j<SrSSKJr SSKrSSKrSSKrSSKrSSKrSSKJ r SSK J r SSK J r Jr \R"\5r\R$S5r1Skr"S S 5rS*S jrS+S jrS,SjrS-SjrS.SjrS/SjrS0SjrS1SjrS2SjrS3Sjr"SS\ 5r!"SS5r"S4Sjr#S S SSSSSSSSSS SS!. r$S5S"jr%S6S#jr&S7S$jr'S8S%jr(S9S&jr)"S'S(5r*S:S)jr+g);uServer-side execution of `gr.Workflow` graphs, for exposing each workflow subject (output node) as a regular Gradio API endpoint. The canvas executes a workflow client-side (see `js/workflowcanvas/workflow/workflow-executor.ts`). This module ports that orchestration to Python so a workflow can be run headlessly — once per subject's upstream sub-DAG — and wired into Gradio's normal `/info` + `/call` machinery. Pure graph helpers (parsing, topo-sort, subgraph extraction, free-input detection) have no Gradio/network dependencies and are unit-tested directly. The executor calls back into the workflow server-functions (`call_space`, `call_model`, `call_fn`, `fetch_dataset`) which are injected so they can be mocked in tests. ) annotationsN)deque)Callable)AnyOptionalc## SSKJn URURp2XlXlSv X!lX1lg!X!lX1lf=f7f)u3Make `blocks` the active render target so components/events register into it, without entering `Blocks.__exit__` — which would re-run `attach_load_events` (duplicating the canvas's callable-`value` load event) and recreate the running `App`. We only need the render-context primitives, restored afterward.r)ContextN)gradio.contextr root_blockblock)blocksr prev_root prev_blocks ڡC:\Users\johnn\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.13_qbz5n2kfra8p0\LocalCache\local-packages\Python313\site-packages\gradio/workflow_api.py_active_blocksrsG'#.. zM# &" '" s*A> AA  A>fileaudioimagevideogallerymodel3dc^\rSrSrSrS Sjr\S Sj5r\S Sj5r S Sjr SSjr Sr g ) WorkflowGraph<zParsed, indexed view of a schema-v2 workflow dict. Holds the three role collections (references / operators / subjects) plus a flat id index and edge adjacency, so the executor and endpoint registration don't re-scan lists repeatedly. c X[U[5(d [S5eXlUR S5=(d SUlUR US5UlUR US5UlUR US5Ul UR US5Ul 0Ul 0Ul SUR4S UR4S UR44Hup#[U5HupE[U[5(d[S US US 35eUR S5n[U[5(aU(d[S US US35eX`R;a[SU35eXPRU'X RU'M M [UR5H}upG[U[5(d[SUS 35eSHNn[UR U5[5(aUR U5(aM?[SUSU35e M g)Nz$Workflow graph must be a JSON objectnameWorkflow references operatorssubjectsedges referenceoperatorsubjectz Workflow z at index z must be an objectidz is missing an idz%Workflow contains duplicate node id: zWorkflow edge at index ) from_node_id from_port_id to_node_id to_port_idz is missing ) isinstancedict ValueErrorrawgetr _list_fieldrrr r! node_by_id role_by_id enumeratestr) selfdatarolenodesinnode_idekeys r__init__WorkflowGraph.__init__Ds$%%CD D&)7Z &*&6&6t\&J%)%5%5dK%H$($4$4T:$F !%!1!1$!@ +-*, $// *  (  & KD "%(!!T**$yjCU%VWW%%+!'3//w$yjCT%UVVoo-$'LWI%VWW+,(+/()  djj)DAa&& #:1#=O!PQQS!!%%*c22!