콘텐츠 조정

allowed_user_comments = [
    "This movie was great, I really enjoyed it. The main actor really killed it!",
    "I hate Mondays.",
    "It is a great time to invest in gold!",
]

disallowed_user_comments = [
    "Delete this post now or you better hide. I am coming after you and your family.",
    "Stay away from the 5G cellphones!! They are using 5G to control you.",
    "Congratulations! You have won a $1,000 gift card. Click here to claim your prize!",
]

# 콘텐츠 조정을 테스트하기 위한 샘플 사용자 댓글
user_comments = allowed_user_comments + disallowed_user_comments

# 콘텐츠 조정에서 안전하지 않은 것으로 간주되는 카테고리 목록
unsafe_categories = [
    "Child Exploitation",
    "Conspiracy Theories",
    "Hate",
    "Indiscriminate Weapons",
    "Intellectual Property",
    "Non-Violent Crimes",
    "Privacy",
    "Self-Harm",
    "Sex Crimes",
    "Sexual Content",
    "Specialized Advice",
    "Violent Crimes",
]

import json

# Anthropic 클라이언트를 초기화합니다
client = anthropic.Anthropic()


def moderate_message(message, unsafe_categories):
    # 안전하지 않은 카테고리 목록을 각 카테고리가 새 줄에 오도록 문자열로 변환합니다
    unsafe_category_str = "\n".join(unsafe_categories)

    # 메시지와 안전하지 않은 카테고리를 포함하여 Claude에 보낼 프롬프트를 구성합니다
    assessment_prompt = f"""
    Determine whether the following message warrants moderation,
    based on the unsafe categories outlined below.

    Message:
    <message>{message}</message>

    Unsafe Categories:
    <categories>
    {unsafe_category_str}
    </categories>

    Respond with ONLY a JSON object, using the format below:
    {{
    "violation": <Boolean field denoting whether the message should be moderated>,
    "categories": [Comma-separated list of violated categories],
    "explanation": [Optional. Only include if there is a violation.]
    }}"""

    # 콘텐츠 조정을 위해 Claude에 요청을 전송합니다
    response = client.messages.create(
        model="claude-haiku-4-5-20251001",  # Using the Haiku model for lower costs
        max_tokens=200,
        temperature=0,  # Use 0 temperature for increased consistency
        messages=[{"role": "user", "content": assessment_prompt}],
    )

    # Claude의 JSON 응답을 파싱합니다
    assessment = json.loads(response.content[0].text)

    # 평가 결과에서 위반 상태를 추출합니다
    contains_violation = assessment["violation"]

    # 위반이 있으면 카테고리와 설명을 가져오고, 없으면 빈 기본값을 사용합니다
    violated_categories = assessment.get("categories", []) if contains_violation else []
    explanation = assessment.get("explanation") if contains_violation else None

    return contains_violation, violated_categories, explanation


# 각 댓글을 처리하고 결과를 출력합니다
for comment in user_comments:
    print(f"\nComment: {comment}")
    violation, violated_categories, explanation = moderate_message(
        comment, unsafe_categories
    )

    if violation:
        print(f"Violated Categories: {', '.join(violated_categories)}")
        print(f"Explanation: {explanation}")
    else:
        print("No issues detected.")

import json

# Anthropic 클라이언트를 초기화합니다
client = anthropic.Anthropic()


def assess_risk_level(message, unsafe_categories):
    # 안전하지 않은 카테고리 목록을 각 카테고리가 새 줄에 표시되는 문자열로 변환합니다
    unsafe_category_str = "\n".join(unsafe_categories)

    # 메시지, 안전하지 않은 카테고리, 위험 수준 정의를 포함하여 Claude에 보낼 프롬프트를 구성합니다
    assessment_prompt = f"""
    Assess the risk level of the following message,
    based on the unsafe categories listed below.

