Update app.py

app.py

@@ -34,6 +34,13 @@ except ImportError:
     REQUESTS_AVAILABLE = False
     print("Requests library not available. Install with: pip install requests")
 
+try:
+    from huggingface_hub import HfApi, create_repo, upload_file
+    HF_HUB_AVAILABLE = True
+except ImportError:
+    HF_HUB_AVAILABLE = False
+    print("Huggingface Hub not available. Install with: pip install huggingface-hub")
+
 # -------------------------------------------------------------------
 # 🛠️  헬퍼 함수들
 # -------------------------------------------------------------------
@@ -323,6 +330,287 @@ def create_sample_workflow(example_type="basic"):
     # 기본값은 basic
     return create_sample_workflow("basic")
 
+# 배포를 위한 독립 앱 생성 함수
+def generate_standalone_app(workflow_data: dict, app_name: str, app_description: str) -> str:
+    """워크플로우를 독립적인 Gradio 앱으로 변환"""
+    
+    app_code = f'''"""
+{app_name}
+{app_description}
+Generated by MOUSE Workflow
+"""
+
+import os
+import json
+import gradio as gr
+import requests
+
+# Workflow configuration
+WORKFLOW_DATA = {json.dumps(workflow_data, indent=2)}
+
+def execute_workflow(*input_values):
+    """Execute the workflow with given inputs"""
+    
+    # API keys from environment
+    vidraft_token = os.getenv("FRIENDLI_TOKEN")
+    openai_key = os.getenv("OPENAI_API_KEY")
+    
+    nodes = WORKFLOW_DATA.get("nodes", [])
+    edges = WORKFLOW_DATA.get("edges", [])
+    
+    results = {{}}
+    
+    # Get input nodes
+    input_nodes = [n for n in nodes if n.get("type") in ["ChatInput", "textInput", "Input", "numberInput"]]
+    
+    # Map inputs to node IDs
+    for i, node in enumerate(input_nodes):
+        if i < len(input_values):
+            results[node["id"]] = input_values[i]
+    
+    # Process nodes
+    for node in nodes:
+        node_id = node.get("id")
+        node_type = node.get("type", "")
+        node_data = node.get("data", {{}})
+        template = node_data.get("template", {{}})
+        
+        if node_type == "textNode":
+            # Combine connected inputs
+            base_text = template.get("text", {{}}).get("value", "")
+            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]}}")
+            
+            if connected_inputs:
+                results[node_id] = f"{{base_text}}\\n\\nInputs:\\n" + "\\n".join(connected_inputs)
+            else:
+                results[node_id] = base_text
+                
+        elif node_type in ["llmNode", "OpenAIModel", "ChatModel"]:
+            # Get provider and model
+            provider = template.get("provider", {{}}).get("value", "OpenAI")
+            temperature = template.get("temperature", {{}}).get("value", 0.7)
+            system_prompt = template.get("system_prompt", {{}}).get("value", "")
+            
+            # Get input text
+            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
+            
+            # Call API
+            if provider == "OpenAI" and openai_key:
+                try:
+                    from openai import OpenAI
+                    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",
+                        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)}}]"
+                    
+            elif provider == "VIDraft" and vidraft_token:
+                try:
+                    headers = {{
+                        "Authorization": f"Bearer {{vidraft_token}}",
+                        "Content-Type": "application/json"
+                    }}
+                    
+                    messages = []
+                    if system_prompt:
+                        messages.append({{"role": "system", "content": system_prompt}})
+                    messages.append({{"role": "user", "content": input_text}})
+                    
+                    payload = {{
+                        "model": "dep89a2fld32mcm",
+                        "messages": messages,
+                        "max_tokens": 16384,
+                        "temperature": temperature,
+                        "top_p": 0.8,
+                        "stream": False
+                    }}
+                    
+                    response = requests.post(
+                        "https://api.friendli.ai/dedicated/v1/chat/completions",
+                        headers=headers,
+                        json=payload,
+                        timeout=30
+                    )
+                    
+                    if response.status_code == 200:
+                        results[node_id] = response.json()["choices"][0]["message"]["content"]
+                    else:
+                        results[node_id] = f"[VIDraft Error: {{response.status_code}}]"
