Moderación de contenido

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!",
]

# Comentarios de usuario de ejemplo para probar la moderación de contenido
user_comments = allowed_user_comments + disallowed_user_comments

# Lista de categorías consideradas no seguras para la moderación de contenido
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

# Inicializa el cliente de Anthropic
client = anthropic.Anthropic()


def moderate_message(message, unsafe_categories):
    # Convierte la lista de categorías no seguras en una cadena, con cada categoría en una nueva línea
    unsafe_category_str = "\n".join(unsafe_categories)

    # Construye el prompt para Claude, incluyendo el mensaje y las categorías no seguras
    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.]
    }}"""

    # Envía la solicitud a Claude para la moderación de contenido
    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}],
    )

    # Analiza la respuesta JSON de Claude
    assessment = json.loads(response.content[0].text)

    # Extrae el estado de infracción de la evaluación
    contains_violation = assessment["violation"]

    # Si hay una infracción, obtén las categorías y la explicación; de lo contrario, usa valores predeterminados vacíos
    violated_categories = assessment.get("categories", []) if contains_violation else []
    explanation = assessment.get("explanation") if contains_violation else None

    return contains_violation, violated_categories, explanation


# Procesa cada comentario e imprime los resultados
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

# Inicializa el cliente de Anthropic
client = anthropic.Anthropic()


def assess_risk_level(message, unsafe_categories):
    # Convierte la lista de categorías no seguras en una cadena, con cada categoría en una nueva línea
    unsafe_category_str = "\n".join(unsafe_categories)

    # Construye el prompt para Claude, incluyendo el mensaje, las categorías no seguras y las definiciones de niveles de riesgo
    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>
}}"""

    # Envía la solicitud a Claude para la evaluación de riesgo
    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}],
    )

    # Analiza la respuesta JSON de Claude
    assessment = json.loads(response.content[0].text)

    # Extrae el nivel de riesgo, las categorías infringidas y la explicación de la evaluación
    risk_level = assessment["risk_level"]
    violated_categories = assessment["categories"]
    explanation = assessment.get("explanation")

    return risk_level, violated_categories, explanation


# Procesa cada comentario e imprime los resultados
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

# Inicializa el cliente de Anthropic
client = anthropic.Anthropic()

# Diccionario de categorías consideradas no seguras para la moderación de contenido, con sus definiciones
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):
    # Formatea la cadena de categorías no seguras, con cada categoría y su definición en una nueva línea
    unsafe_category_str = "\n".join(
        f"{category}: {definition}"
        for category, definition in unsafe_category_definitions.items()
    )

    # Construye el prompt para Claude, incluyendo el mensaje y las categorías no seguras
    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.]
}}"""

    # Envía la solicitud a Claude para la moderación de contenido
    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}],
    )

    # Analiza la respuesta JSON de Claude
    assessment = json.loads(response.content[0].text)

    # Extrae el estado de infracción de la evaluación
    contains_violation = assessment["violation"]

    # Si hay una infracción, obtén las categorías y la explicación; de lo contrario, usa valores predeterminados vacíos
    violated_categories = assessment.get("categories", []) if contains_violation else []
    explanation = assessment.get("explanation") if contains_violation else None

    return contains_violation, violated_categories, explanation


# Procesa cada comentario e imprime los resultados
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

# Inicializa el cliente de Anthropic
client = anthropic.Anthropic()


def batch_moderate_messages(messages, unsafe_categories):
    # Convierte la lista de categorías no seguras en una cadena, con cada categoría en una nueva línea
    unsafe_category_str = "\n".join(unsafe_categories)

    # Formatea la cadena de mensajes, envolviendo cada mensaje en etiquetas tipo XML y asignándole un ID
    messages_str = "\n".join(
        [f"<message id={idx}>{msg}</message>" for idx, msg in enumerate(messages)]
    )

    # Construye el prompt para Claude, incluyendo los mensajes y las categorías no seguras
    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."""

    # Envía la solicitud a Claude para la moderación de contenido
    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}],
    )

    # Analiza la respuesta JSON de Claude
    assessment = json.loads(response.content[0].text)
    return assessment


# Procesa el lote de comentarios y obtén la respuesta
response_obj = batch_moderate_messages(user_comments, unsafe_categories)

# Imprime los resultados de cada infracción detectada
for violation in response_obj["violations"]:
    print(f"""Comment: {user_comments[violation["id"]]}
Violated Categories: {", ".join(violation["categories"])}
Explanation: {violation["explanation"]}
""")