational consultant. Create personalized learning plans based on the user's name, topic of interest, and skill level."} } } }, { "id": "output_1", "type": "ChatOutput", "position": {"x": 900, "y": 250}, "data": {"label": "Your Learning Plan"} } ], "edges": [ {"id": "e1", "source": "name_input", "target": "combiner"}, {"id": "e2", "source": "topic_input", "target": "combiner"}, {"id": "e3", "source": "level_input", "target": "combiner"}, {"id": "e4", "source": "combiner", "target": "llm_1"}, {"id": "e5", "source": "llm_1", "target": "output_1"} ] } elif example_type == "chain": # 체인 처리 예제 return { "nodes": [ { "id": "input_1", "type": "ChatInput", "position": {"x": 50, "y": 200}, "data": { "label": "Original Text", "template": { "input_value": {"value": "The quick brown fox jumps over the lazy dog."} } } }, { "id": "translator", "type": "llmNode", "position": {"x": 300, "y": 200}, "data": { "label": "Translate to Korean", "template": { "provider": {"value": "VIDraft"}, "model": {"value": "Gemma-3-r1984-27B"}, "temperature": {"value": 0.3}, "system_prompt": {"value": "You are a professional translator. Translate the given English text to Korean accurately."} } } }, { "id": "analyzer", "type": "llmNode", "position": {"x": 600, "y": 200}, "data": { "label": "Analyze Translation", "template": { "provider": {"value": "OpenAI"}, "model": {"value": "gpt-4.1-mini"}, "temperature": {"value": 0.5}, "system_prompt": {"value": "You are a linguistic expert. Analyze the Korean translation and explain its nuances and cultural context."} } } }, { "id": "output_translation", "type": "ChatOutput", "position": {"x": 450, "y": 350}, "data": {"label": "Korean Translation"} }, { "id": "output_analysis", "type": "ChatOutput", "position": {"x": 900, "y": 200}, "data": {"label": "Translation Analysis"} } ], "edges": [ {"id": "e1", "source": "input_1", "target": "translator"}, {"id": "e2", "source": "translator", "target": "analyzer"}, {"id": "e3", "source": "translator", "target": "output_translation"}, {"id": "e4", "source": "analyzer", "target": "output_analysis"} ] } # 기본값은 basic return create_sample_workflow("basic") # UI 실행을 위한 실제 워크플로우 실행 함수 def execute_workflow_simple(workflow_data: dict, input_values: dict) -> dict: """워크플로우 실제 실행""" import traceback # API 키 확인 vidraft_token = os.getenv("FRIENDLI_TOKEN") # VIDraft/Friendli token openai_key = os.getenv("OPENAI_API_KEY") # anthropic_key = os.getenv("ANTHROPIC_API_KEY") # 주석 처리 # OpenAI 라이브러리 확인 try: from openai import OpenAI openai_available = True except ImportError: openai_available = False print("OpenAI library not available") # Anthropic 라이브러리 확인 - 주석 처리 # try: # import anthropic # anthropic_available = True # except ImportError: # anthropic_available = False # print("Anthropic library not available") anthropic_available = False results = {} nodes = workflow_data.get("nodes", []) edges = workflow_data.get("edges", []) # 노드를 순서대로 처리 for node in nodes: node_id = node.get("id") node_type = node.get("type", "") node_data = node.get("data", {}) try: if node_type in ["ChatInput", "textInput", "Input"]: # UI에서 제공된 입력값 사용 if node_id in input_values: results[node_id] = input_values[node_id] else: # 기본값 사용 template = node_data.get("template", {}) default_value = template.get("input_value", {}).get("value", "") results[node_id] = default_value elif node_type == "textNode": # 텍스트 노드는 연결된 모든 입력을 결합 template = node_data.get("template", {}) base_text = template.get("text", {}).get("value", "") # 연결된 입력들 수집 connected_inputs = [] for edge in edges: if edge.get("target") == node_id: source_id = edge.get("source") if source_id in results: