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src/backend/gradio_workflowbuilder/workflowbuilder.py
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1 |
+
from __future__ import annotations
|
2 |
+
|
3 |
+
from typing import Any, Dict, List, Optional, Union, Callable
|
4 |
+
from pathlib import Path
|
5 |
+
import json
|
6 |
+
from gradio.components import Component
|
7 |
+
|
8 |
+
COMP_DIR = Path(__file__).resolve().parent / "templates" / "component" # โฌ
๏ธ ์ถ๊ฐ
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9 |
+
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10 |
+
class WorkflowBuilder(Component):
|
11 |
+
"""
|
12 |
+
Professional Workflow Builder component with support for 25+ node types
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13 |
+
inspired by n8n and Langflow for AI agent development and MCP integration.
|
14 |
+
"""
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15 |
+
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16 |
+
# โถ๏ธ Gradio 4.x : ์ด๋ฒคํธ ์ด๋ฆ์ ๋ฌธ์์ด๋ก ์ง์ ๋ช
์
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17 |
+
EVENTS = ["change", "input"]
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18 |
+
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19 |
+
def __init__(
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20 |
+
self,
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21 |
+
value: Optional[Dict[str, Any]] = None,
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22 |
+
label: Optional[str] = None,
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23 |
+
info: Optional[str] = None,
|
24 |
+
show_label: Optional[bool] = None,
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25 |
+
container: bool = True,
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26 |
+
scale: Optional[int] = None,
|
27 |
+
min_width: int = 160,
|
28 |
+
visible: bool = True,
|
29 |
+
elem_id: Optional[str] = None,
|
30 |
+
elem_classes: Optional[List[str]] = None,
|
31 |
+
render: bool = True,
|
32 |
+
**kwargs,
|
33 |
+
|
34 |
+
):
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35 |
+
# ์ด ๋ถ๋ถ์ด ๋น ์ํฌํ๋ก์ฐ์ธ์ง ํ์ธ
|
36 |
+
if value is None:
|
37 |
+
value = {"nodes": [], "edges": []}
|
38 |
+
|
39 |
+
"""
|
40 |
+
Parameters:
|
41 |
+
value: Default workflow data with nodes and edges
|
42 |
+
label: Component label
|
43 |
+
info: Additional component information
|
44 |
+
show_label: Whether to show the label
|
45 |
+
container: Whether to use container styling
|
46 |
+
scale: Relative width scale
|
47 |
+
min_width: Minimum width in pixels
|
48 |
+
visible: Whether component is visible
|
49 |
+
elem_id: HTML element ID
|
50 |
+
elem_classes: CSS classes
|
51 |
+
render: Whether to render immediately
|
52 |
+
"""
|
53 |
+
|
54 |
+
# Validate the workflow data
|
55 |
+
if not isinstance(value, dict):
|
56 |
+
raise ValueError("Workflow value must be a dictionary")
|
57 |
+
|
58 |
+
if "nodes" not in value:
|
59 |
+
value["nodes"] = []
|
60 |
+
if "edges" not in value:
|
61 |
+
value["edges"] = []