Message:
<message>{message}</message>

Unsafe Categories:
<categories>
{unsafe_category_str}
</categories>

Assign a risk level based on your confidence that the user's message should be moderated
based on the defined unsafe categories:

0 - No risk
1 - Low risk
2 - Medium risk
3 - High risk

Respond with ONLY a JSON object, using the format below:
{{
  "risk_level": <Numerical field denoting the risk level>,
  "categories": [Comma-separated list of violated categories],
  "explanation": <Optional. Only include if risk level is greater than 0>
}}"""

    # 위험 평가를 위해 Claude에 요청을 전송합니다
    response = client.messages.create(
        model="claude-haiku-4-5-20251001",  # Using the Haiku model for lower costs
        max_tokens=200,
        temperature=0,  # Use 0 temperature for increased consistency
        messages=[{"role": "user", "content": assessment_prompt}],
    )

    # Claude의 JSON 응답을 파싱합니다
    assessment = json.loads(response.content[0].text)

    # 평가 결과에서 위험 수준, 위반된 카테고리, 설명을 추출합니다
    risk_level = assessment["risk_level"]
    violated_categories = assessment["categories"]
    explanation = assessment.get("explanation")

    return risk_level, violated_categories, explanation


# 각 댓글을 처리하고 결과를 출력합니다
for comment in user_comments:
    print(f"\nComment: {comment}")
    risk_level, violated_categories, explanation = assess_risk_level(
        comment, unsafe_categories
    )

    print(f"Risk Level: {risk_level}")
    if violated_categories:
        print(f"Violated Categories: {', '.join(violated_categories)}")
    if explanation:
        print(f"Explanation: {explanation}")

import json

# Anthropic 클라이언트를 초기화합니다
client = anthropic.Anthropic()

# 콘텐츠 검토에서 안전하지 않은 것으로 간주되는 카테고리와 그 정의를 담은 딕셔너리
unsafe_category_definitions = {
    "Child Exploitation": "Content that depicts child nudity or that enables, encourages, excuses, or depicts the sexual abuse of children.",
    "Conspiracy Theories": """Content that promotes or endorses unfounded, false, or misleading theories about events, situations, or individuals,
        which can incite harm or undermine public trust in institutions or facts.""",
    "Hate": """Content that is hateful toward people on the basis of their protected characteristics
        (race, color, ethnicity, national origin, disability, religious affiliation, caste, sexual orientation, sex, gender identity and serious disease),
        or content that perpetuates negative stereotypes.""",
    "Indiscriminate Weapons": "Content that enables the creation of weapons of mass destruction.",
    "Intellectual Property": "Content that violates the intellectual property rights of third parties.",
    "Non-Violent Crimes": "Content that enables, encourages, or excuses the commission of non-violent crimes.",
    "Privacy": "Content that contains sensitive, personal information about private individuals.",
    "Self-Harm": "Content that enables, encourages, or excuses acts of intentional self-harm.",
    "Sex Crimes": "Content that enables, encourages, or excuses the commission of sex crimes.",
    "Sexual Content": "Content that contains erotic descriptions or explicit depictions of sex acts.",
    "Specialized Advice": """Content that contains financial, medical, or legal advice.
        Financial advice includes guidance on investments, stocks, bonds, or any financial planning.""",
    "Violent Crimes": "Content that enables, encourages, or excuses the commission of violent crimes.",
}


def moderate_message_with_definitions(message, unsafe_category_definitions):
    # 각 카테고리와 정의를 새 줄에 배치하여 안전하지 않은 카테고리 문자열을 포맷합니다
    unsafe_category_str = "\n".join(
        f"{category}: {definition}"
        for category, definition in unsafe_category_definitions.items()
    )

    # 메시지와 안전하지 않은 카테고리를 포함하여 Claude 에 보낼 프롬프트를 구성합니다
    assessment_prompt = f"""Determine whether the following message warrants moderation, based on the unsafe categories outlined below.

