ai_audit_analysis_categories = {
    "AI Audit": [
        "sentiment_analysis", 
        "emotion_detection", 
        "political_bias_detection", 
        "stress_level_detection",
        "empathy_level_assessment",
        "mood_detection",
        "toxicity_detection"
        ],

    "GDPR": [
        "Privacy_Assessment",
        "Consent_and_Transparency", 
        "Data_Security",
        "Environmental_Impact"],

    "Toxicity": [
        "Content_Moderation", 
        "Reporting_Mechanism", 
        "Content_Guidelines", 
        "User_Education"],

    "Legal": [
        "Privacy_Policy", 
        "Data_Retention", 
        "Consent_Mechanism"],

    "Context": [
        "Ethical_AI", 
        "Bais_Mitigation", 
        "Fairness_Assestment", 
        "Explainability"],

    "Governance": [
        "Model_development", 
        "Data_Quality", 
        "Bais_Mitigation", 
        "Fairness_Assestment"
        "Explainability"
        "User_Input"],

    "RiskManagement": [
     "Corporate_Ethics", 
     "Board_Management", 
     "Stakeholder_Engagement"],
    
    "Robustness": [
        "System_Reliability", 
        "Quality_Assurance", 
        "Stress_Testing", 
        "Fail_Safe_Procedures"],

    "Sustainability": [
        "Renewable_Resources", 
        "Waste_Reduction", 
        "Energy_Efficiency", 
        "Sustainable_Practices"]
}


# Define a standard template for prompts
STANDARD_PROMPT_TEMPLATE = "You are a data analysis assistant capable of {analysis_type} analysis. {specific_instruction} Respond with your analysis in JSON format. The JSON schema should include '{json_schema}'."




def get_system_prompt(analysis_type: str) -> str:
    specific_instruction = ANALYSIS_TYPES.get(analysis_type, "Perform the analysis as per the specified type.")
    json_schema = JSON_SCHEMAS.get(analysis_type, {})
    json_schema_str = ', '.join([f"'{key}': {value}" for key, value in json_schema.items()])
    return (f"You are a data analyst API capable of {analysis_type} analysis. "
            f"{specific_instruction} Please respond with your analysis directly in JSON format "
            f"(without using Markdown code blocks or any other formatting). Always include confidence_score:number (0-1) with two decimals for result based on analysis"
            f"The JSON schema should include: {{{json_schema_str}}}.")




ANALYSIS_TYPES = {
    "sentiment_analysis": "Analyze the sentiment of the provided text. Determine whether the sentiment is positive, negative, or neutral and provide a confidence score.",
    "emotion_detection": "Detect and identify the primary emotions expressed in the provided text. Provide a score for the intensity of the detected emotion.",
    "political_bias_detection": "Detect any political bias in the provided text, identifying leaning towards particular ideologies or parties.",
    "stress_level_detection": "Analyze the text to assess stress levels, identifying triggers and intensity of stress.",
    "empathy_level_assessment": "Assess the level of empathy expressed in the text, identifying empathetic responses and tendencies.",
    "mood_detection": "Detect the mood of the individual based on textual cues, ranging from happy to sad, calm to angry.",
    "toxicity_detection": "Identify and assess the level of toxicity in the provided text. Determine whether the text contains harmful, offensive, or inappropriate content and provide a score indicating the severity of the toxicity",

     # GDPR-related types
    "Consent_and_Transparency": "Evaluate how consent is obtained and the level of transparency provided to users regarding data usage.",
    "Data_Security": "Assess the measures in place for data security, including vulnerabilities and compliance with security standards.",
    "Privacy_Assessment": "Analyze the overall privacy practices, including policy compliance, data minimization, and user data accessibility.",
    "Environmental_Impact": "Assess the environmental impact of data processing practices, including carbon footprint and energy efficiency.",

    # Toxicity-related types
    "Content_Moderation": "Evaluate the effectiveness of content moderation practices, including automated and human moderation efforts.",
    "Reporting_Mechanism": "Assess the ease and effectiveness of reporting mechanisms for inappropriate or harmful content.",
    "Content_Guidelines": "Analyze the clarity and comprehensiveness of content guidelines and their enforcement consistency.",
    "User_Education": "Evaluate the availability and accessibility of educational resources for users regarding appropriate content and behavior.",

    # Legal-related types
    "Privacy_Policy": "Analyze the clarity and compliance of a privacy policy with legal standards.",
    "Data_Retention": "Evaluate the data retention practices, including periods, deletion policies, and legal compliance.",
    "Consent_Mechanism": "Assess the clarity and effectiveness of the consent mechanism in place for data collection and usage.",
    "GDPR_Compliance": "Evaluate the level of GDPR compliance in data handling, protection measures, and breach notification protocols.",