%%**$'>qccU%STTT*cURU5=(d /n[U[5(d[SUS35e[U5$)NzWorkflow field 'z' must be a list)r.r*listr,)r5r<values rr/WorkflowGraph._list_fieldgsA #%&&/u4DEF FE{r?c U(dg[R"U5n[ U[ 5(dgUR S5S:wagU"U5$![R[4a gf=f![a gf=f)Nschema_version2)jsonloadsJSONDecodeError TypeErrorr*r+r.r,)clstextr5s r from_jsonWorkflowGraph.from_jsonns ::d#D$%% 88$ % , t9 $$i0    s#A A5A21A25 BBcB^[U4SjUR55$)Nc3J># UHoRS5T:Hv M g7f)r(Nr.).0r;r:s r -WorkflowGraph.has_incoming..sF:a55&'1:s #)anyr!)r4r:s `r has_incomingWorkflowGraph.has_incomingsF4::FFFr?cURH3nURS5U:XdMURS5U:XdM1Us $ g)Nr(r))r!r.)r4r:port_idr;s r incoming_edgeWorkflowGraph.incoming_edges;Auu\"g-!%% 2E2Pr?)r!rr0rr-rr1r N)r5dict[str, Any])r5r\r<r3return list[dict])rL str | Noner]zWorkflowGraph | None)r:r3r]bool)r:r3rYr3r]z dict | None) __name__ __module__ __qualname____firstlineno____doc__r= staticmethodr/ classmethodrMrVrZ__static_attributes__r?rrr<sA!UF $Gr?rc[U5n[RUS5nUVs0sHoD/_M nnUHQnURS5URS5pXr;dM+X;dM2X8==S- ss'XWR U5 MS [ UVs/sHoCUS:XdM UPM sn5n /n U (a\U R 5n U R U 5 X[H*n X<==S-ss'X<S:XdMU R U 5 M, U (aM\[U 5[U5:wa [S5eU $s snfs snf)zKahn's algorithm over the given node ids (edges referencing nodes outside the set are ignored). Mirrors `topoSort` in workflow-graph.ts.rr&r(z0Workflow contains a cycle and cannot be executed) setr+fromkeysr.appendrpopleftlenr,) node_idsr!idsindegnidadjr;abqueueoutr9ms r topo_sortr{s h-C MM(A &E4< =HSbHC = uu^$aeeL&91 8 HMH FMM!   (>(3CjAo3(> ?EC  MMO 1 A HMHx1} Q % 3x3x= KLL J)!> ?s D; E$Er3cNU1n[U/5nU(aUR5nURHenURS5nURS5U:XdM+X`R;dM<Xb;dMCUR U5 UR U5 Mg U(aMU$)zmAll nodes transitively feeding `target_id` (inclusive). Mirrors `buildUpstreamSubgraph` in workflow-graph.ts.r&r()rror!r.r0addrn)graph target_idincluderxcurr;srcs rupstream_node_idsrskG 9+ E mmoA%%'Cl#s*+++& C  S! % Nr?c /nURHnUSU;aMURUS5(aM)URS5=(d /nU(aUSOSnU=(d 0RS5=(d URS5=(d SnURUUUURSS 5S .5 M U$) u>The reference nodes in the subgraph that the caller must supply (no incoming edge → user-provided input). Returned in a stable order (graph declaration order) so endpoint parameters are deterministic. Each entry: `{"node": , "port": , "type": , "label": }`. r%outputsrNtype asset_typerLlabel)nodeportrr)rrVr.rn)r~ subgraph_idsresultrefrr port_types r free_inputsrsF t9L (    c$i ( ( '')$*$wqz$ZR$$V,O 0EO  !"-    Mr?r+cURS5=(d /nU(a5USRS5=(d URS5=(d S$URS5=(d S$)NinputsrrrrLrQ)r$rs rsubject_output_typersR [[ " (bF ay}}V$K L(AKVK ;;| $ ..r?c^ URVs0sHoU_M snm SU 4SjjnURHEnURS5URS5pTUT ;dM,UT ;dM4U"U5T U"U5'MG 0n/nURH;nU"US5n X;a/Xi'UR U 5 XiR U5 M= UV s/sHoU PM sn $s snfs sn f)uGroup subjects by weakly-connected component (edges undirected), mirroring `countSubgraphs` in workflow-graph.ts. Each component is one independent pipeline, so all of its outputs become a *single* API endpoint that returns a tuple — matching how a Gradio Space with multiple outputs exposes one endpoint, not one per output. Groups are ordered by the declaration order of their first subject; subjects within a group keep declaration order (this fixes the output tuple order).c|>UnTUU:waTUnTUU:waMTUU:waUTUsTU'nTUU:waMU$Nri)xrootparents rfindsubject_groups..finds]Tld"$>GDM"  FL LL  G$ " &+ +UT4LU ++/D. ,s C C%c`[5nUHnU[XS5-nM [X5$)zThe free inputs feeding a subject group: the union of each subject's upstream subgraph, deduped and returned in graph declaration order (so a reference shared by two outputs in the group becomes a single parameter).r%)rlrr)r~groupsub_idsr$s rgroup_free_inputsrs4G$UDM:: u &&r?cUcgU[;a2[U[5(aU$[U[5(aSU0$U$U$)aConvert an endpoint input value into the shape the workflow server functions expect. Media ports arrive as a local filepath (from the output component) or a FileData-like dict; wrap a bare path as `{"path": ...