connected_inputs.append(f"{source_id}: {results[source_id]}") # 결합된 텍스트 생성 if connected_inputs: combined_text = f"{base_text}\n\nInputs:\n" + "\n".join(connected_inputs) results[node_id] = combined_text else: results[node_id] = base_text elif node_type in ["llmNode", "OpenAIModel", "ChatModel"]: # LLM 노드 처리 template = node_data.get("template", {}) # 프로바이더 정보 추출 - VIDraft 또는 OpenAI만 허용 provider_info = template.get("provider", {}) provider = provider_info.get("value", "OpenAI") if isinstance(provider_info, dict) else "OpenAI" # provider가 VIDraft 또는 OpenAI가 아닌 경우 OpenAI로 기본 설정 if provider not in ["VIDraft", "OpenAI"]: provider = "OpenAI" # 모델 정보 추출 if provider == "OpenAI": # OpenAI는 gpt-4.1-mini로 고정 model = "gpt-4.1-mini" elif provider == "VIDraft": # VIDraft는 Gemma-3-r1984-27B로 고정 model = "Gemma-3-r1984-27B" else: model = "gpt-4.1-mini" # 기본값 # 온도 정보 추출 temp_info = template.get("temperature", {}) temperature = temp_info.get("value", 0.7) if isinstance(temp_info, dict) else 0.7 # 시스템 프롬프트 추출 prompt_info = template.get("system_prompt", {}) system_prompt = prompt_info.get("value", "") if isinstance(prompt_info, dict) else "" # 입력 텍스트 찾기 input_text = "" for edge in edges: if edge.get("target") == node_id: source_id = edge.get("source") if source_id in results: input_text = results[source_id] break # 실제 API 호출 if provider == "OpenAI" and openai_key and openai_available: try: client = OpenAI(api_key=openai_key) messages = [] if system_prompt: messages.append({"role": "system", "content": system_prompt}) messages.append({"role": "user", "content": input_text}) response = client.chat.completions.create( model="gpt-4.1-mini", # 고정된 모델명 messages=messages, temperature=temperature, max_tokens=1000 ) results[node_id] = response.choices[0].message.content except Exception as e: results[node_id] = f"[OpenAI Error: {str(e)}]" # Anthropic 관련 코드 주석 처리 # elif provider == "Anthropic" and anthropic_key and anthropic_available: # try: # client = anthropic.Anthropic(api_key=anthropic_key) # # message = client.messages.create( # model="claude-3-haiku-20240307", # max_tokens=1000, # temperature=temperature, # system=system_prompt if system_prompt else None, # messages=[{"role": "user", "content": input_text}] # ) # # results[node_id] = message.content[0].text # # except Exception as e: # results[node_id] = f"[Anthropic Error: {str(e)}]" elif provider == "VIDraft" and vidraft_token: try: import requests headers = { "Authorization": f"Bearer {vidraft_token}", "Content-Type": "application/json" } # 메시지 구성 messages = [] if system_prompt: messages.append({"role": "system", "content": system_prompt}) messages.append({"role": "user", "content": input_text}) payload = { "model": "dep89a2fld32mcm", # VIDraft 모델 ID "messages": messages, "max_tokens": 16384, "temperature": temperature, "top_p": 0.8, "stream": False # 동기 실행을 위해 False로 설정 } # VIDraft API endpoint response = requests.post( "https://api.friendli.ai/dedicated/v1/chat/completions", headers=headers, json=payload, timeout=30 ) if response.status_code == 200: response_json = response.json() results[node_id] = response_json["choices"][0]["message"]["content"] else: results[node_id] = f"[VIDraft API Error: {response.status_code} - {response.text}]" except Exception as e: results[node_id] = f"[VIDraft Error: {str(e)}]" else: # API 키가 없는 경우 시뮬레이션 results[node_id] = f"[Simulated {provider} Response to: {input_text[:50]}...]" elif node_type in ["ChatOutput", "textOutput", "Output"]: # 출력 노드는 연결된 노드의 결과를 가져옴 for edge in edges: if edge.get("target") == node_id: source_id = edge.get("source") if source_id in results: results[node_id] = results[source_id] break