from collections import Counter
from io import BytesIO

from PIL import Image

from app import llm


def test_generated_qa_is_normalized_to_conversational_text():
    content = '{"items":[{"question":" 洗完会不会很干？？ ","answers":["回答：我用着还行。”", "感觉不会，挺清爽的！"]}]}'

    result = llm._parse_content(content, expected_answers=2)

    assert result[0]["question"] == "洗完会不会很干"
    assert result[0]["answers"] == ["我用着还行", "感觉不会，挺清爽的"]


def test_generation_prompts_request_varied_buyer_language_without_question_marks():
    qa_prompt = llm._prompt(llm.QAGenerateRequest(product_name="洗面奶"))
    task_prompt = llm._questions_prompt({"product_name": "洗面奶"}, 5)
    task_prompt_terms = (
        "像买家随手输入",
        "末尾不要加问号",
        "不要每句都用",
    )

    assert all(term in qa_prompt for term in task_prompt_terms)
    assert all(term in task_prompt for term in task_prompt_terms)
    assert "不要写成客服说明" in qa_prompt


def test_multi_image_ocr_retries_per_image_and_deduplicates(monkeypatch):
    attempts = Counter()

    def recognize(image_data, content_type):
        attempts[image_data] += 1
        if image_data == b"first" and attempts[image_data] == 1:
            raise TimeoutError("temporary timeout")
        if image_data == b"first":
            return [
                {"product_title": "商品A", "question": "共同问题？"},
                {"product_title": "商品A", "question": "第一张的问题？"},
            ]
        return [
            {"product_title": "商品A", "question": "共同问题？"},
            {"product_title": "商品A", "question": "第二张的问题？"},
        ]

    monkeypatch.setattr(llm.settings, "llm_api_key", "test-key")
    monkeypatch.setattr(llm, "_compress_ocr_image", lambda data, content_type: (data, content_type))
    monkeypatch.setattr(llm, "_extract_questions_from_image", recognize)
    monkeypatch.setattr(llm.time, "sleep", lambda seconds: None)

    result = llm.extract_questions_from_images([
        (b"first", "image/png"),
        (b"second", "image/png"),
    ])

    assert attempts == Counter({b"first": 2, b"second": 1})
    assert [item["question"] for item in result] == ["共同问题？", "第一张的问题？", "第二张的问题？"]
    assert [item["source_index"] for item in result] == [0, 0, 1]


def test_ocr_image_compression_limits_dimensions_and_converts_to_jpeg():
    image = Image.new("RGBA", (3200, 2600), (20, 100, 220, 180))
    source = BytesIO()
    image.save(source, format="PNG")

    compressed, content_type = llm._compress_ocr_image(source.getvalue(), "image/png")

    with Image.open(BytesIO(compressed)) as result:
        assert max(result.size) <= 2200
        assert result.mode == "RGB"
        assert result.format == "JPEG"
    assert content_type == "image/jpeg"
    assert len(compressed) < len(source.getvalue())