+                except Exception as e:
+                    results[node_id] = f"[VIDraft Error: {{str(e)}}]"
+            else:
+                results[node_id] = f"[Simulated Response: {{input_text[:50]}}...]"
+                
+        elif node_type in ["ChatOutput", "textOutput", "Output"]:
+            # Get connected result
+            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
+    
+    # Return outputs
+    output_nodes = [n for n in nodes if n.get("type") in ["ChatOutput", "textOutput", "Output"]]
+    return [results.get(n["id"], "") for n in output_nodes]
+
+# Build UI
+with gr.Blocks(title="{app_name}", theme=gr.themes.Soft()) as demo:
+    gr.Markdown(f"# {{app_name}}")
+    gr.Markdown(f"{{app_description}}")
+    
+    # Extract nodes
+    nodes = WORKFLOW_DATA.get("nodes", [])
+    input_nodes = [n for n in nodes if n.get("type") in ["ChatInput", "textInput", "Input", "numberInput"]]
+    output_nodes = [n for n in nodes if n.get("type") in ["ChatOutput", "textOutput", "Output"]]
+    
+    # Create inputs
+    inputs = []
+    if input_nodes:
+        gr.Markdown("### 📥 Inputs")
+        for node in input_nodes:
+            label = node.get("data", {{}}).get("label", node.get("id"))
+            template = node.get("data", {{}}).get("template", {{}})
+            default_value = template.get("input_value", {{}}).get("value", "")
+            
+            if node.get("type") == "numberInput":
+                inp = gr.Number(label=label, value=float(default_value) if default_value else 0)
+            else:
+                inp = gr.Textbox(label=label, value=default_value, lines=2)
+            inputs.append(inp)
+    
+    # Execute button
+    btn = gr.Button("🚀 Execute Workflow", variant="primary")
+    
+    # Create outputs
+    outputs = []
+    if output_nodes:
+        gr.Markdown("### 📤 Outputs")
+        for node in output_nodes:
+            label = node.get("data", {{}}).get("label", node.get("id"))
+            out = gr.Textbox(label=label, interactive=False, lines=3)
+            outputs.append(out)
+    
+    # Connect
+    btn.click(fn=execute_workflow, inputs=inputs, outputs=outputs)
+    
+    gr.Markdown("---")
+    gr.Markdown("*Powered by MOUSE Workflow*")
+
+if __name__ == "__main__":
+    demo.launch()
+'''
+    
+    return app_code
+
+def generate_requirements_txt() -> str:
+    """Generate requirements.txt for the standalone app"""
+    return """gradio>=4.0.0
+openai>=1.0.0
+requests>=2.31.0
+"""
+
+def deploy_to_huggingface(workflow_data: dict, app_name: str, app_description: str, 
+                         hf_token: str, space_name: str, is_private: bool = False) -> dict:
+    """Deploy workflow to Hugging Face Space"""
+    
+    if not HF_HUB_AVAILABLE:
+        return {"success": False, "error": "huggingface-hub library not installed"}
+    
+    try:
+        # Initialize HF API
+        api = HfApi(token=hf_token)
+        
+        # Create repository
+        repo_id = api.create_repo(
+            repo_id=space_name,
+            repo_type="space",
+            space_sdk="gradio",
+            private=is_private,
+            exist_ok=True
+        )
+        
+        # Generate files
+        app_code = generate_standalone_app(workflow_data, app_name, app_description)
+        requirements = generate_requirements_txt()
+        readme = f"""---
+title: {app_name}
+emoji: 🐭
+colorFrom: blue
+colorTo: green
+sdk: gradio
+sdk_version: 4.0.0
+app_file: app.py
+pinned: false
+---
+
+# {app_name}
+
+{app_description}
+
+Generated by MOUSE Workflow
+"""
+        
+        # Upload files
+        api.upload_file(
+            path_or_fileobj=app_code.encode(),
+            path_in_repo="app.py",
+            repo_id=repo_id.repo_id,
+            repo_type="space"
+        )
+        
+        api.upload_file(
+            path_or_fileobj=requirements.encode(),
+            path_in_repo="requirements.txt",
+            repo_id=repo_id.repo_id,
+            repo_type="space"
+        )
+        
+        api.upload_file(
+            path_or_fileobj=readme.encode(),
+            path_in_repo="README.md",
+            repo_id=repo_id.repo_id,
+            repo_type="space"
+        )
+        
+        space_url = f"https://huggingface.co/spaces/{repo_id.repo_id}"
+        
+        return {
+            "success": True,
+            "space_url": space_url,
+            "message": f"Successfully deployed to {space_url}"
+        }
+        
+    except Exception as e:
+        return {
+            "success": False,
+            "error": str(e)
+        }
+
 # UI 실행을 위한 실제 워크플로우 실행 함수
 def execute_workflow_simple(workflow_data: dict, input_values: dict) -> dict:
     """워크플로우 실제 실행"""