except Exception as e: results[node_id] = f"[Node Error: {str(e)}]" print(f"Error processing node {node_id}: {traceback.format_exc()}") return results # ------------------------------------------------------------------- # 🎨 CSS # ------------------------------------------------------------------- CSS = """ .main-container{max-width:1600px;margin:0 auto;} .workflow-section{margin-bottom:2rem;min-height:500px;} .button-row{display:flex;gap:1rem;justify-content:center;margin:1rem 0;} .status-box{ padding:10px;border-radius:5px;margin-top:10px; background:#f0f9ff;border:1px solid #3b82f6;color:#1e40af; } .component-description{ padding:24px;background:linear-gradient(135deg,#f8fafc 0%,#e2e8f0 100%); border-left:4px solid #3b82f6;border-radius:12px; box-shadow:0 2px 8px rgba(0,0,0,.05);margin:16px 0; } .workflow-container{position:relative;} .ui-execution-section{ background:linear-gradient(135deg,#f0fdf4 0%,#dcfce7 100%); padding:24px;border-radius:12px;margin:24px 0; border:1px solid #86efac; } .powered-by{ text-align:center;color:#64748b;font-size:14px; margin-top:8px;font-style:italic; } .sample-buttons{ display:grid;grid-template-columns:1fr 1fr;gap:0.5rem; margin-top:0.5rem; } """ # ------------------------------------------------------------------- # 🖥️ Gradio 앱 # ------------------------------------------------------------------- with gr.Blocks(title="🐭 MOUSE Workflow", theme=gr.themes.Soft(), css=CSS) as demo: with gr.Column(elem_classes=["main-container"]): gr.Markdown("# 🐭 MOUSE Workflow") gr.Markdown("**Visual Workflow Builder with Interactive UI Execution**") gr.HTML('

@Powered by VIDraft & Huggingface gradio

') gr.HTML( """

Build sophisticated workflows visually • Import/Export JSON • Generate interactive UI for end-users

""" ) # API Status Display with gr.Accordion("🔌 API Status", open=False): gr.Markdown(f""" **Available APIs:** - FRIENDLI_TOKEN (VIDraft): {'✅ Connected' if os.getenv("FRIENDLI_TOKEN") else '❌ Not found'} - OPENAI_API_KEY: {'✅ Connected' if os.getenv("OPENAI_API_KEY") else '❌ Not found'} **Libraries:** - OpenAI: {'✅ Installed' if OPENAI_AVAILABLE else '❌ Not installed'} - Requests: {'✅ Installed' if REQUESTS_AVAILABLE else '❌ Not installed'} **Available Models:** - OpenAI: gpt-4.1-mini (fixed) - VIDraft: Gemma-3-r1984-27B (model ID: dep89a2fld32mcm) **Sample Workflows:** - Basic Q&A: Simple question-answer flow - VIDraft: Korean language example with Gemma model - Multi-Input: Combine multiple inputs for personalized output - Chain: Sequential processing with multiple outputs *Note: Without API keys, the UI will simulate AI responses.* """) # State for storing workflow data loaded_data = gr.State(None) trigger_update = gr.State(False) # ─── Dynamic Workflow Container ─── with gr.Column(elem_classes=["workflow-container"]): @gr.render(inputs=[loaded_data, trigger_update]) def render_workflow(data, trigger): """동적으로 WorkflowBuilder 렌더링""" workflow_value = data if data else {"nodes": [], "edges": []} return WorkflowBuilder( label="🎨 Visual Workflow Designer", info="Drag from sidebar → Connect nodes → Edit properties", value=workflow_value, elem_id="main_workflow" ) # ─── Import Section ─── with gr.Accordion("📥 Import Workflow", open=True): with gr.Row(): with gr.Column(scale=2): import_json_text = gr.Code( language="json", label="Paste JSON here", lines=8, value='{\n "nodes": [],\n "edges": []\n}' ) with gr.Column(scale=1): file_upload = gr.File( label="Or upload JSON file", file_types=[".json"], type="filepath" ) btn_load = gr.Button("📥 Load Workflow", variant="primary", size="lg") # Sample buttons gr.Markdown("**Sample