"""S7 inference tests (METADATA-105/106, 45, 55, 70, SETTINGS-45)."""

import base64
import json
import os
import shutil
import tempfile

import pytest
from fastapi.testclient import TestClient

import backend.db as db_mod


@pytest.fixture
def env(monkeypatch):
    tmp = tempfile.mkdtemp(prefix="inf_")
    os.environ["SLAB_DB_PATH"] = os.path.join(tmp, "i.db")
    os.environ["SLAB_IMAGES_DIR"] = tmp
    os.environ["WOO_BASE_URL"] = "https://x.com"
    os.environ["SLAB_AES_KEY"] = base64.b64encode(b"0123456789abcdef0123456789abcdef").decode()
    db_mod._reset_for_tests()
    from backend.crypto import encrypt

    conn = db_mod.init_db()
    conn.execute("INSERT OR REPLACE INTO settings(key,value,value_enc) VALUES ('inference_base_url',NULL,?)", (encrypt("https://infer.example.com"),))
    conn.execute("INSERT OR REPLACE INTO settings(key,value,value_enc) VALUES ('inference_api_key',NULL,?)", (encrypt("sk-test"),))
    conn.execute("INSERT OR REPLACE INTO settings(key,value,value_enc) VALUES ('inference_model','gpt-test',NULL)")
    conn.commit()
    from backend.app import app

    yield TestClient(app), conn
    db_mod._reset_for_tests()
    shutil.rmtree(tmp, ignore_errors=True)


def test_prompt_seed_and_revision(env):
    client, conn = env
    r = client.get("/api/v1/prompts/call1")
    assert r.status_code == 200
    body = r.json()
    assert body["active_content"] is not None
    assert len(body["revisions"]) == 1  # seed only

    r = client.post("/api/v1/prompts/call1/revisions", json={"content": "revised prompt v2"})
    assert r.status_code == 201
    new_rev = r.json()["id"]
    # new revision becomes active
    r = client.get("/api/v1/prompts/call1")
    assert r.json()["active_revision_id"] == new_rev
    assert r.json()["active_content"] == "revised prompt v2"
    assert len(r.json()["revisions"]) == 2

    # activate back to seed
    seed_rev = 1
    r = client.post("/api/v1/prompts/call1/activate", json={"revision_id": seed_rev})
    assert r.status_code == 200
    assert client.get("/api/v1/prompts/call1").json()["active_content"] is not None


def test_call1_confidence_gate(env, monkeypatch):
    client, conn = env
    # enable inference
    conn.execute("INSERT OR REPLACE INTO settings(key,value,value_enc) VALUES ('inference_enabled','true',NULL)")
    conn.execute("INSERT INTO woo_categories(id,name,slug,parent_id,woo_id) VALUES (1,'Black Walnut','black-walnut',NULL,100)")
    conn.execute("INSERT INTO slabs(id,sku,length_in,thickness_in,status,server_rev,client_rev,created_at,updated_at) VALUES ('s1','SLAB-01',100,2,'calibrated',1,1,'t','t')")
    conn.execute("INSERT INTO slab_photos(id,slab_id,seq,processed_blob,created_at) VALUES ('p1','s1',1,X'89504E47','t')")
    conn.commit()
    from backend.inference import InferenceClient

    def fake_chat(self, messages):
        return json.dumps({
            "species_id": 1, "species_confidence": 0.9,
            "wood_category_ids": [1], "wood_category_confidence": 0.5,  # below threshold
            "grade_term_id": 7, "grade_confidence": 0.3,               # below
            "figure_term_ids": [2], "figure_confidence": 0.8,          # at/above
        })
    monkeypatch.setattr(InferenceClient, "chat", fake_chat)
    r = client.post("/api/v1/slabs/s1/inference/call1")
    assert r.status_code == 200, r.text
    s = r.json()["suggestions"]
    assert s.get("species_id") == 1
    assert s.get("figure_term_ids") == [2]
    assert "wood_category_ids" not in s  # gated out
    assert "grade_term_id" not in s       # gated out
    # inference_log written with call=1
    assert conn.execute("SELECT call FROM inference_log").fetchone()["call"] == 1


def test_call2_stores_description(env, monkeypatch):
    client, conn = env
    conn.execute("INSERT OR REPLACE INTO settings(key,value,value_enc) VALUES ('content_llm_enabled','true',NULL)")
    conn.execute("INSERT INTO slabs(id,sku,length_in,thickness_in,width_avg_in,bdft,title,status,server_rev,client_rev,created_at,updated_at) VALUES ('s1','SLAB-01',100,2,20,18,'Walnut Slab','ready',1,1,'t','t')")
    conn.execute("INSERT INTO slab_photos(id,slab_id,seq,processed_blob,created_at) VALUES ('p1','s1',1,X'89504E47','t')")
    conn.commit()
    from backend.inference import InferenceClient

