prompts.py

#              - OpenAI Data Anaylsis & Guiderails Script
#     /\__/\   - prompts.py
#    ( o.o  )  - v0.0.1
#      >^<     - by @rUv

from typing import Dict, Optional
from pydantic import BaseModel, create_model, ValidationError
from typing import List, Union, Any, Optional
from decimal import Decimal



ANALYSIS_TYPES = {

    "youtube_shorts_title_creation": "As an Analyst API specializing in YouTube Shorts title creation, your task is to generate titles that are concise, SEO-friendly, and engaging. Aim to capture the essence of the video's story with a focus on emotional resonance, specific details, and viewer curiosity. Consider highlighting key moments, emotions, or figures that stand out. Use of emojis for visual appeal and questions to provoke thought is encouraged, but balance these with brevity to ensure titles are impactful and to the point. ",
    "president_speech_creator": "You are the President of the United States. You have been asked to address the nation on a specific topic. Write a speech that is 3,500 characters long.",
    "json_creator": "Create a JSON object based on the provided data.",
    "visual_content_synthesis": "Synthesize the provided video transcription to identify key visual elements, themes, or moments that are most representative of the video content. Use this synthesis to suggest creative, visually appealing concepts for a YouTube thumbnail.",
    "sentiment_analysis": "Analyze the sentiment of the provided text. Determine whether the sentiment is positive, negative, or neutral and provide a confidence score.",
    "text_summarization": "Summarize the provided text into a concise version, capturing the key points and main ideas.",
    "topic_extraction": "Identify and extract the main topics or themes from the provided text.",
    "emotion_detection": "Detect and identify the primary emotions expressed in the provided text. Provide a score for the intensity of the detected emotion.",
    "language_translation": "Translate the provided text into a specified target language, maintaining the original meaning and context.",
    "grammatical_error_check": "Check the provided text for any grammatical errors, and suggest corrections where necessary.",
    "keyword_extraction": "Extract key words or phrases from the provided text that are central to its meaning.",
    "content_classification": "Classify the provided text into predefined categories or genres based on its content.",
    "trend_analysis": "Analyze the provided text to identify current trends, patterns, or emerging themes.",
    "customer_feedback_analysis": "Analyze customer feedback to determine overall satisfaction, pinpointing areas of strength and those needing improvement.",
    "brand_sentiment_analysis": "Assess public sentiment towards a specific brand based on the provided text, identifying positive, negative, or neutral attitudes.",
    "product_review_analysis": "Analyze product reviews to extract overall customer opinions, satisfaction levels, and key feedback points.",
    "market_research_analysis": "Examine the provided text for insights into market trends, consumer preferences, and competitive landscape.",
    "political_bias_detection": "Detect any political bias in the provided text, identifying leaning towards particular ideologies or parties.",
    "fake_news_detection": "Evaluate the provided text to determine the likelihood of it being fake news, assessing credibility and factual accuracy.",
    "cultural_trend_analysis": "Analyze the text for insights into cultural trends, shifts, and general public sentiment on cultural topics.",
    "historical_data_analysis": "Analyze historical data presented in the text, extracting key events, trends, and patterns.",
    "literary_analysis": "Perform an analysis of literary aspects in the provided text, examining themes, motifs, character development, and narrative style.",
    "scientific_research_analysis": "Analyze scientific content in the text, summarizing research findings, methodology, and conclusions.",
    "social_media_monitoring": "Monitor and analyze content from social media platforms in the provided text, identifying trends, sentiments, and influential discussions.",
    "psychological_analysis": "Analyze the text for psychological insights, understanding emotions, behaviors, and mental states.",
    "criminal_intent_detection": "Assess the text for indications of criminal intent or illicit activities.",
    "behavioral_analysis": "Analyze behaviors described or implied in the text, understanding patterns and motivations.",
    "relationship_analysis": "Examine the text for insights into relationships, interactions, and social dynamics.",
    "emotional_intelligence_analysis": "Evaluate the text for aspects of emotional intelligence, such as empathy, self-awareness, and social skills.",
    "ideological_alignment_detection": "Detect ideological alignment in the text, identifying adherence to specific sets of beliefs or principles.",
