Create app.py

app.py

@@ -0,0 +1,921 @@
+"""
+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:
+            elif 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('<p class="powered-by">@Powered by VIDraft & Huggingface gradio</p>')
+        
+        gr.HTML(
+            """
+            <div class="component-description">
+              <p style="font-size:16px;margin:0;">Build sophisticated workflows visually • Import/Export JSON • Generate interactive UI for end-users</p>
+            </div>
+            """
+        )
+        
+        # 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)