|
62 |
+
|
63 |
+
super().__init__(
|
64 |
+
label=label,
|
65 |
+
info=info,
|
66 |
+
show_label=show_label,
|
67 |
+
container=container,
|
68 |
+
scale=scale,
|
69 |
+
min_width=min_width,
|
70 |
+
visible=visible,
|
71 |
+
elem_id=elem_id,
|
72 |
+
elem_classes=elem_classes,
|
73 |
+
render=render,
|
74 |
+
value=value,
|
75 |
+
**kwargs,
|
76 |
+
)
|
77 |
+
|
78 |
+
def preprocess(self, payload: Dict[str, Any]) -> Dict[str, Any]:
|
79 |
+
"""
|
80 |
+
Process workflow data from frontend
|
81 |
+
"""
|
82 |
+
if payload is None:
|
83 |
+
return {"nodes": [], "edges": []}
|
84 |
+
|
85 |
+
# Validate and clean the workflow data
|
86 |
+
workflow = self._validate_workflow(payload)
|
87 |
+
return workflow
|
88 |
+
|
89 |
+
def postprocess(self, value: Dict[str, Any]) -> Dict[str, Any]:
|
90 |
+
"""
|
91 |
+
Process workflow data for frontend
|
92 |
+
"""
|
93 |
+
if value is None:
|
94 |
+
return {"nodes": [], "edges": []}
|
95 |
+
|
96 |
+
# Ensure proper structure
|
97 |
+
if not isinstance(value, dict):
|
98 |
+
return {"nodes": [], "edges": []}
|
99 |
+
|
100 |
+
return {
|
101 |
+
"nodes": value.get("nodes", []),
|
102 |
+
"edges": value.get("edges", [])
|
103 |
+
}
|
104 |
+
|
105 |
+
def _validate_workflow(self, workflow: Dict[str, Any]) -> Dict[str, Any]:
|
106 |
+
"""
|
107 |
+
Validate workflow structure and node configurations
|
108 |
+
"""
|
109 |
+
if not isinstance(workflow, dict):
|
110 |
+
return {"nodes": [], "edges": []}
|
111 |
+
|
112 |
+
nodes = workflow.get("nodes", [])
|
113 |
+
edges = workflow.get("edges", [])
|
114 |
+
|
115 |
+
# Validate each node
|
116 |
+
validated_nodes = []
|
117 |
+
for node in nodes:
|
118 |
+
if self._validate_node(node):
|
119 |
+
validated_nodes.append(node)
|
120 |
+
|
121 |
+
# Validate each edge
|
122 |
+
validated_edges = []
|
123 |
+
node_ids = {node["id"] for node in validated_nodes}
|
124 |
+
for edge in edges:
|
125 |
+
if self._validate_edge(edge, node_ids):
|
126 |
+
validated_edges.append(edge)
|
127 |
+
|
128 |
+
return {
|
129 |
+
"nodes": validated_nodes,
|
130 |
+
"edges": validated_edges
|
131 |
+
}
|
132 |
+
|
133 |
+
def _validate_node(self, node: Dict[str, Any]) -> bool:
|
134 |
+
"""
|
135 |
+
Validate individual node structure and properties
|
136 |
+
"""
|
137 |
+
required_fields = ["id", "type", "position", "data"]
|
138 |
+
|
139 |
+
# Check required fields
|
140 |
+
if not all(field in node for field in required_fields):
|
141 |
+
return False
|
142 |
+
|
143 |
+
# Validate node type
|
144 |
+
if not self._is_valid_node_type(node["type"]):
|
145 |
+
return False
|
146 |
+
|
147 |
+
# Validate position
|
148 |
+
position = node["position"]
|
149 |
+
if not isinstance(position, dict) or "x" not in position or "y" not in position:
|
150 |
+
return False
|
151 |
+
|
152 |
+
# Validate node data based on type
|
153 |
+
return self._validate_node_data(node["type"], node["data"])
|
154 |
+
|
155 |
+
def _validate_edge(self, edge: Dict[str, Any], valid_node_ids: set) -> bool:
|
156 |
+
"""
|
157 |
+