    "conflict_resolution_analysis": "Analyze the text for conflict resolution strategies, understanding approaches to solving disputes or disagreements.",
    "narrative_analysis": "Analyze the narrative structure and elements in the text, understanding plot development, storytelling techniques, and thematic elements.",
    "ethical_stance_detection": "Evaluate the text for ethical stances and moral viewpoints.",
    "propaganda_identification": "Identify propaganda techniques and messages within the text, assessing bias and persuasive strategies.",
    "socioeconomic_status_analysis": "Analyze the text for indications of socioeconomic status, understanding economic and social factors.",
    "health_and_wellness_analysis": "Evaluate the text for health and wellness-related content, understanding medical conditions, treatments, and lifestyle factors.",
    "sarcasm_and_irony_detection": "Detect sarcasm and irony in the text, differentiating between literal and figurative language.",
    "crisis_detection_analysis": "Identify and analyze signs of crisis or emergency situations in the text.",
    "cognitive_bias_identification": "Detect cognitive biases in the text, understanding prejudiced thinking or skewed perspectives.",
    "dialogue_analysis": "Analyze dialogues in the text, understanding character interactions, conversational dynamics, and communication styles.",
    "legal_document_analysis": "Examine legal documents in the text, interpreting legal language, clauses, and implications.",
    "cultural_analysis": "Analyze the text for cultural insights, understanding societal norms, values, and practices.",
    "user_experience_feedback_analysis": "Evaluate user experience feedback in the text, identifying usability issues, satisfaction levels, and user preferences.",
    "automated_therapy_session_analysis": "Analyze transcripts of therapy sessions, understanding therapeutic techniques, patient responses, and progress indicators.",
    "stress_level_detection": "Analyze the text to assess stress levels, identifying triggers and intensity of stress.",
    "mood_detection": "Detect the mood of the individual based on textual cues, ranging from happy to sad, calm to angry.",
    "personality_type_analysis": "Analyze text to determine the personality type of the individual, based on standard personality traits.",
    "cognitive_load_measurement": "Measure the cognitive load or mental effort required in the text context.",
    "therapeutic_intervention_analysis": "Analyze text for therapeutic interventions, evaluating their relevance and effectiveness.",
    "empathy_level_assessment": "Assess the level of empathy expressed in the text, identifying empathetic responses and tendencies.",
    "conflict_tendency_analysis": "Analyze text for conflict tendencies, understanding triggers and patterns of conflict.",
    "motivational_analysis": "Examine the text for motivational messages and their impact.",
    "mindfulness_meditation_effectiveness": "Analyze the effectiveness of mindfulness and meditation techniques mentioned in the text.",
    "psychological_resilience_assessment": "Assess the level of psychological resilience presented in the text.",
    "addiction_tendency_analysis": "Evaluate the text for signs of addictive behavior and tendencies.",
    "depression_anxiety_detection": "Detect signs of depression and anxiety in the text, noting severity and contextual factors.",
    "self_esteem_assessment": "Assess the level of self-esteem expressed in the text.",
    "trauma_analysis": "Analyze the text for references to trauma and its psychological impact.",
    "life_satisfaction_analysis": "Evaluate the text for expressions of life satisfaction and fulfillment.",
    "sleep_quality_assessment": "Analyze text for mentions of sleep quality and related issues.",
    "psychosomatic_symptom_analysis": "Assess text for psychosomatic symptoms and their psychological underpinnings.",
    "learning_style_identification": "Identify preferred learning styles mentioned or implied in the text.",
    "interpersonal_relationship_analysis": "Analyze the text for insights into interpersonal relationships and dynamics.",
    "cultural_adaptation_analysis": "Evaluate how individuals or groups adapt to different cultural contexts in the text.",
    "youtube_thumbnail_prompt_creation": "Create a detailed prompt for generating a YouTube thumbnail based on the provided video transcription or summary. This includes visual elements like key scenes, characters, emotions, and color schemes that capture the essence of the video."
}

JSON_SCHEMAS = {
    "youtube_shorts_title_creation": {
        "video_titles": {
            "video_title_1": "string",
            "video_title_2": "string",
            "video_title_3": "string"
        },
        "Best_title": "string (the best title among the generated options)",
            },