}` so `call_space` runs it through `handle_file`. Scalars pass through.Npath)MEDIA_PORT_TYPESr*r+r3)rBrs r_to_argrsH  }$$ eT " "L eS ! !E? " Lr?r`cUcgU[;aN[U[5(aURS5$[U[5=(a SU;=(d SU;$US;a[U[5$US:Xa2[U[ [ 45=(a [U[5(+$US:Xa[U[5$US:Xa[U[[45$g ) NF)zhttp://zhttps://zblob:zdata:/rurl)rLmarkdownnumberbooleanrGT) rr*r3 startswithr+intfloatr`rA)itemrs r_output_matches_port_typer$s |$$ dC ??#QR R$%K6T>+JUd]K(($$$H$e -Ljt6L2LLI$%%F$t -- r?cURS5n[U[5(aU[U5:aX$OSnO-US:Xa[U5S:aSnOU[U5:aX!OSnUb"[ XPRSS55(aU$UH'n[ X`RSS55(dM%Us $ UbU$U(aUS$S$)zSelect which element of a multi-output response feeds a given output port. Mirrors `pick_response_item` in workflow-executor.ts: honor an explicit `output_index`, else position, else shape-match by port type. output_indexNrkrrr)r.r*rrpr)r port_index output_data total_portsrprimaryrs r_pick_response_itemr6s 88N+L,$$/;c+>N/N++TX  c+.2-7#k:J-J+)PT8((6SUBVWW $T88FB+? @ @K(;q>2d2r?c[U[5(a2URS5=(d URS5=(d U$U$)uLNormalize a value coming out of the executor for the endpoint's output component. File dicts → local path (preferred) or url; scalars pass through. The internal `{path,url,is_file}` shape (from `call_space`) is consumable by downstream operators as-is, so this only runs for the final subject value handed to the output component.rr)r*r+r.)rBs r _from_outputrPs8 %yy =EIIe$4== Lr?c\rSrSrSrSrg)WorkflowExecutionErrori`zaRaised when a node in the workflow fails; the message is suitable for surfacing to an API caller.riN)rarbrcrdrerhrir?rrr`s#r?rc\rSrSrSrSSjrSSSjjrSSSjjrSSjrSSjr SS jr SS jr SS jr SS jr SS jrSSjrSSjrSSjrSSjrSrg)WorkflowExecutorieuHRuns a subject's upstream sub-DAG server-side. `callers` maps operator kinds to the workflow server-functions, each with the signature `(data: list, request, token) -> str` (a JSON string, either a list of outputs or an `{"error": ...}` dict) — exactly the existing `call_space` / `call_model` / `call_fn` / `fetch_dataset`. cXlX lgr)r~callers)r4r~rs rr=WorkflowExecutor.__init__ns   r?Nc0URU/X#U5S$)uExecute the subgraph feeding `subject_id`. `inputs` maps free-input reference node id → value. Returns the subject's output value.r)run_many)r4 subject_idrrequesttokens rrunWorkflowExecutor.runrs}}j\6EB1EEr?cvURnUH0nURRU5S:wdM$[SU35e [ 5nUHnU[ XV5-nM [ [U5UR5n0n X0l X@l UHn URXU5 M /n UHnURURS5=(d /n U (dU RS5 MDU RU05RU SS5n U R[U 55 M U $)aExecute the combined subgraph feeding all `subject_ids` in a single pass and return one output value per subject, in order. Nodes shared between outputs (e.g. an operator with two outputs wired to two subjects) run exactly once rather than once per subject.r$zNo such workflow output: rNrr%)r~r1r.rrlrr{rAr!_request_token _execute_noder0rnr)r4 subject_idsrrrr~sidrrdata_mapr:rin_portsrBs rrWorkflowExecutor.run_many}s" C##C(I5,/H-NOO EC (4 4G$w-5.0  G   w& 9 C'',00:@bHt$LLb)--hqk$.?