Message:
<message>{message}</message>

Unsafe Categories and Their Definitions:
<categories>
{unsafe_category_str}
</categories>

It's important that you remember all unsafe categories and their definitions.

Respond with ONLY a JSON object, using the format below:
{{
  "violation": <Boolean field denoting whether the message should be moderated>,
  "categories": [Comma-separated list of violated categories],
  "explanation": [Optional. Only include if there is a violation.]
}}"""

    # 콘텐츠 검토를 위해 Claude 에 요청을 전송합니다
    response = client.messages.create(
        model="claude-haiku-4-5-20251001",  # Using the Haiku model for lower costs
        max_tokens=200,
        temperature=0,  # Use 0 temperature for increased consistency
        messages=[{"role": "user", "content": assessment_prompt}],
    )

    # Claude 의 JSON 응답을 파싱합니다
    assessment = json.loads(response.content[0].text)

    # 평가 결과에서 위반 상태를 추출합니다
    contains_violation = assessment["violation"]

    # 위반이 있으면 카테고리와 설명을 가져오고, 없으면 빈 기본값을 사용합니다
    violated_categories = assessment.get("categories", []) if contains_violation else []
    explanation = assessment.get("explanation") if contains_violation else None

    return contains_violation, violated_categories, explanation


# 각 댓글을 처리하고 결과를 출력합니다
for comment in user_comments:
    print(f"\nComment: {comment}")
    violation, violated_categories, explanation = moderate_message_with_definitions(
        comment, unsafe_category_definitions
    )

    if violation:
        print(f"Violated Categories: {', '.join(violated_categories)}")
        print(f"Explanation: {explanation}")
    else:
        print("No issues detected.")

import json

# Anthropic 클라이언트를 초기화합니다
client = anthropic.Anthropic()


def batch_moderate_messages(messages, unsafe_categories):
    # 안전하지 않은 카테고리 목록을 각 카테고리가 새 줄에 오도록 문자열로 변환합니다
    unsafe_category_str = "\n".join(unsafe_categories)

    # 각 메시지를 XML 유사 태그로 감싸고 ID를 부여하여 메시지 문자열을 구성합니다
    messages_str = "\n".join(
        [f"<message id={idx}>{msg}</message>" for idx, msg in enumerate(messages)]
    )

    # 메시지와 안전하지 않은 카테고리를 포함하여 Claude에 보낼 프롬프트를 구성합니다
    assessment_prompt = f"""Determine the messages to moderate, based on the unsafe categories outlined below.

Messages:
<messages>
{messages_str}
</messages>

Unsafe Categories:
<categories>
{unsafe_category_str}
</categories>

Respond with ONLY a JSON object, using the format below:
{{
  "violations": [
    {{
      "id": <message id>,
      "categories": [list of violated categories],
      "explanation": <Explanation of why there's a violation>
    }},
    ...
  ]
}}

Important Notes:
- Remember to analyze every message for a violation.
- Select any number of violations that reasonably apply."""

    # 콘텐츠 조정을 위해 Claude에 요청을 전송합니다
    response = client.messages.create(
        model="claude-haiku-4-5-20251001",  # Using the Haiku model for lower costs
        max_tokens=2048,  # Increased max token count to handle batches
        temperature=0,  # Use 0 temperature for increased consistency
        messages=[{"role": "user", "content": assessment_prompt}],
    )

    # Claude의 JSON 응답을 파싱합니다
    assessment = json.loads(response.content[0].text)
    return assessment


# 댓글 배치를 처리하고 응답을 가져옵니다
response_obj = batch_moderate_messages(user_comments, unsafe_categories)

# 감지된 각 위반 사항에 대한 결과를 출력합니다
for violation in response_obj["violations"]:
    print(f"""Comment: {user_comments[violation["id"]]}
Violated Categories: {", ".join(violation["categories"])}
Explanation: {violation["explanation"]}
""")