"""
Concept E — many subjects → many REST endpoints
===============================================

A workflow isn't just a canvas — it's an API. Each *disconnected* pipeline
(weakly-connected group of nodes ending in a subject) becomes ONE REST endpoint,
named after its first subject. Free references become the endpoint's parameters.

This graph has two independent pipelines, so it exposes two endpoints:
    /word_count   (text  → number)
    /fahrenheit   (number → number)

Once launched:
    curl http://127.0.0.1:7860/gradio_api/call/word_count -s \
      -H "Content-Type: application/json" -d '{"data": ["hello there friend"]}'

This file also prints its endpoint schema at startup via `describe_workflow_api`.

Run it:   python concepts/e_multi_endpoint_api.py
"""

import json
import os

import gradio as gr


def word_count(text: str) -> int:
    return len(text.split())


def to_fahrenheit(celsius: float) -> float:
    return round(celsius * 9 / 5 + 32, 1)


def _ref(nid, label, ptype, default, y):
    return {"id": nid, "role": "reference", "label": label, "asset_type": ptype,
            "inputs": [{"id": "in", "label": label, "type": ptype}],
            "outputs": [{"id": "out", "label": label, "type": ptype}],
            "data": {"out": default}, "x": 60, "y": y, "width": 200, "height": 90}


def _op(nid, fn, in_type, out_type, y):
    return {"id": nid, "role": "operator", "kind": "fn", "fn": fn, "label": fn,
            "inputs": [{"id": "in_0", "label": "input", "type": in_type, "required": True}],
            "outputs": [{"id": "out_0", "label": "output", "type": out_type}],
            "data": {}, "x": 320, "y": y, "width": 200, "height": 90}


def _sub(nid, label, ptype, y):
    return {"id": nid, "role": "subject", "label": label, "asset_type": ptype,
            "inputs": [{"id": "in", "label": label, "type": ptype}],
            "outputs": [{"id": "out", "label": label, "type": ptype}],
            "data": {}, "x": 580, "y": y, "width": 200, "height": 120}


def _edge(eid, s, sp, t, tp, ty):
    return {"id": eid, "from_node_id": s, "from_port_id": sp,
            "to_node_id": t, "to_port_id": tp, "type": ty}


GRAPH = {
    "schema_version": "2", "name": "Two Endpoints",
    "references": [_ref("ref_text", "Text", "text", "hello there friend", 60),
                   _ref("ref_c", "Celsius", "number", 20, 260)],
    "operators": [_op("op_wc", "word_count", "text", "number", 60),
                  _op("op_f", "to_fahrenheit", "number", "number", 260)],
    "subjects": [_sub("sub_wc", "Word count", "number", 60),
                 _sub("sub_f", "Fahrenheit", "number", 260)],
    "edges": [_edge("e1", "ref_text", "out", "op_wc", "in_0", "text"),
              _edge("e2", "op_wc", "out_0", "sub_wc", "in", "number"),
              _edge("e3", "ref_c", "out", "op_f", "in_0", "number"),
              _edge("e4", "op_f", "out_0", "sub_f", "in", "number")],
}

GRAPH_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "e_multi_endpoint_api.json")
with open(GRAPH_PATH, "w", encoding="utf-8") as f:
    json.dump(GRAPH, f, indent=2)

demo = gr.Workflow(GRAPH_PATH, bind={"word_count": word_count, "to_fahrenheit": to_fahrenheit})

if __name__ == "__main__":
    from gradio.workflow_api import WorkflowGraph, describe_workflow_api
    for ep in describe_workflow_api(WorkflowGraph.from_json(json.dumps(GRAPH))):
        print(f"  {ep['api_name']:14s} params={[p['type'] for p in ep['parameters']]} "
              f"returns={[r['type'] for r in ep['returns']]}")
    demo.launch()