from __future__ import annotations

import json
from pathlib import Path

import gradio as gr

from json_semval.pipeline import run_validation


def load_fixtures() -> tuple[str, str]:
    try:
        schema = Path("tests/fixtures/sample_schema.json").read_text(encoding="utf-8")
        bad = Path("tests/fixtures/sample_bad.json").read_text(encoding="utf-8")
        return schema, bad
    except Exception:
        example_schema = '{"type":"object","properties":{"name":{"type":"string"}},"required":["name"]}'
        example_json = '{"name":"Alice"}'
        return example_schema, example_json


def infer(
    schema_text: str, json_text: str, backend: str, apply_minimal: bool
) -> tuple[str, str, str]:
    try:
        schema = json.loads(schema_text)
        payload = json.loads(json_text)
    except Exception as e:
        return "[]", f"Invalid input JSON: {e}", json_text
    report = run_validation(schema, payload, apply_fixes=apply_minimal, backend=backend)
    rule_errors = json.dumps(report.get("rule_errors", []), indent=2)
    ml_preds = json.dumps(report.get("ml_predictions", []), indent=2)
    corrected = json.dumps(report.get("corrected_json", payload), indent=2)
    return rule_errors, ml_preds, corrected


EXAMPLES = {
    "Example 1: Fixtures (bad)": (
        (
            Path("tests/fixtures/sample_schema.json").read_text(encoding="utf-8")
            if Path("tests/fixtures/sample_schema.json").exists()
            else '{"type":"object","properties":{"age":{"type":"integer"},"start_date":{"type":"string","format":"date"},"active":{"type":"boolean"},"status":{"type":"string","enum":["pending","approved","rejected"]}},"required":["age","start_date","active","status"]}'
        ),
        (
            Path("tests/fixtures/sample_bad.json").read_text(encoding="utf-8")
            if Path("tests/fixtures/sample_bad.json").exists()
            else '{"age":"twenty five","active":"yes","start_date":"15 Jan 2024","status":"pendng"}'
        ),
    ),
    "Example 2: Minimal valid": (
        '{"type":"object","properties":{"name":{"type":"string"}},"required":["name"]}',
        '{"name":"Alice"}',
    ),
    "Example 3: Enum/date mix": (
        '{"type":"object","properties":{"status":{"type":"string","enum":["pending","approved","rejected"]},"d":{"type":"string","format":"date"}},"required":["status","d"]}',
        '{"status":"Pendng","d":"01/02/2024"}',
    ),
}

with gr.Blocks(title="JSON Semantic Validator") as demo:
    gr.Markdown(
        """
    # JSON Semantic Validator
    Hybrid rules + tiny ML to validate and auto-fix JSON against a schema.
    """
    )

    with gr.Row():
        schema_in = gr.Code(label="JSON Schema", language="json")
        json_in = gr.Code(label="JSON Payload", language="json")

    with gr.Row():
        backend = gr.Dropdown(
            ["rules-only", "local", "onnx"], value="rules-only", label="Backend"
        )
        apply_minimal = gr.Checkbox(value=True, label="Apply minimal fixes")
        example_dd = gr.Dropdown(list(EXAMPLES.keys()), label="Load Example")
        load_btn = gr.Button("Load Fixtures")
        run_btn = gr.Button("Run Validation")

    with gr.Row():
        rule_errors_out = gr.Code(label="Rule Errors", language="json")
        ml_preds_out = gr.Code(label="ML Predictions", language="json")
        corrected_out = gr.Code(label="Corrected JSON", language="json")
    validity_md = gr.Markdown(visible=True)

    def _backend_value(b: str) -> str:
        if b == "rules-only":
            return "local"  # we will set apply_minimal False when executing
        return b

    def on_load() -> tuple[str, str]:
        return load_fixtures()

    def on_example_select(label: str) -> tuple[str, str]:
        pair = EXAMPLES.get(label)
        if not pair:
            return "", ""
        return pair

    def on_run(
        schema_text: str, json_text: str, b: str, do_fix: bool
    ) -> tuple[str, str, str, str]:
        # Compute rules-only and hybrid validity to show a delta badge
        validity_text = ""
        try:
            schema = json.loads(schema_text)
            payload = json.loads(json_text)
            rules_only = run_validation(
                schema, payload, apply_fixes=False, backend="rules-only"
            )
            hybrid = run_validation(
                schema,
                payload,
                apply_fixes=(False if b == "rules-only" else do_fix),
                backend=_backend_value(b),
            )
            r_ok = 1 if rules_only.get("valid") else 0
            h_ok = 1 if hybrid.get("valid") else 0
            delta = (h_ok - r_ok) * 100
            validity_text = f"**Validity** — Rules-only: {r_ok*100}% · Hybrid: {h_ok*100}% · Δ: {delta:+d}%"
            # Return details based on the hybrid run to keep UX consistent
            rule_errors = json.dumps(hybrid.get("rule_errors", []), indent=2)
            ml_preds = json.dumps(hybrid.get("ml_predictions", []), indent=2)
            corrected = json.dumps(hybrid.get("corrected_json", payload), indent=2)
            return rule_errors, ml_preds, corrected, validity_text
        except Exception as e:
            return "[]", f"Invalid input JSON: {e}", json_text, ""

    load_btn.click(fn=on_load, outputs=[schema_in, json_in])
    example_dd.change(
        fn=on_example_select, inputs=[example_dd], outputs=[schema_in, json_in]
    )
    run_btn.click(
        fn=on_run,
        inputs=[schema_in, json_in, backend, apply_minimal],
        outputs=[rule_errors_out, ml_preds_out, corrected_out, validity_md],
    )

    # Disable the "Apply minimal fixes" checkbox when backend == rules-only
    def on_backend_change(b: str, current: bool):
        if b == "rules-only":
            return gr.update(value=False, interactive=False)
        return gr.update(interactive=True)

    backend.change(
        fn=on_backend_change, inputs=[backend, apply_minimal], outputs=apply_minimal
    )

if __name__ == "__main__":
    demo.launch(share=True)