    # Context-related types
    "Ethical_AI": "Assess adherence to ethical standards in AI practices, including identification and mitigation of ethical issues.",
    "Bias_Mitigation": "Evaluate the presence and mitigation of bias in data or algorithms.",
    "Fairness_Assessment": "Assess fairness in AI systems, identifying affected groups and providing recommendations for improvement.",
    "Explainability": "Evaluate the transparency and explainability of AI models to users.",

    # Governance-related types
    "Model_Development": "Analyze the process of model development, including team composition and ethical considerations.",
    "Data_Quality": "Assess the quality of data used, focusing on accuracy, completeness, and timeliness.",
    "User_Input": "Evaluate the mechanisms for and impact of user feedback on the system.",

    # Risk Management-related types
    "Corporate_Ethics": "Assess the ethical practices within a corporation, including employee training and ethics code adherence.",
    "Board_Management": "Evaluate the effectiveness and diversity of board management and its compliance with ethical standards.",
    "Stakeholder_Engagement": "Analyze stakeholder engagement practices, including inclusion, feedback mechanisms, and satisfaction.",
    "Risk_Management": "Assess the identification, mitigation, and monitoring of risks within an organization.",

    # Robustness-related types
    "System_Reliability": "Evaluate the reliability and resilience of a system, including uptime and redundancy measures.",
    "Quality_Assurance": "Assess the quality assurance practices, including compliance with standards and testing frequency.",
    "Stress_Testing": "Analyze the system's robustness through stress testing and identify weaknesses.",
    "Fail_Safe_Procedures": "Evaluate the effectiveness of fail-safe procedures in place for system failures.",

    # Sustainability-related types
    "Renewable_Resources": "Assess the use of renewable resources and sustainability goals in operations.",
    "Waste_Reduction": "Evaluate waste management practices, reduction rates, and recycling initiatives.",
    "Energy_Efficiency": "Analyze energy consumption and efficiency, including energy-saving measures and audits.",
    "Sustainable_Practices": "Evaluate the adoption of sustainable practices, including training and overall impact."
}


JSON_SCHEMAS = {

    "sentiment_analysis": {
         "sentiment": "string (positive, negative, neutral)",
         "confidence_score": "number (0-1)",
         "text_snippets": "array of strings (specific text portions contributing to sentiment)"
     },
    "emotion_detection": {
         "emotion": "string (primary emotion detected)",
         "confidence_score": "number (0-1)",
         "secondary_emotions": "array of objects (secondary emotions and their scores)"
     },
     "political_bias_detection": {
         "bias": "string (left, right, neutral)",
         "confidence_score": "number (0-1)",
         "bias_indicators": "array of strings (elements indicating bias)",
         "political_alignment_score": "number (quantifying degree of political bias)"
     },
     "stress_level_detection": {
         "stress_level": "string", 
         "stress_triggers": "array of strings"
     },
    "empathy_level_assessment": {
         "empathy_level": "string", 
         "empathetic_responses": "array of strings"
     },
     "mood_detection": {
         "mood": "string", 
         "mood_intensity": "number"
     },
     "toxicity_detection": {
         "toxicity_level": "string (none, low, medium, high)",
         "toxicity_flags": "array of strings (specific words or phrases contributing to toxicity)",
         "contextual_factors": "array of objects (additional contextual elements influencing toxicity interpretation)"
     },

     # GDPR-related schemas
    "Consent_and_Transparency": {
        "consent_obtained": "boolean",
        "transparency_level": "string (low, medium, high)",
        "missing_information": "array of strings (information not clearly presented or missing)",
        "user_understanding": "string (poor, average, good)"
    },
    "Data_Security": {
        "security_status": "string (secure, at risk, breached)",
        "vulnerability_points": "array of strings (specific areas of potential vulnerability)",
        "data_encryption": "boolean",
        "compliance_status": "string (compliant, partially compliant, non-compliant)"
    },
    "Environmental_Impact": {
        "carbon_footprint": "number (metric tons of CO2 equivalent)",
        "energy_efficiency": "string (low, moderate, high)",
        "sustainable_practices": "boolean",
        "environmental_impact_score": "number (0-100)"
    },
   "Privacy_Assessment": {
        "overall_privacy_status": "string (positive, negative)" ,
        "privacy_policy_compliance": "string (compliant, partially compliant, non-compliant)",
        "data_minimization": "boolean",
        "user_data_accessibility": "string (none, limited, full)",
        "anonymization": "boolean"
    },

    # Toxicity-related schemas
    "Content_Moderation": {
        "moderation_effectiveness": "string (low, medium, high)",
        "moderated_content_types": "array of strings (types of content being moderated)",
        "automated_moderation": "boolean",
        "human_moderation": "boolean"
    },
    "Reporting_Mechanism": {
        "reporting_ease": "string (easy, moderate, difficult)",
        "response_time": "string (fast, average, slow)",
        "report_feedback": "string (detailed, minimal, none)"
    },
    "Content_Guidelines": {
        "clarity": "string (clear, somewhat clear, unclear)",
        "comprehensiveness": "string (comprehensive, partial, lacking)",
        "enforcement_consistency": "string (consistent, inconsistent)"
    },
    "User_Education": {
        "educational_resources_available": "boolean",
        "resource_accessibility": "string (easy, moderate, difficult)",
        "user_comprehension_level": "string (high, medium, low)"
    },