    "president_speech_creator": {
        "speech": "string (the speech written by the President of the United States)",
    },
    "json_creator": {
        "json_object": "object (the JSON object created based on the provided data)"
    },
    "visual_content_synthesis": {
        "key_visual_elements": "array of strings (descriptions of visual elements identified in the video content)",
        "themes": "array of strings (central themes or messages conveyed in the video)",
        "representative_moments": "array of strings (specific scenes or moments highlighted for their visual impact)",
        "suggested_concepts": "array of strings (creative concepts for YouTube thumbnail based on the synthesis)",
        "color_scheme": "string (suggested color scheme for the thumbnail)",
        "emotional_tone": "string (overall emotional tone suggested for the thumbnail to convey)",
    },
    "youtube_thumbnail_prompt_creation": {
        "thumbnail_prompt": "string (description of the prompt for creating a YouTube thumbnail)",
    },
     "sentiment_analysis": {
         "sentiment": "string (positive, negative, neutral)",
         "confidence_score": "number (0-1)",
         "text_snippets": "array of strings (specific text portions contributing to sentiment)"
     },
     "text_summarization": {
         "summary": "string",
         "key_points": "array of strings (main points summarized)",
         "length": "number (number of words in summary)"
     },
     "topic_extraction": {
         "topics": "array of strings",
         "relevance_scores": "array of numbers (0-1) (relevance of each topic)",
         "key_phrases": "array of strings (phrases most associated with each topic)"
     },
     "emotion_detection": {
         "emotion": "string (primary emotion detected)",
         "confidence_score": "number (0-1)",
         "secondary_emotions": "array of objects (secondary emotions and their scores)"
     },
     "language_translation": {
         "translated_text": "string",
         "source_language": "string (detected or specified source language)",
         "target_language": "string (language into which text is translated)"
     },
     "grammatical_error_check": {
         "corrected_text": "string",
         "errors": "array of objects (error details including type, position, and suggestions)",
         "total_errors": "number (total count of errors found)"
     },
     "keyword_extraction": {
         "keywords": "array of strings (key phrases or words extracted from the text)",
         "relevance_scores": "array of numbers (0-1) (indicating the relevance of each keyword)",
         "context_snippets": "array of strings (text snippets where each keyword prominently features)",
         "keyword_frequency": "array of numbers (count of occurrences of each keyword in the text)"
     },
     "content_classification": {
         "category": "string",
         "subcategories": "array of strings",
         "confidence_score": "number (0-1)",
         "contextual_details": "array of strings (explanations for classification)"
     },
     "trend_analysis": {
         "trends": "array of objects (each object detailing trend name, relevance score, and description)",
         "emerging_trends": "array of strings (newly identified trends)",
         "trend_lifetime": "array of objects (duration and evolution of each trend)"
     },
     "customer_feedback_analysis": {
         "feedback_summary": "string",
         "sentiment": "string (positive, negative, neutral)",
         "key_feedback_points": "array of strings",
         "customer_satisfaction_index": "number (0-1)"
     },
     "brand_sentiment_analysis": {
         "brand_sentiment": "string (positive, negative, neutral)",
         "confidence_score": "number (0-1)",
         "key_sentiment_drivers": "array of strings",
         "brand_health_index": "number (overall health score of the brand)"
     },
     "product_review_analysis": {
         "review_summary": "string",
         "sentiment": "string (positive, negative, neutral)",
         "product_rating": "number (average rating from reviews)",
         "key_review_topics": "array of strings (main topics mentioned in reviews)"
     },
     "market_research_analysis": {
         "market_trends": "array of objects (trend details including trend name, impact score, and description)",
         "consumer_preferences": "array of objects (preference details including preference type and popularity score)",
         "market_segmentation": "array of objects (segmentation details including segment name and characteristics)"
     },
     "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)"
     },
     "fake_news_detection": {
         "credibility": "string (credible, not credible)",
         "confidence_score": "number (0-1)",
         "fact_check_results": "array of objects (details of fact-checking each claim)",
         "reliability_index": "number (overall reliability score)"
     },
     "cultural_trend_analysis": {
         "trends": "array of objects (trend details including trend name, cultural impact score, and description)",
         "cultural_shifts": "array of objects (shift details including shift name, affected areas, and significance)",
         "cultural_health_index": "number (overall health score of cultural aspects)"
     },
     "historical_data_analysis": {
         "key_events": "array of objects (event details including event name, date, and significance)",
         "patterns": "array of objects (pattern details including pattern name, frequency, and implications)",
         "historical_impact_score": "number (quantifying impact of historical events)"
     },
    "literary_analysis": {
        "themes": "array of strings",
        "character_development": "string",
        "narrative_style": "string"
    },
    "scientific_research_analysis": {
        "research_findings": "string",
        "methodology": "string",
        "conclusions": "string"
    },
    "social_media_monitoring": {
        "trending_topics": "array of strings",
        "influential_posts": "array of objects (post details)",
        "sentiment": "string (positive, negative, neutral)",
        "confidence_score": "number (0-1)",
        "likes": "number",
        "shares": "number",
