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https://github.com/FuzzingLabs/fuzzforge_ai.git
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Initial commit
This commit is contained in:
Tanguy Duhamel
@@ -0,0 +1,386 @@
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"""
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API endpoints for workflow management with enhanced error handling
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"""
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# Copyright (c) 2025 FuzzingLabs
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#
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# Licensed under the Business Source License 1.1 (BSL). See the LICENSE file
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# at the root of this repository for details.
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#
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# After the Change Date (four years from publication), this version of the
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# Licensed Work will be made available under the Apache License, Version 2.0.
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# See the LICENSE-APACHE file or http://www.apache.org/licenses/LICENSE-2.0
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#
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# Additional attribution and requirements are provided in the NOTICE file.
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import logging
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import traceback
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from typing import List, Dict, Any, Optional
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from fastapi import APIRouter, HTTPException, Depends
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from pathlib import Path
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from src.models.findings import (
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WorkflowSubmission,
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WorkflowMetadata,
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WorkflowListItem,
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RunSubmissionResponse
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)
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from src.core.workflow_discovery import WorkflowDiscovery
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/workflows", tags=["workflows"])
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def create_structured_error_response(
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error_type: str,
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message: str,
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workflow_name: Optional[str] = None,
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run_id: Optional[str] = None,
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container_info: Optional[Dict[str, Any]] = None,
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deployment_info: Optional[Dict[str, Any]] = None,
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suggestions: Optional[List[str]] = None
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) -> Dict[str, Any]:
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"""Create a structured error response with rich context."""
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error_response = {
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"error": {
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"type": error_type,
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"message": message,
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"timestamp": __import__("datetime").datetime.utcnow().isoformat() + "Z"
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}
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}
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if workflow_name:
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error_response["error"]["workflow_name"] = workflow_name
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if run_id:
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error_response["error"]["run_id"] = run_id
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if container_info:
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error_response["error"]["container"] = container_info
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if deployment_info:
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error_response["error"]["deployment"] = deployment_info
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if suggestions:
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error_response["error"]["suggestions"] = suggestions
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return error_response
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def get_prefect_manager():
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"""Dependency to get the Prefect manager instance"""
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from src.main import prefect_mgr
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return prefect_mgr
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@router.get("/", response_model=List[WorkflowListItem])
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async def list_workflows(
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prefect_mgr=Depends(get_prefect_manager)
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) -> List[WorkflowListItem]:
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"""
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List all discovered workflows with their metadata.
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Returns a summary of each workflow including name, version, description,
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author, and tags.
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"""
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workflows = []
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for name, info in prefect_mgr.workflows.items():
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workflows.append(WorkflowListItem(
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name=name,
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version=info.metadata.get("version", "0.6.0"),
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description=info.metadata.get("description", ""),
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author=info.metadata.get("author"),
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tags=info.metadata.get("tags", [])
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))
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return workflows
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@router.get("/metadata/schema")
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async def get_metadata_schema() -> Dict[str, Any]:
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"""
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Get the JSON schema for workflow metadata files.
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This schema defines the structure and requirements for metadata.yaml files
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that must accompany each workflow.
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"""
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return WorkflowDiscovery.get_metadata_schema()
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@router.get("/{workflow_name}/metadata", response_model=WorkflowMetadata)
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async def get_workflow_metadata(
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workflow_name: str,
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prefect_mgr=Depends(get_prefect_manager)
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) -> WorkflowMetadata:
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"""
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Get complete metadata for a specific workflow.
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Args:
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workflow_name: Name of the workflow
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Returns:
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Complete metadata including parameters schema, supported volume modes,
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required modules, and more.