@E NN<. / r?cURn0nURS5=(d /HnURUSUS5nU(a.URUS05RUS5XES'MPURS5=(d 0nURUSURS55XES'M U$)Nrr%r&r'r5 default_value)r~r.rZ)r4rrr~resolvedredger5s r_resolve_inputs WorkflowExecutor._resolve_inputss #%HHX&,",D&&tDz4:>D'/||D4H"'M'Q'Q((d$xx'-2'+xxT DHH_(+FtfG)TU UT==$++> K( fd # #6(9 <(*::g&*)  )- -#FD11v?x?r?c 0nURS5=(d /n[U5H upg[XvU[U55XGS'M" XCUS'g)Nrr%)r.r2rrp)r4rrrr out_portsr8rs r _map_outputsWorkflowExecutor._map_outputssW"$HHY'-2  +GA!4c)n"F: , &dr?cxURX5nURX5 URS5=(d /Vs/sH oCUSPM nnURS5=(d SnURSURS5U[R "U5/5nUR XU5 gs snf)Nrr%endpointz/predictspacespace_idrrr.rrGdumpsr)r4rrrpargsrrs rrWorkflowExecutor._run_space s''7 d%+/88H+=+C+CD+Ca4!+CD88J'5:jj dhhz*Hdjj6FG  $X6 EsB7cURX5nURX5 URS5=(d SnURS5nU(aiURS5=(d /Vs0sHnUSU;dMUSX6S_M nnURS5U[R"U5SU/nOsURS5=(d /Vs/sH ocUSPM n nURS5=(d Sn URS5U [R"U 5SU/nUR S U5n UR XU5 gs snfs snf) Nproviderautorrr%model_id pipeline_tagztext-generationr)rrr.rGrrr) r4rrrrrrkwargs call_datartagrs rrWorkflowExecutor._run_modelsN''7 d%88J'1688J' (+1r11AT7h&+$D'**1  $ 6" I04xx/A/GR/GH/G!QtW%/GDH((>*?.?C*-sDJJt4DdHUIjj)4  $X6#Is1 EEEcBURX5nURX5 URS5=(d /Vs/sH oCUSPM nnURSURS5[R "U5/5nUR XU5 gs snf)Nrr%rr)r4rrrrrrs rrWorkflowExecutor._run_fn.s''7 d%+/88H+=+C+CD+Ca4!+CDjj 48H'IJ  $X6EsBc URX5nURS5=(d Sn[S[U55nUR SURS5URS5=(d SURS5=(d S[U5S /5nU(aUSO0n[U[5(aURS 5OSn[U[5(aU(aUSO0O0n 0n URS 5=(d /HLn U RU RS 55n [U [5(a S U ;aSU S 0n XU S'MN XUS'g![[ 4a SnGNCf=f)N row_indexrr dataset_iddataset_configdefault dataset_splittrain1rowsrrrrr%) rr.maxrrJr,rr3r*r+rA) r4rrrr-offsetrrrrowrrrBs rrWorkflowExecutor._run_dataset5s^''7ll;',1 CH%Fjj &)*7i)4WF     $/QB%/%=%=vzz&!4+5dD+A+A$tAwBr!#HHY'-2-DGGDHHW-.E%&&5E>e -!&4:  . &d3:& F sE00FF)rrrr~)r~rrdict[str, Callable])NN) rr3rr\rrrrr]r) r list[str]rr\rrrrr]z list[Any])rr+rdict[str, dict[str, Any]]r]r\)r:r3rrrr\r]None)rr+rrrr\r]r)rr+rrr]r)rr+rr\r]r)rr3r5rAr]rA)rr+rrArrr]r)rarbrcrdrer=rrrrrrrrrrrrrrhrir?rrresD F F F F  F F $$$ $  $  $P$=  ,,,, ,  ,@&&$=&GU& & & @ & &'+ &7P &  &7727&r?rc[R"SSU=(d SR5R55RS5nU=(d S$)Nz [^a-z0-9]+_rr)resubstriplower)rslugs r_slugifyr'[s@ 66-u{&9&9&;&A&A&C D J J3 OD  :r?rfilepathzlist[filepath]z list[list]) rLrrrrrrr3drr dataframerGc## [5nUHHn[USRSS55nX1;a US3nX1;aM URU5 X#4v MJ g7f)zYield (group, api_name) with the dedup used at registration, so the described endpoints line up with the ones actually registered. The slug is derived from the group's first subject label.rroutputr!N)rlr'r.r})rusedrapi_names r_group_slug_iterr/ss^UDE!HLL(;<"1~H o s =AAc8SSKJn U"[S//055$)zWhether a subgraph endpoint takes a `gr.OAuthToken`, asked of the same builder that registers them so the panel can't drift from `/info`.roauth_token_requirementcgrririr?r3_endpoint_oauth_token_requirement..sdr?) gradio.utilsr2_build_endpoint_fnr1s r!_endpoint_oauth_token_requirementr8s5 "#5lBB#O PPr?c/n[5n[[U55Hup4[X5nUR SU-USR SS5S.U(aSU0O0E[ U5VVs/sH,upgUSSU3US[R USS 5S .PM. snnUVs/sH?nUR SS5[U5[R [U5S 5S .PMA snS .E5 M U$s snnfs snf) zDescribe each subject endpoint for the frontend "View API" panel: `api_name`, label, parameters (free inputs), and the return type. Mirrors the schema that `register_workflow_endpoints` exposes via `/info`.rrrr,)r.r oauth_tokenin_rr3)rparameter_namer python_type)rrr=) parametersreturns) r8r/rrrnr.r2_PY_TYPEr) r~ endpointsr:rr.freesr8fr$s rdescribe_workflow_apirDs*I35K+N5,AB!