Validate edge connections
|
158 |
+
"""
|
159 |
+
required_fields = ["id", "source", "target"]
|
160 |
+
|
161 |
+
if not all(field in edge for field in required_fields):
|
162 |
+
return False
|
163 |
+
|
164 |
+
# Check if source and target nodes exist
|
165 |
+
return (edge["source"] in valid_node_ids and
|
166 |
+
edge["target"] in valid_node_ids)
|
167 |
+
|
168 |
+
def _is_valid_node_type(self, node_type: str) -> bool:
|
169 |
+
"""
|
170 |
+
Check if node type is supported
|
171 |
+
"""
|
172 |
+
# All the node types from your frontend
|
173 |
+
|
174 |
+
|
175 |
+
|
176 |
+
|
177 |
+
supported_types = {
|
178 |
+
# ๐ [CUSTOM] --------------------------------------------------
|
179 |
+
"llmNode", # ๋ฒ์ฉ LLM ๋
ธ๋ (AI Processing)
|
180 |
+
"textNode", # ๊ฐ๋จํ Markdown/Text ๋
ธ๋
|
181 |
+
# --------------------------------------------------------------
|
182 |
+
|
183 |
+
|
184 |
+
|
185 |
+
|
186 |
+
|
187 |
+
|
188 |
+
# Input/Output Nodes
|
189 |
+
"ChatInput", "ChatOutput", "Input", "Output",
|
190 |
+
|
191 |
+
# AI & Language Models
|
192 |
+
"OpenAIModel", "ChatModel", "Prompt", "HFTextGeneration",
|
193 |
+
|
194 |
+
# API & Web
|
195 |
+
"APIRequest", "WebSearch",
|
196 |
+
|
197 |
+
# Data Processing
|
198 |
+
"ExecutePython", "ConditionalLogic", "Wait",
|
199 |
+
|
200 |
+
# RAG & Knowledge
|
201 |
+
"KnowledgeBase", "RAGQuery",
|
202 |
+
|
203 |
+
# Speech & Vision
|
204 |
+
"HFSpeechToText", "HFTextToSpeech", "HFVisionModel",
|
205 |
+
|
206 |
+
# Image Generation
|
207 |
+
"HFImageGeneration", "NebiusImage",
|
208 |
+
|
209 |
+
# MCP Integration
|
210 |
+
"MCPConnection", "MCPAgent",
|
211 |
+
|
212 |
+
# Legacy types (for backward compatibility)
|
213 |
+
"textInput", "fileInput", "numberInput", "llm", "textProcessor",
|
214 |
+
"conditional", "textOutput", "fileOutput", "chartOutput",
|
215 |
+
"apiCall", "dataTransform", "webhook", "schedule", "manualTrigger",
|
216 |
+
"emailTrigger", "httpRequest", "googleSheets", "database", "csvFile",
|
217 |
+
"openaiChat", "claudeChat", "huggingFace", "textEmbedding",
|
218 |
+
"codeNode", "functionNode", "setNode", "jsonParse",
|
219 |
+
"ifCondition", "switchNode", "merge", "waitNode",
|
220 |
+
"email", "slack", "discord", "telegram",
|
221 |
+
"fileUpload", "awsS3", "googleDrive", "ftp",
|
222 |
+
"dateTime", "crypto", "validator", "regex"
|
223 |
+
}
|
224 |
+
|
225 |
+
return node_type in supported_types
|
226 |
+
|
227 |
+
def _validate_node_data(self, node_type: str, data: Dict[str, Any]) -> bool:
|
228 |
+
"""
|
229 |
+
Validate node data based on node type
|
230 |
+
"""
|
231 |
+
if not isinstance(data, dict):
|
232 |
+
return False
|
233 |
+
|
234 |
+
# Define required fields for each node type
|
235 |
+
required_fields = {
|
236 |
+
|
237 |
+
|
238 |
+
# ๐ [CUSTOM] --------------------------------------------------
|
239 |
+
"llmNode": ["template"], # provider ยท model ๋ฑ์ template ๋ด๋ถ์ ์กด์ฌ
|
240 |
+
"textNode": ["template"], # { "text": {...} }
|
241 |
+
# --------------------------------------------------------------
|
