def generate_text()

in movie_search/final_ver/backend/scene_search.py [0:0]

• 34 lines of code
• 1 McCabe index (conditional complexity)

def generate_text(prompt: str, model: GenerativeModel = model_pro, temperature: float = 0.4, top_p: float = 0.4) -> dict:
    """Gemini でテキストを生成する

    Args:
        prompt: 入力プロンプト
        model: 利用する Gemini モデル
        temperature: 生成テキストのランダム性
        top_p: 生成テキストの多様性

    Returns:
        生成された JSON オブジェクト
    """

    response_schema = {
        "type": "array",
        "items": {
            "type": "object",
            "properties": {
                "Timestamp": {
                    "type": "string",
                },
                "Description": {
                    "type": "string",
                },
            },
            "required": ["Timestamp", "Description"],
        },
    }

    responses = model.generate_content(
        prompt,
        generation_config=GenerationConfig(
            max_output_tokens=8192,
            temperature=temperature,
            top_p=top_p,
            response_mime_type="application/json",
            response_schema=response_schema
        ),
        safety_settings={
            generative_models.HarmCategory.HARM_CATEGORY_HATE_SPEECH: generative_models.HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
            generative_models.HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: generative_models.HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
            generative_models.HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: generative_models.HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
            generative_models.HarmCategory.HARM_CATEGORY_HARASSMENT: generative_models.HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
        },
        # stream=True は不要になりました
    )

    # 最初のレスポンスのみを取得
    result = responses.text

    # JSON 文字列をパースして返す
    return json.loads(result)