        "comments": "number",
        "retweets": "number",
        "age_range": "string",
        "location": "string",
        "gender": "string",
        "influencers": "array of strings",
        "influence_score": "number (0-1)",
        "category": "string",
        "subcategories": "array of strings",
        "future_trends": "array of strings",
        "prediction_confidence": "number (0-1)",
        "timeline": "array of objects date and relevance score",
        "hashtags": "array of strings",
        "usage_frequency": "number",
        "source": "string",
        "credibility_score": "number (0-1)",
        "emotions": "array of objects (emotion type and score)"
    },
    "psychological_analysis": {
        "emotional_states": "array of strings",
        "behavioral_patterns": "string"
    },
    "criminal_intent_detection": {
        "potential_risks": "array of strings",
        "threat_level": "string"
    },
     "behavioral_analysis": {
         "observed_behaviors": "array of strings",
         "behavioral_patterns": "string",
         "motivations": "string"
     },
     "relationship_analysis": {
         "interaction_types": "array of strings",
         "relationship_dynamics": "string",
         "communication_patterns": "string"
     },
     "emotional_intelligence_analysis": {
         "empathy_levels": "string",
         "self_awareness_assessment": "string",
         "social_skills_evaluation": "string"
     },
     "ideological_alignment_detection": {
         "political_ideologies": "array of strings",
         "alignment_strength": "number (0-1)"
     },
     "conflict_resolution_analysis": {
         "conflict_types": "array of strings",
         "resolution_strategies": "array of strings"
     },
     "narrative_analysis": {
         "plot_structure": "string",
         "character_roles": "array of objects (character details)",
         "thematic_elements": "array of strings"
     },
     "ethical_stance_detection": {
         "ethical_positions": "array of strings",
         "stance_strength": "number (0-1)"
     },
     "propaganda_identification": {
         "propaganda_techniques": "array of strings",
         "persuasive_strength": "number (0-1)"
     },
     "socioeconomic_status_analysis": {
         "economic_indicators": "array of strings",
         "social_factors": "array of strings"
     },
     "health_and_wellness_analysis": {
         "health_conditions_identified": "array of strings",
         "wellness_recommendations": "array of strings"
     },
     "sarcasm_and_irony_detection": {
         "sarcasm_level": "string",
         "irony_type": "string"
     },
     "crisis_detection_analysis": {
         "crisis_signals": "array of strings",
         "urgency_level": "string"
     },
     "cognitive_bias_identification": {
         "biases_identified": "array of strings",
         "bias_impact": "string"
     },
     "dialogue_analysis": {
         "speaking_styles": "array of strings",
         "conversation_themes": "array of strings"
     },
     "legal_document_analysis": {
         "key_clauses": "array of strings",
         "document_legality": "string"
     },
     "cultural_analysis": {
         "cultural_values": "array of strings",
         "societal_norms": "array of strings"
     },
     "user_experience_feedback_analysis": {
         "usability_issues": "array of strings",
         "user_satisfaction_levels": "string"
     },
     "automated_therapy_session_analysis": {
         "therapeutic_techniques_used": "array of strings",
         "patient_response_types": "array of strings"
     },
     "stress_level_detection": {
         "stress_level": "string", 
         "stress_triggers": "array of strings"
     },
     "mood_detection": {
         "mood": "string", 
         "mood_intensity": "number"
     },
     "personality_type_analysis": {
         "personality_type": "string", 
         "trait_scores": "object"
     },
     "cognitive_load_measurement": {
         "cognitive_load_level": "string", 
         "factors_contributing": "array of strings"
     },
     "therapeutic_intervention_analysis": {
         "interventions": "array of strings", 
         "effectiveness": "number"
     },
     "empathy_level_assessment": {
         "empathy_level": "string", 
         "empathetic_responses": "array of strings"
     },
     "conflict_tendency_analysis": {
         "conflict_triggers": "array of strings", 
         "conflict_resolution": "string"
     },
     "motivational_analysis": {
         "motivational_messages": "array of strings", 
         "impact_score": "number"
     },
     "mindfulness_meditation_effectiveness": {
         "techniques_used": "array of strings", 
         "effectiveness_score": "number"
     },
     "psychological_resilience_assessment": {
         "resilience_level": "string", 
         "coping_strategies": "array of strings"
     },
     "addiction_tendency_analysis": {
         "addictive_behaviors": "array of strings", 
         "severity_level": "string"
     },
     "depression_anxiety_detection": {
         "depression_level": "string", 
         "anxiety_level": "string"
     },
     "self_esteem_assessment": {
         "self_esteem_level": "string", 
         "influencing_factors": "array of strings"
     },
     "trauma_analysis": {
         "traumatic_events": "array of strings", 
         "psychological_impact": "string"
     },
     "life_satisfaction_analysis": {
         "satisfaction_level": "string", 
         "key_factors": "array of strings"
     },
     "sleep_quality_assessment": {
         "sleep_quality": "string", 
         "disruptive_factors": "array of strings"
     },
     "psychosomatic_symptom_analysis": {
         "symptoms": "array of strings", 
         "psychological_causes": "array of strings"
     },
     "learning_style_identification": {
         "preferred_styles": "array of strings", 
         "effectiveness": "number"
     },
     "interpersonal_relationship_analysis": {
         "relationship_types": "array of strings", 
         "interaction_patterns": "array of strings"
     },
     "cultural_adaptation_analysis": {
         "adaptation_levels": "array of strings", 
         "challenges_faced": "array of strings"
     }
}