242 |
+
|
243 |
+
|
244 |
+
|
245 |
+
# Input/Output Nodes
|
246 |
+
"ChatInput": ["display_name", "template"],
|
247 |
+
"ChatOutput": ["display_name", "template"],
|
248 |
+
"Input": ["display_name", "template"],
|
249 |
+
"Output": ["display_name", "template"],
|
250 |
+
|
251 |
+
# AI & Language Models
|
252 |
+
"OpenAIModel": ["display_name", "template"],
|
253 |
+
"ChatModel": ["display_name", "template"],
|
254 |
+
"Prompt": ["display_name", "template"],
|
255 |
+
"HFTextGeneration": ["display_name", "template"],
|
256 |
+
|
257 |
+
# API & Web
|
258 |
+
"APIRequest": ["display_name", "template"],
|
259 |
+
"WebSearch": ["display_name", "template"],
|
260 |
+
|
261 |
+
# Data Processing
|
262 |
+
"ExecutePython": ["display_name", "template"],
|
263 |
+
"ConditionalLogic": ["display_name", "template"],
|
264 |
+
"Wait": ["display_name", "template"],
|
265 |
+
|
266 |
+
# RAG & Knowledge
|
267 |
+
"KnowledgeBase": ["display_name", "template"],
|
268 |
+
"RAGQuery": ["display_name", "template"],
|
269 |
+
|
270 |
+
# Speech & Vision
|
271 |
+
"HFSpeechToText": ["display_name", "template"],
|
272 |
+
"HFTextToSpeech": ["display_name", "template"],
|
273 |
+
"HFVisionModel": ["display_name", "template"],
|
274 |
+
|
275 |
+
# Image Generation
|
276 |
+
"HFImageGeneration": ["display_name", "template"],
|
277 |
+
"NebiusImage": ["display_name", "template"],
|
278 |
+
|
279 |
+
# MCP Integration
|
280 |
+
"MCPConnection": ["display_name", "template"],
|
281 |
+
"MCPAgent": ["display_name", "template"],
|
282 |
+
|
283 |
+
# Legacy types
|
284 |
+
"webhook": ["method", "path"],
|
285 |
+
"httpRequest": ["method", "url"],
|
286 |
+
"openaiChat": ["model"],
|
287 |
+
"claudeChat": ["model"],
|
288 |
+
"codeNode": ["language", "code"],
|
289 |
+
"ifCondition": ["conditions"],
|
290 |
+
"email": ["fromEmail", "toEmail", "subject"],
|
291 |
+
"awsS3": ["operation", "bucketName"]
|
292 |
+
}
|
293 |
+
|
294 |
+
# Check required fields for this node type
|
295 |
+
if node_type in required_fields:
|
296 |
+
required = required_fields[node_type]
|
297 |
+
if not all(field in data for field in required):
|
298 |
+
return False
|
299 |
+
|
300 |
+
return True
|
301 |
+
|
302 |
+
def api_info(self) -> Dict[str, Any]:
|
303 |
+
"""
|
304 |
+
API information for the component
|
305 |
+
"""
|
306 |
+
return {
|
307 |
+
"info": {
|
308 |
+
"type": "object",
|
309 |
+
"properties": {
|
310 |
+
"nodes": {
|
311 |
+
"type": "array",
|
312 |
+
"items": {
|
313 |
+
"type": "object",
|
314 |
+
"properties": {
|
315 |
+
"id": {"type": "string"},
|
316 |
+
"type": {"type": "string"},
|
317 |
+
"position": {
|
318 |
+
"type": "object",
|
319 |
+
"properties": {
|
320 |
+
"x": {"type": "number"},
|
321 |
+
"y": {"type": "number"}
|
322 |
+
}
|
323 |
+
},
|
324 |
+
"data": {"type": "object"}
|
325 |
+
}
|
326 |
+
}
|
327 |
+
},
|
328 |
+
"edges": {
|
329 |
+
"type": "array",
|
330 |
+
"items": {
|
331 |
+
"type": "object",
|
332 |
+
"properties": {
|
333 |
+
"id": {"type": "string"},
|
334 |
+