      # Legal-related schemas
    "Privacy_Policy": {
        "clarity": "string (clear, somewhat clear, unclear)",
        "compliance": "string (compliant, partially compliant, non-compliant)",
        "user_rights": "array of strings (specific rights mentioned in policy)"
    },
    "Consent_Mechanism": {
        "mechanism_clarity": "string (clear, somewhat clear, unclear)",
        "user_control": "boolean",
        "opt_in_out": "string (opt-in, opt-out, not applicable)"
    },
    "GDPR_Compliance": {
        "compliance_level": "string (fully compliant, partially compliant, non-compliant)",
        "data_protection_officer": "boolean",
        "breach_notification": "boolean"
    },

    # Context-related schemas
    "Ethical_AI": {
        "ethical_standards_adherence": "string (high, medium, low)",
        "ethical_issues_identified": "array of strings",
        "mitigation_measures": "array of strings"
    },
    "Bias_Mitigation": {
        "bias_identified": "boolean",
        "bias_types": "array of strings",
        "mitigation_strategies": "array of strings"
    },
    "Fairness_Assessment": {
        "fairness_level": "string (high, medium, low)",
        "affected_groups": "array of strings",
        "improvement_recommendations": "array of strings"
    },
    "Explainability": {
        "model_transparency": "string (transparent, opaque)",
        "explanation_comprehensibility": "string (high, medium, low)",
        "user_friendly_explanations": "boolean"
    },

    # Governance-related schemas
    "Model_Development": {
        "development_process": "string (structured, ad-hoc, undefined)",
        "team_composition": "array of strings (roles involved)",
        "ethics_considerations": "boolean"
    },
    "Data_Quality": {
        "accuracy_level": "string (high, medium, low)",
        "completeness": "string (complete, partial, incomplete)",
        "timeliness": "string (up-to-date, outdated)"
    },
    "User_Input": {
        "user_feedback_mechanism": "boolean",
        "feedback_responsiveness": "string (responsive, moderately responsive, unresponsive)",
        "user_input_impact": "string (high, medium, low)"
    },

    # Risk Management-related schemas
    "Corporate_Ethics": {
        "ethics_code": "string (exists, partial, none)",
        "employee_training": "boolean",
        "ethics_violations": "array of strings"
    },
    "Board_Management": {
        "board_structure": "string (effective, average, ineffective)",
        "board_diversity": "boolean",
        "board_ethics_compliance": "string (compliant, non-compliant)"
    },
    "Stakeholder_Engagement": {
        "stakeholder_inclusion": "string (inclusive, partially inclusive, exclusive)",
        "feedback_mechanism": "boolean",
        "stakeholder_satisfaction": "string (high, medium, low)"
    },
    "Risk_Management": {
        "risk_identification": "boolean",
        "risk_mitigation_strategies": "array of strings",
        "risk_monitoring": "boolean"
    },

    # Robustness-related schemas
    "System_Reliability": {
        "uptime_percentage": "number (0-100)",
        "system_resilience": "string (high, medium, low)",
        "redundancy_measures": "boolean"
    },
    "Quality_Assurance": {
        "quality_standards": "array of strings",
        "testing_frequency": "string (frequent, occasional, rare)",
        "quality_assurance_compliance": "string (compliant, partially compliant, non-compliant)"
    },
    "Stress_Testing": {
        "stress_test_pass_rate": "number (0-100)",
        "identified_weaknesses": "array of strings",
        "improvement_actions": "array of strings"
    },
    "Fail_Safe_Procedures": {
        "procedures_defined": "boolean",
        "execution_frequency": "string (regular, occasional, never)",
        "effectiveness": "string (effective, partially effective, ineffective)"
    },

    # Sustainability-related schemas
    "Renewable_Resources": {
        "resource_usage": "string (high, moderate, low)",
        "renewable_resource_percentage": "number (0-100)",
        "sustainability_goals": "boolean"
    },
    "Waste_Reduction": {
        "waste_management_practices": "string (effective, average, poor)",
        "reduction_rate": "number (0-100)",
        "recycling_initiatives": "boolean"
    },
    "Energy_Efficiency": {
        "energy_consumption": "string (high, moderate, low)",
        "energy_saving_measures": "array of strings",
        "energy_audit": "boolean"
    },
    "Sustainable_Practices": {
        "practice_adoption": "string (widespread, partial, none)",
        "sustainability_training": "boolean",
        "sustainability_impact": "string (high, medium, low)"
    }
}