scripts/generate_example_cases.py

"""Generate reproducible end-to-end example cases for the final report."""

from __future__ import annotations

import json
import re
import sys
from pathlib import Path

PROJECT_ROOT = Path(__file__).resolve().parents[1]
sys.path.append(str(PROJECT_ROOT))

from app.app import predict_listing

REPORT_PATH = PROJECT_ROOT / "reports/example_cases.md"
JSON_PATH = PROJECT_ROOT / "reports/example_cases.json"


CASES = [
    {
        "case_id": "clean_vehicle_manual_check",
        "description": "Clean baseline case using verified vehicle data and manual no-damage fallback.",
        "args": {
            "make": "BMW",
            "model": "3 Series",
            "production_year": 2019,
            "mileage_km": 64_000,
            "power_hp": 184,
            "fuel_category": "Gasoline",
            "transmission": "Automatic",
            "custom_make": "",
            "body_type": "Sedan",
            "seller_is_dealer": False,
            "has_warranty": True,
            "nr_prev_owners": 1,
            "seller_notes": "Fresh service, non-smoking vehicle, winter tires included.",
            "front_image": None,
            "side_image": None,
            "rear_image": None,
            "interior_image": None,
            "use_image_model": False,
            "vision_mode": "OpenAI Vision",
            "manual_damage_labels": [],
            "prompt_strategy": "Sales-optimized but honest",
        },
    },
    {
        "case_id": "minor_damage_manual_check",
        "description": "Minor visible-damage case using controlled scratch and dent labels.",
        "args": {
            "make": "Volkswagen",
            "model": "Golf",
            "production_year": 2018,
            "mileage_km": 50_000,
            "power_hp": 150,
            "fuel_category": "Gasoline",
            "transmission": "Manual",
            "custom_make": "",
            "body_type": "Hatchback",
            "seller_is_dealer": False,
            "has_warranty": False,
            "nr_prev_owners": 1,
            "seller_notes": "Fresh service, summer and winter tires included.",
            "front_image": None,
            "side_image": None,
            "rear_image": None,
            "interior_image": None,
            "use_image_model": False,
            "vision_mode": "OpenAI Vision",
            "manual_damage_labels": ["scratch", "dent"],
            "prompt_strategy": "Sales-optimized but honest",
        },
    },
    {
        "case_id": "local_cv_severe_damage",
        "description": "Local CV baseline case using a held-out severe-damage sample image.",
        "args": {
            "make": "Volkswagen",
            "model": "Golf",
            "production_year": 2018,
            "mileage_km": 50_000,
            "power_hp": 150,
            "fuel_category": "Gasoline",
            "transmission": "Manual",
            "custom_make": "",
            "body_type": "Hatchback",
            "seller_is_dealer": False,
            "has_warranty": False,
            "nr_prev_owners": 1,
            "seller_notes": "Vehicle has visible exterior damage in the uploaded image.",
            "front_image": str(PROJECT_ROOT / "data/samples/cv_subset/severe_damage_1.jpg"),
            "side_image": None,
            "rear_image": None,
            "interior_image": None,
            "use_image_model": True,
            "vision_mode": "Local CV baseline",
            "manual_damage_labels": [],
            "prompt_strategy": "Neutral factual",
        },
    },
]


def strip_html(text: str) -> str:
    """Convert short app HTML snippets into readable report text."""
    text = re.sub(r"<br\\s*/?>", "\n", text)
    text = re.sub(r"<[^>]+>", " ", text)
    text = text.replace("&nbsp;", " ")
    text = re.sub(r"\\s+", " ", text)
    return text.strip()


def format_chf_amount(value: float | int | None) -> str:
    """Format a value that is already expressed in CHF."""
    if value is None:
        return "n/a"
    return f"CHF {float(value):,.0f}".replace(",", "'")


def run_case(case: dict) -> dict:
    """Run one app case and collect structured outputs."""
    seller_cockpit, metrics, sensitivity, flow, vision, listing, explanation, details_json, prompt = predict_listing(**case["args"])
    details = json.loads(details_json)
    return {
        "case_id": case["case_id"],
        "description": case["description"],
        "input": {
            key: value
            for key, value in case["args"].items()
            if key not in {"front_image", "side_image", "rear_image", "interior_image"}
        },
        "images": {
            "front": case["args"]["front_image"],
            "side": case["args"]["side_image"],
            "rear": case["args"]["rear_image"],
            "interior": case["args"]["interior_image"],
        },
        "base_price_chf": details["base_price_chf"],
        "discount_chf": details["discount_chf"],
        "adjusted_price_chf": details["adjusted_price_chf"],
        "damage_score": details["damage_score"],
        "damage_model": details["damage_model"],
        "detected_damages": details["detected_damages"],
        "image_quality": details["image_quality"],
        "quality_warnings": details["quality_warnings"],
        "visual_evidence": details["visual_evidence"],
        "seller_cockpit_text": strip_html(seller_cockpit),
        "metrics_text": strip_html(metrics),
        "sensitivity_text": strip_html(sensitivity),
        "flow_text": strip_html(flow),
        "vision_text": strip_html(vision),
        "listing_text": listing,
        "explanation": explanation,
        "prompt_trace": prompt,
    }


def write_report(rows: list[dict]) -> None:
    """Write markdown and JSON example case artifacts."""
    REPORT_PATH.parent.mkdir(parents=True, exist_ok=True)
    JSON_PATH.write_text(json.dumps(rows, indent=2), encoding="utf-8")

    lines = [
        "# Example Cases",
        "",
        "These cases were generated by running the actual application pipeline. They demonstrate how structured vehicle data, visual damage information, CHF pricing, and listing generation interact.",
        "",
        "| Case | Vision mode | Damage score | Base price | Discount | Recommended price | Detected damages |",
        "|---|---|---:|---:|---:|---:|---|",
    ]
    for row in rows:
        labels = ", ".join(item["label"] for item in row["detected_damages"]) or "none"
        lines.append(
            f"| {row['case_id']} | {row['input']['vision_mode']} | {row['damage_score']:.3f} | "
            f"{format_chf_amount(row['base_price_chf'])} | {format_chf_amount(row['discount_chf'])} | "
            f"{format_chf_amount(row['adjusted_price_chf'])} | {labels} |"
        )

    for row in rows:
        lines.extend(
            [
                "",
                f"## {row['case_id']}",
                "",
                row["description"],
                "",
                f"- Vision mode: {row['input']['vision_mode']}",
                f"- Damage model: `{row['damage_model']}`",
                f"- Damage score: {row['damage_score']:.3f}",
                f"- Base price: CHF {row['base_price_chf']:,.0f}".replace(",", "'"),
                f"- Damage discount: CHF {row['discount_chf']:,.0f}".replace(",", "'"),
                f"- Recommended price: CHF {row['adjusted_price_chf']:,.0f}".replace(",", "'"),
                f"- Visual evidence: {row['visual_evidence'] or 'No visual evidence available.'}",
                "",
                "Listing output:",
                "",
                "```text",
                row["listing_text"],
                "```",
                "",
                "Explanation output:",
                "",
                "```text",
                row["explanation"],
                "```",
            ]
        )
    REPORT_PATH.write_text("\n".join(lines), encoding="utf-8")


def main() -> None:
    rows = [run_case(case) for case in CASES]
    write_report(rows)
    print(f"Wrote {REPORT_PATH}")
    print(f"Wrote {JSON_PATH}")


if __name__ == "__main__":
    main()