"source": {"type": "string"},
|
335 |
+
"target": {"type": "string"}
|
336 |
+
}
|
337 |
+
}
|
338 |
+
}
|
339 |
+
}
|
340 |
+
}
|
341 |
+
}
|
342 |
+
|
343 |
+
def example_payload(self) -> Dict[str, Any]:
|
344 |
+
"""
|
345 |
+
Example payload for the component
|
346 |
+
"""
|
347 |
+
return {"nodes": [], "edges": []} # ๋น ์ํฌํ๋ก์ฐ ๋ฐํ
|
348 |
+
|
349 |
+
|
350 |
+
|
351 |
+
def example_value(self) -> Dict[str, Any]:
|
352 |
+
"""
|
353 |
+
Example value for the component
|
354 |
+
"""
|
355 |
+
|
356 |
+
return {"nodes": [], "edges": []} # ๋น ์ํฌํ๋ก์ฐ ๋ฐํ
|
357 |
+
|
358 |
+
|
359 |
+
# Utility functions for workflow analysis and execution
|
360 |
+
class WorkflowAnalyzer:
|
361 |
+
"""
|
362 |
+
Analyze workflow configurations and provide insights
|
363 |
+
"""
|
364 |
+
|
365 |
+
@staticmethod
|
366 |
+
def analyze_workflow(workflow: Dict[str, Any]) -> Dict[str, Any]:
|
367 |
+
"""
|
368 |
+
Provide detailed analysis of a workflow
|
369 |
+
"""
|
370 |
+
nodes = workflow.get("nodes", [])
|
371 |
+
edges = workflow.get("edges", [])
|
372 |
+
|
373 |
+
# Count node types
|
374 |
+
node_types = {}
|
375 |
+
for node in nodes:
|
376 |
+
node_type = node.get("type", "unknown")
|
377 |
+
node_types[node_type] = node_types.get(node_type, 0) + 1
|
378 |
+
|
379 |
+
# Analyze workflow complexity
|
380 |
+
complexity = "Simple"
|
381 |
+
if len(nodes) > 10:
|
382 |
+
complexity = "Complex"
|
383 |
+
elif len(nodes) > 5:
|
384 |
+
complexity = "Medium"
|
385 |
+
|
386 |
+
# Check for potential issues
|
387 |
+
issues = []
|
388 |
+
|
389 |
+
# Check for disconnected nodes
|
390 |
+
connected_nodes = set()
|
391 |
+
for edge in edges:
|
392 |
+
connected_nodes.add(edge["source"])
|
393 |
+
connected_nodes.add(edge["target"])
|
394 |
+
|
395 |
+
disconnected = [node["id"] for node in nodes if node["id"] not in connected_nodes]
|
396 |
+
if disconnected:
|
397 |
+
issues.append(f"Disconnected nodes: {', '.join(disconnected)}")
|
398 |
+
|
399 |
+
# Check for missing required fields and API keys
|
400 |
+
for node in nodes:
|
401 |
+
node_type = node.get("type")
|
402 |
+
data = node.get("data", {})
|
403 |
+
|
404 |
+
# Check for required API keys
|
405 |
+
if node_type == "OpenAIModel" and not data.get("template", {}).get("api_key", {}).get("value"):
|
406 |
+
issues.append(f"Node {node['id']} missing OpenAI API key")
|
407 |
+
elif node_type == "ChatModel" and not data.get("template", {}).get("api_key", {}).get("value"):
|
408 |
+
issues.append(f"Node {node['id']} missing API key")
|
409 |
+
elif node_type == "NebiusImage" and not data.get("template", {}).get("api_key", {}).get("value"):
|
410 |
+
issues.append(f"Node {node['id']} missing Nebius API key")
|
411 |
+
|
412 |
+
# Check for required model configurations
|
413 |
+
if node_type in ["OpenAIModel", "ChatModel", "HFTextGeneration"] and not data.get("template", {}).get("model", {}).get("value"):
|
414 |
+
issues.append(f"Node {node['id']} missing model configuration")
|
415 |
+
|
416 |
+
# Check for required templates
|
417 |
+