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Raises:
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HTTPException: 404 if workflow not found
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"""
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if workflow_name not in prefect_mgr.workflows:
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available_workflows = list(prefect_mgr.workflows.keys())
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error_response = create_structured_error_response(
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error_type="WorkflowNotFound",
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message=f"Workflow '{workflow_name}' not found",
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workflow_name=workflow_name,
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suggestions=[
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f"Available workflows: {', '.join(available_workflows)}",
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"Use GET /workflows/ to see all available workflows",
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"Check workflow name spelling and case sensitivity"
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]
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)
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raise HTTPException(
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status_code=404,
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detail=error_response
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)
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info = prefect_mgr.workflows[workflow_name]
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metadata = info.metadata
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return WorkflowMetadata(
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name=workflow_name,
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version=metadata.get("version", "0.6.0"),
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description=metadata.get("description", ""),
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author=metadata.get("author"),
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tags=metadata.get("tags", []),
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parameters=metadata.get("parameters", {}),
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default_parameters=metadata.get("default_parameters", {}),
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required_modules=metadata.get("required_modules", []),
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supported_volume_modes=metadata.get("supported_volume_modes", ["ro", "rw"]),
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has_custom_docker=info.has_docker
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)
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@router.post("/{workflow_name}/submit", response_model=RunSubmissionResponse)
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async def submit_workflow(
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workflow_name: str,
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submission: WorkflowSubmission,
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prefect_mgr=Depends(get_prefect_manager)
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) -> RunSubmissionResponse:
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"""
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Submit a workflow for execution with volume mounting.
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Args:
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workflow_name: Name of the workflow to execute
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submission: Submission parameters including target path and volume mode
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Returns:
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Run submission response with run_id and initial status
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Raises:
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HTTPException: 404 if workflow not found, 400 for invalid parameters
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"""
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if workflow_name not in prefect_mgr.workflows:
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available_workflows = list(prefect_mgr.workflows.keys())
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error_response = create_structured_error_response(
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error_type="WorkflowNotFound",
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message=f"Workflow '{workflow_name}' not found",
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workflow_name=workflow_name,
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suggestions=[
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f"Available workflows: {', '.join(available_workflows)}",
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"Use GET /workflows/ to see all available workflows",
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"Check workflow name spelling and case sensitivity"
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]
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)
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raise HTTPException(
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status_code=404,
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detail=error_response
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)
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try:
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# Convert ResourceLimits to dict if provided
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resource_limits_dict = None
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if submission.resource_limits:
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resource_limits_dict = {
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"cpu_limit": submission.resource_limits.cpu_limit,
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"memory_limit": submission.resource_limits.memory_limit,
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"cpu_request": submission.resource_limits.cpu_request,
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"memory_request": submission.resource_limits.memory_request
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}
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# Submit the workflow with enhanced parameters
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flow_run = await prefect_mgr.submit_workflow(
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workflow_name=workflow_name,
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target_path=submission.target_path,
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volume_mode=submission.volume_mode,
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parameters=submission.parameters,
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resource_limits=resource_limits_dict,
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additional_volumes=submission.additional_volumes,
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timeout=submission.timeout
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)
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run_id = str(flow_run.id)
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# Initialize fuzzing tracking if this looks like a fuzzing workflow
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workflow_info = prefect_mgr.workflows.get(workflow_name, {})
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workflow_tags = workflow_info.metadata.get("tags", []) if hasattr(workflow_info, 'metadata') else []
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if "fuzzing" in workflow_tags or "fuzz" in workflow_name.lower():
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from src.api.fuzzing import initialize_fuzzing_tracking
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initialize_fuzzing_tracking(run_id, workflow_name)
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return RunSubmissionResponse(
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run_id=run_id,
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status=flow_run.state.name if flow_run.state else "PENDING",
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workflow=workflow_name,
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message=f"Workflow '{workflow_name}' submitted successfully"
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)
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except ValueError as e:
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# Parameter validation errors
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error_response = create_structured_error_response(
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error_type="ValidationError",
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message=str(e),
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workflow_name=workflow_name,
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suggestions=[
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"Check parameter types and values",
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"Use GET /workflows/{workflow_name}/parameters for schema",
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"Ensure all required parameters are provided"
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]
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)
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raise HTTPException(status_code=400, detail=error_response)
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except Exception as e:
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logger.error(f"Failed to submit workflow '{workflow_name}': {e}")