%/(Nqgx8 4?M;/B !*% 0!1 "#7,/s) !& '/||AfIu'E  !1"$) $) ")Wh!? 3G <'/||/8%($)   C8 + s 13D+ADcSSKnU=(d UnUS:XaURUSSS9$US:XaURUSSS9$US:XaURUSS 9$US ;aUR USS 9$US :XaUR USSS9$US :XaUR USS 9$US :XaURUSS 9$US:XaURUSS 9$US:XaURUSS 9$US:XaURUSS 9$URUSS 9$)a#Map a workflow port type to a Gradio component for the API schema. Real components are used (not `gr.api` type hints) so multimodal I/O round-trips via their proven `api_info` / `FileData` handling. Created unrendered; the caller renders them inside a hidden container so they reach `/info`.rNrr(F)rrrenderrr)rrF)rr)rrrr*rrG) gradioImageAudioVideoModel3DFileNumberCheckbox DataframeGalleryJSONTextbox)rrgrs rport_to_componentrTs9   YEGxxe*UxCCGxxe*UxCCGxxeEx22%%zzez44FwwUEwBBHyyuUy33I{{u{55K||%|66Izzez44FwwU5w11 ::E%: 00r?c d^^^^SSKJn SSKJn UUUU4Sjn[ [ T55Vs/sH5n[ R"SU3[ RR5PM7 nnUR[ R"S[ RRS[US95 UR[ R"S [ RRS[US95 [ R"U5Ul [U[US .Ul U$s snf) aGBuild the callable backing one subgraph endpoint. `subject_ids` are all the outputs of the subgraph; with more than one the endpoint returns a tuple (matching its multiple output components), otherwise a single value. Execution re-reads the *current* graph via `get_graph()` so operator and wiring edits take effect without a restart. The advertised signature (positional input per free reference, plus `request`/`token`) is synthesized so Gradio's `special_args` injects the request and OAuth token, letting the executor reuse the workflow's token resolution for downstream calls. r) OAuthToken)Requestc>UGtpnT "5nUc [S5e[[TU55n[UT5R T XRU5n[ U5S:XaUS$[ U5$)NzWorkflow graph is unavailablerkr)rr+ziprrrptuple) r input_valuesrrr~rresultsrfree_ids get_graphrs rr$_build_endpoint_fn..endpointst(,%  =()HI Ic(L12"5'2;; % !\Q.wqzBE'NBr?r;rN)r annotationr)rr) gradio.oauthrVgradio.route_utilsrWrangerpinspect ParameterPOSITIONAL_OR_KEYWORDrnr Signature __signature____annotations__) r^rr]rrVrWrr8paramss ```` rr7r7s (* C Cs8}%%A Cs)W%6%6%L%LM%  MM     3 3(   MM     3 3 +  %..v6HG$*% H O3sDKK dkk<6 ?CL#C   N LL=L M Ns)AA65A6)rnrorpr rr^N)r^%Callable[[], Optional[WorkflowGraph]]rr)r]r)r]r)r~rr]r) rarbrcrdrer=rvrsrtrurhrir?rrlrls6  '9 '% '$ *MX$r?rlc>[XU5nUR5 U$)zCreate a `WorkflowEndpointManager` and register the initial endpoint set from the current graph. Returns the manager so the caller can `.sync()` it again whenever the graph is saved.)rlrv)r r^rmanagers rregister_workflow_endpointsrvs&fAG LLN Nr?)rqrr!r^r]r)r~rrr3r]set[str])r~rrrr]r^)r$r+r]r3)r~rr]list[list[dict]])r~rrr^r]r^)rBrrr3r]r)rrrr3r]r`) rr+rrrrArrr]r)rBrr]r)rr3r]r3)rr)r]r_)r~rr]r^)rr3rr3)r^rrrr]rrr)r^rrrr]rl),re __future__r contextlibrdrGloggingr" collectionsrcollections.abcrtypingrr getLoggerrarcontextmanagerrrrr{rrrrrrrrrrrrr'r@r/r8rDrTr7rlrrir?rrs~ #  $   8 $ ##*MLL^808/ ,F'  $3 33.23AD334 #Y# n&n&l        " Q"J1>74777! 7tm$m$` 4 !  r?