Update app-BACKUP-LAST.py

app-BACKUP-LAST.py

@@ -91,47 +91,237 @@ def load_json_from_text_or_file(json_text: str, file_obj) -> typing.Tuple[typing
     except Exception as e:
         return None, f"❌ Error: {str(e)}"
 
-def create_sample_workflow():
+def create_sample_workflow(example_type="basic"):
     """샘플 워크플로우 생성"""
-    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?"}
+    
+    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"}
                 }
-            },
-            {
-                "id": "llm_1",
-                "type": "llmNode",
-                "position": {"x": 400, "y": 200},
-                "data": {
-                    "label": "AI Processing",
-                    "template": {
-                        "provider": {"value": "OpenAI"},  # OpenAI or VIDraft
-                        "model": {"value": "gpt-4.1-mini"},  # Fixed to gpt-4.1-mini for OpenAI
-                        "temperature": {"value": 0.7},
-                        "system_prompt": {"value": "You are a helpful assistant."}
+            ],
+            "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"}
                 }
-            },
-            {
-                "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"}
-        ]
-    }
+            ],
+            "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:
@@ -181,6 +371,26 @@ def execute_workflow_simple(workflow_data: dict, input_values: dict) -> dict:
                     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", {})
@@ -346,6 +556,10 @@ CSS = """
     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;
+}
 """
 
 # -------------------------------------------------------------------
@@ -381,6 +595,12 @@ with gr.Blocks(title="🐭 MOUSE Workflow", theme=gr.themes.Soft(), css=CSS) as
             - 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.*
             """)
         
@@ -419,7 +639,15 @@ with gr.Blocks(title="🐭 MOUSE Workflow", theme=gr.themes.Soft(), css=CSS) as
                         type="filepath"
                     )
                     btn_load = gr.Button("📥 Load Workflow", variant="primary", size="lg")
-                    btn_sample = gr.Button("🎯 Load Sample", variant="secondary")
+                    
+                    # 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(
@@ -472,6 +700,9 @@ with gr.Blocks(title="🐭 MOUSE Workflow", theme=gr.themes.Soft(), css=CSS) as
                         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 = {}
@@ -618,9 +849,36 @@ with gr.Blocks(title="🐭 MOUSE Workflow", theme=gr.themes.Soft(), css=CSS) as
             outputs=trigger_update
         )
         
-        # Load sample
-        btn_sample.click(
-            fn=lambda: (create_sample_workflow(), "✅ Sample loaded", export_pretty(create_sample_workflow())),
+        # 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,