Workflows:**") with gr.Row(): btn_sample_basic = gr.Button("🎯 Basic Q&A", variant="secondary", scale=1) btn_sample_vidraft = gr.Button("🤖 VIDraft", variant="secondary", scale=1) with gr.Row(): btn_sample_multi = gr.Button("📝 Multi-Input", variant="secondary", scale=1) btn_sample_chain = gr.Button("🔗 Chain", variant="secondary", scale=1) # Status status_text = gr.Textbox( label="Status", value="Ready", elem_classes=["status-box"], interactive=False ) # ─── Export Section ─── gr.Markdown("## 💾 Export") with gr.Row(): with gr.Column(scale=3): export_preview = gr.Code( language="json", label="Current Workflow JSON", lines=8 ) with gr.Column(scale=1): btn_preview = gr.Button("👁️ Preview JSON", size="lg") btn_download = gr.DownloadButton("💾 Download JSON", size="lg") # ─── UI Execution Section ─── with gr.Column(elem_classes=["ui-execution-section"]): gr.Markdown("## 🚀 UI Execution") gr.Markdown("Generate an interactive UI from your workflow for end-users") btn_execute_ui = gr.Button("▶️ Generate & Run UI", variant="primary", size="lg") # UI execution state ui_workflow_data = gr.State(None) # Dynamic UI container @gr.render(inputs=[ui_workflow_data]) def render_execution_ui(workflow_data): if not workflow_data or not workflow_data.get("nodes"): gr.Markdown("*Load a workflow first, then click 'Generate & Run UI'*") return gr.Markdown("### 📋 Generated UI") # Extract input and output nodes input_nodes = [] output_nodes = [] for node in workflow_data.get("nodes", []): node_type = node.get("type", "") if node_type in ["ChatInput", "textInput", "Input", "numberInput"]: input_nodes.append(node) elif node_type in ["ChatOutput", "textOutput", "Output"]: output_nodes.append(node) elif node_type == "textNode": # textNode는 중간 처리 노드로, UI에는 표시하지 않음 pass # Create input components input_components = {} if input_nodes: gr.Markdown("#### 📥 Inputs") for node in input_nodes: node_id = node.get("id") label = node.get("data", {}).get("label", node_id) node_type = node.get("type") # Get default value template = node.get("data", {}).get("template", {}) default_value = template.get("input_value", {}).get("value", "") if node_type == "numberInput": input_components[node_id] = gr.Number( label=label, value=float(default_value) if default_value else 0 ) else: input_components[node_id] = gr.Textbox( label=label, value=default_value, lines=2, placeholder="Enter your input..." ) # Execute button execute_btn = gr.Button("🎯 Execute", variant="primary") # Create output components output_components = {} if output_nodes: gr.Markdown("#### 📤 Outputs") for node in output_nodes: node_id = node.get("id") label = node.get("data", {}).get("label", node_id) output_components[node_id] = gr.Textbox( label=label, interactive=False, lines=3 ) # Execution log gr.Markdown("#### 📊 Execution Log") log_output = gr.Textbox( label="Log", interactive=False, lines=5 ) # Define execution handler def execute_ui_workflow(*input_values): # Create input dictionary inputs_dict = {} input_keys = list(input_components.keys()) for i, key in enumerate(input_keys): if i < len(input_values): inputs_dict[key] = input_values[i] # Check API status log = "=== Workflow Execution Started ===\n" log += f"Inputs provided: {len(inputs_dict)}\n" # API 상태 확인 vidraft_token = os.getenv("FRIENDLI_TOKEN") openai_key = os.getenv("OPENAI_API_KEY") log += "\nAPI Status:\n" log += f"- FRIENDLI_TOKEN (VIDraft): {'✅ Found' if vidraft_token else '❌ Not found'}\n" log += f"- OPENAI_API_KEY: {'✅ Found' if openai_key else '❌ Not found'}\n" if not vidraft_token and not openai_key: log += "\n⚠️ No API keys found. Results will be simulated.