# 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}'."

STANDARD_PROMPT_TEMPLATE = (
    "You are a {role} API capable of {analysis_type}. {specific_instruction} "
    "Please respond with your analysis directly in JSON format (without using Markdown code blocks or any other formatting). "
    "Always include confidence_score:number (0-1) with two decimals for results based on analysis. "
    "The JSON schema should include: {json_schema}."
)

def get_system_prompt(analysis_type: str, role: str, additional_fields: Optional[Dict[str, str]] = None) -> str:
    """
    Generates a system prompt using the updated standard template and dynamically updated JSON schema.
    """
    specific_instruction = ANALYSIS_TYPES.get(analysis_type, "Perform the analysis as specified.")
    updated_schema = dynamic_schema_update(analysis_type, additional_fields)
    json_schema_str = ", ".join([f"'{k}': {v}" for k, v in updated_schema.items()])
    
    # Adding confidence score field explicitly in the template
    if "confidence_score" not in updated_schema:
        json_schema_str += ", 'confidence_score': 'number (0-1)'"

    return STANDARD_PROMPT_TEMPLATE.format(role=role, analysis_type=analysis_type,
                                           specific_instruction=specific_instruction, json_schema=json_schema_str)


def dynamic_schema_update(analysis_type: str, additional_fields: Optional[Dict[str, str]] = None) -> Dict[str, str]:
    """
    Dynamically updates the JSON schema for a given analysis type based on additional fields.
    """
    base_schema = JSON_SCHEMAS.get(analysis_type, {}).copy()  # Copy base schema to avoid mutation
    if additional_fields:
        base_schema.update(additional_fields)  # Incorporate additional fields
    return base_schema