    monkeypatch.setattr(InferenceClient, "chat", lambda self, messages: "<p>A fine walnut slab.</p>")
    r = client.post("/api/v1/slabs/s1/inference/call2")
    assert r.status_code == 200, r.text
    assert r.json()["description"] == "<p>A fine walnut slab.</p>"
    row = conn.execute("SELECT description, content_source FROM slabs WHERE id='s1'").fetchone()
    assert row["description"] == "<p>A fine walnut slab.</p>"
    assert row["content_source"] == "llm"


def test_inference_off_call1_409(env):
    client, conn = env
    conn.execute("INSERT INTO slabs(id,sku,status,server_rev,client_rev,created_at,updated_at) VALUES ('s1','SLAB-01','calibrated',1,1,'t','t')")
    conn.commit()
    r = client.post("/api/v1/slabs/s1/inference/call1")
    assert r.status_code == 409  # inference_enabled default off


def test_server_description_inference_off(env):
    from backend.inference import server_description

    d = server_description({"title": "Figured Walnut Slab", "length_in": 114.0, "width_avg_in": 24.0, "thickness_in": 1.75, "bdft": 18.4})
    assert "114.0in" in d and "18.4" in d


def test_test_inference_unconfigured(env):
    client, conn = env
    conn.execute("UPDATE settings SET value_enc=NULL WHERE key='inference_api_key'")
    conn.commit()
    r = client.post("/api/v1/admin/test-inference")
    assert r.status_code == 200
    assert r.json()["ok"] is False and r.json()["mode"] == "fail"


def _rgba_png(w: int, h: int) -> bytes:
    """RGBA PNG: opaque body + transparent top-left block (capture-like alpha)."""
    from PIL import Image
    import io
    im = Image.new("RGBA", (w, h), (10, 20, 30, 255))
    for y in range(min(80, h)):
        for x in range(min(80, w)):
            im.putpixel((x, y), (0, 0, 0, 0))
    buf = io.BytesIO()
    im.save(buf, format="PNG")
    return buf.getvalue()


def test_fit_long_edge_png_downscales_over_1024():
    """METADATA-35: Call 1 images long edge <= 1024; never upscale; keep alpha."""
    from backend.inference import fit_long_edge_png
    from PIL import Image
    import io

    src = _rgba_png(800, 1067)  # inventory 3:4, long edge 1067
    out = fit_long_edge_png(src, max_long_edge=1024)
    im = Image.open(io.BytesIO(out)).convert("RGBA")
    assert max(im.size) == 1024
    assert min(im.size) < 1024
    w, h = im.size
    assert abs((w / h) - (800 / 1067)) < 0.02
    # Transparent block still transparent near origin after downscale
    assert im.getpixel((0, 0))[3] == 0
    assert im.getpixel((5, 5))[3] == 0


def test_fit_long_edge_png_leaves_small_unchanged_dims():
    from backend.inference import fit_long_edge_png
    from PIL import Image
    import io

    src = _rgba_png(400, 300)
    out = fit_long_edge_png(src, max_long_edge=1024)
    im = Image.open(io.BytesIO(out))
    assert im.size == (400, 300)


def test_call1_sends_long_edge_at_most_1024(env, monkeypatch):
    """METADATA-35 end-to-end: messages to the model use <=1024 long edge."""
    client, conn = env
    conn.execute("INSERT OR REPLACE INTO settings(key,value,value_enc) VALUES ('inference_enabled','true',NULL)")
    conn.execute(
        "INSERT INTO slabs(id,sku,length_in,thickness_in,status,server_rev,client_rev,created_at,updated_at) "
        "VALUES ('s1','SLAB-01',100,2,'calibrated',1,1,'t','t')"
    )
    big = _rgba_png(800, 1067)
    conn.execute(
        "INSERT INTO slab_photos(id,slab_id,seq,processed_blob,created_at) VALUES ('p1','s1',1,?, 't')",
        (big,),
    )
    conn.commit()
    from backend.inference import InferenceClient
    from PIL import Image
    import io

    captured = {}

    def fake_chat(self, messages):
        captured["messages"] = messages
        return json.dumps({"species_id": 1, "species_confidence": 0.9})

    monkeypatch.setattr(InferenceClient, "chat", fake_chat)
    r = client.post("/api/v1/slabs/s1/inference/call1")
    assert r.status_code == 200, r.text
    content = captured["messages"][0]["content"]
    imgs = [p for p in content if isinstance(p, dict) and p.get("type") == "image_url"]
    assert len(imgs) == 1
    url = imgs[0]["image_url"]["url"]
    assert url.startswith("data:image/png;base64,")
    raw = base64.b64decode(url.split(",", 1)[1])
    im = Image.open(io.BytesIO(raw))
    assert max(im.size) <= 1024