\n" log += "To get real AI responses, set API keys in environment variables.\n" log += "\n--- Processing Nodes ---\n" try: results = execute_workflow_simple(workflow_data, inputs_dict) # Prepare outputs output_values = [] for node_id in output_components.keys(): value = results.get(node_id, "No output") output_values.append(value) # Log 길이 제한 display_value = value[:100] + "..." if len(str(value)) > 100 else value log += f"\nOutput [{node_id}]: {display_value}\n" log += "\n=== Execution Completed Successfully! ===\n" output_values.append(log) return output_values except Exception as e: error_msg = f"❌ Error: {str(e)}" log += f"\n{error_msg}\n" log += "=== Execution Failed ===\n" return [error_msg] * len(output_components) + [log] # Connect execution all_inputs = list(input_components.values()) all_outputs = list(output_components.values()) + [log_output] execute_btn.click( fn=execute_ui_workflow, inputs=all_inputs, outputs=all_outputs ) # ─── Event Handlers ─── # Load workflow (from text or file) def load_workflow(json_text, file_obj): data, status = load_json_from_text_or_file(json_text, file_obj) if data: return data, status, json_text if not file_obj else export_pretty(data) else: return None, status, gr.update() btn_load.click( fn=load_workflow, inputs=[import_json_text, file_upload], outputs=[loaded_data, status_text, import_json_text] ).then( fn=lambda current_trigger: not current_trigger, inputs=trigger_update, outputs=trigger_update ) # Auto-load when file is uploaded file_upload.change( fn=load_workflow, inputs=[import_json_text, file_upload], outputs=[loaded_data, status_text, import_json_text] ).then( fn=lambda current_trigger: not current_trigger, inputs=trigger_update, outputs=trigger_update ) # Load samples btn_sample_basic.click( fn=lambda: (create_sample_workflow("basic"), "✅ Basic Q&A sample loaded", export_pretty(create_sample_workflow("basic"))), outputs=[loaded_data, status_text, import_json_text] ).then( fn=lambda current_trigger: not current_trigger, inputs=trigger_update, outputs=trigger_update ) btn_sample_vidraft.click( fn=lambda: (create_sample_workflow("vidraft"), "✅ VIDraft sample loaded", export_pretty(create_sample_workflow("vidraft"))), outputs=[loaded_data, status_text, import_json_text] ).then( fn=lambda current_trigger: not current_trigger, inputs=trigger_update, outputs=trigger_update ) btn_sample_multi.click( fn=lambda: (create_sample_workflow("multi_input"), "✅ Multi-input sample loaded", export_pretty(create_sample_workflow("multi_input"))), outputs=[loaded_data, status_text, import_json_text] ).then( fn=lambda current_trigger: not current_trigger, inputs=trigger_update, outputs=trigger_update ) btn_sample_chain.click( fn=lambda: (create_sample_workflow("chain"), "✅ Chain processing sample loaded", export_pretty(create_sample_workflow("chain"))), outputs=[loaded_data, status_text, import_json_text] ).then( fn=lambda current_trigger: not current_trigger, inputs=trigger_update, outputs=trigger_update ) # Preview current workflow btn_preview.click( fn=export_pretty, inputs=loaded_data, outputs=export_preview ) # Download workflow btn_download.click( fn=export_file, inputs=loaded_data ) # Generate UI execution btn_execute_ui.click( fn=lambda data: data, inputs=loaded_data, outputs=ui_workflow_data ) # Auto-update export preview when workflow changes loaded_data.change( fn=export_pretty, inputs=loaded_data, outputs=export_preview ) # ------------------------------------------------------------------- # 🚀 실행 # ------------------------------------------------------------------- if __name__ == "__main__": demo.launch(server_name="0.0.0.0", show_error=True)