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logger.error(f"Traceback: {traceback.format_exc()}")
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# Try to get more context about the error
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container_info = None
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deployment_info = None
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suggestions = []
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error_message = str(e)
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error_type = "WorkflowSubmissionError"
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# Detect specific error patterns
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if "deployment" in error_message.lower():
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error_type = "DeploymentError"
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deployment_info = {
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"status": "failed",
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"error": error_message
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}
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suggestions.extend([
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"Check if Prefect server is running and accessible",
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"Verify Docker is running and has sufficient resources",
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"Check container image availability",
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"Ensure volume paths exist and are accessible"
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])
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elif "volume" in error_message.lower() or "mount" in error_message.lower():
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error_type = "VolumeError"
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suggestions.extend([
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"Check if the target path exists and is accessible",
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"Verify file permissions (Docker needs read access)",
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"Ensure the path is not in use by another process",
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"Try using an absolute path instead of relative path"
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])
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elif "memory" in error_message.lower() or "resource" in error_message.lower():
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error_type = "ResourceError"
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suggestions.extend([
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"Check system memory and CPU availability",
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"Consider reducing resource limits or dataset size",
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"Monitor Docker resource usage",
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"Increase Docker memory limits if needed"
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])
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elif "image" in error_message.lower():
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error_type = "ImageError"
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suggestions.extend([
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"Check if the workflow image exists",
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"Verify Docker registry access",
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"Try rebuilding the workflow image",
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"Check network connectivity to registries"
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])
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else:
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suggestions.extend([
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"Check FuzzForge backend logs for details",
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"Verify all services are running (docker-compose up -d)",
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"Try restarting the workflow deployment",
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"Contact support if the issue persists"
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])
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error_response = create_structured_error_response(
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error_type=error_type,
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message=f"Failed to submit workflow: {error_message}",
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workflow_name=workflow_name,
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container_info=container_info,
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deployment_info=deployment_info,
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suggestions=suggestions
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)
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raise HTTPException(
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status_code=500,
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detail=error_response
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)
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@router.get("/{workflow_name}/parameters")
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async def get_workflow_parameters(
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workflow_name: str,
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prefect_mgr=Depends(get_prefect_manager)
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) -> Dict[str, Any]:
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"""
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Get the parameters schema for a workflow.
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Args:
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workflow_name: Name of the workflow
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Returns:
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Parameters schema with types, descriptions, and defaults
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Raises:
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HTTPException: 404 if workflow not found
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"""
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if workflow_name not in prefect_mgr.workflows:
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available_workflows = list(prefect_mgr.workflows.keys())
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error_response = create_structured_error_response(
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error_type="WorkflowNotFound",
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message=f"Workflow '{workflow_name}' not found",
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workflow_name=workflow_name,
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suggestions=[
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f"Available workflows: {', '.join(available_workflows)}",
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"Use GET /workflows/ to see all available workflows"
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]
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)
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raise HTTPException(
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status_code=404,
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detail=error_response
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)
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info = prefect_mgr.workflows[workflow_name]
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metadata = info.metadata
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# Return parameters with enhanced schema information
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parameters_schema = metadata.get("parameters", {})
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# Extract the actual parameter definitions from JSON schema structure
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if "properties" in parameters_schema:
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param_definitions = parameters_schema["properties"]
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else:
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param_definitions = parameters_schema
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# Add default values to the schema
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default_params = metadata.get("default_parameters", {})
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for param_name, param_schema in param_definitions.items():
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if isinstance(param_schema, dict) and param_name in default_params:
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param_schema["default"] = default_params[param_name]
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return {
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"workflow": workflow_name,
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"parameters": param_definitions,
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"default_parameters": default_params,
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"required_parameters": [
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name for name, schema in param_definitions.items()
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if isinstance(schema, dict) and schema.get("required", False)
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]
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}
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