if node_type in ["Prompt", "ChatInput", "ChatOutput"] and not data.get("template"):
|
418 |
+
issues.append(f"Node {node['id']} missing template configuration")
|
419 |
+
|
420 |
+
# Analyze node categories
|
421 |
+
input_nodes = [n for n in nodes if n.get("type") in ["ChatInput", "Input"]]
|
422 |
+
processing_nodes = [n for n in nodes if n.get("type") in [
|
423 |
+
"OpenAIModel", "ChatModel", "Prompt", "HFTextGeneration",
|
424 |
+
"ExecutePython", "ConditionalLogic", "Wait", "APIRequest",
|
425 |
+
"WebSearch", "KnowledgeBase", "RAGQuery"
|
426 |
+
]]
|
427 |
+
output_nodes = [n for n in nodes if n.get("type") in ["ChatOutput", "Output"]]
|
428 |
+
ai_nodes = [n for n in nodes if n.get("type") in [
|
429 |
+
"OpenAIModel", "ChatModel", "HFTextGeneration", "HFImageGeneration",
|
430 |
+
"NebiusImage", "HFSpeechToText", "HFTextToSpeech", "HFVisionModel"
|
431 |
+
]]
|
432 |
+
|
433 |
+
return {
|
434 |
+
"total_nodes": len(nodes),
|
435 |
+
"total_edges": len(edges),
|
436 |
+
"node_types": node_types,
|
437 |
+
"complexity": complexity,
|
438 |
+
"issues": issues,
|
439 |
+
"is_valid": len(issues) == 0,
|
440 |
+
"categories": {
|
441 |
+
"input_nodes": len(input_nodes),
|
442 |
+
"processing_nodes": len(processing_nodes),
|
443 |
+
"output_nodes": len(output_nodes),
|
444 |
+
"ai_nodes": len(ai_nodes)
|
445 |
+
}
|
446 |
+
}
|
447 |
+
|
448 |
+
@staticmethod
|
449 |
+
def validate_for_execution(workflow: Dict[str, Any]) -> Dict[str, Any]:
|
450 |
+
"""
|
451 |
+
Validate if workflow is ready for execution
|
452 |
+
"""
|
453 |
+
analysis = WorkflowAnalyzer.analyze_workflow(workflow)
|
454 |
+
|
455 |
+
# Additional execution-specific checks
|
456 |
+
nodes = workflow.get("nodes", [])
|
457 |
+
|
458 |
+
# Check for entry points (input nodes)
|
459 |
+
input_types = {"ChatInput", "Input"}
|
460 |
+
inputs = [n for n in nodes if n.get("type") in input_types]
|
461 |
+
|
462 |
+
if not inputs:
|
463 |
+
analysis["issues"].append("No input nodes found - workflow needs an entry point")
|
464 |
+
|
465 |
+
# Check for output nodes
|
466 |
+
output_types = {"ChatOutput", "Output"}
|
467 |
+
outputs = [n for n in nodes if n.get("type") in output_types]
|
468 |
+
|
469 |
+
if not outputs:
|
470 |
+
analysis["issues"].append("No output nodes found - workflow needs an exit point")
|
471 |
+
|
472 |
+
# Check for required environment variables
|
473 |
+
env_vars = set()
|
474 |
+
for node in nodes:
|
475 |
+
data = node.get("data", {})
|
476 |
+
template = data.get("template", {})
|
477 |
+
for field in template.values():
|
478 |
+
if isinstance(field, dict) and field.get("type") == "SecretStr":
|
479 |
+
env_var = field.get("env_var")
|
480 |
+
if env_var:
|
481 |
+
env_vars.add(env_var)
|
482 |
+
|
483 |
+
if env_vars:
|
484 |
+
analysis["required_env_vars"] = list(env_vars)
|
485 |
+
|
486 |
+
analysis["is_executable"] = len(analysis["issues"]) == 0
|
487 |
+
|
488 |
+
return analysis
|
489 |
+
|
490 |
+
|
491 |
+
# Export the main component
|
492 |
+
__all__ = ["WorkflowBuilder", "WorkflowAnalyzer"]
|