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6dad1de | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | from dataclasses import dataclass, field
from typing import Dict, Any, Optional
import random
import json
from tinytroupe.agent_types import Action
from tinytroupe import openai_utils
@dataclass
class TraitProfile:
openness_to_new_ideas: float = 0.5
conscientiousness: float = 0.5
extraversion: float = 0.5
agreeableness: float = 0.5
controversiality_tolerance: float = 0.5
information_seeking_behavior: float = 0.5
visual_content_preference: float = 0.5
def to_dict(self):
return {
"openness_to_new_ideas": self.openness_to_new_ideas,
"conscientiousness": self.conscientiousness,
"extraversion": self.extraversion,
"agreeableness": self.agreeableness,
"controversiality_tolerance": self.controversiality_tolerance,
"information_seeking_behavior": self.information_seeking_behavior,
"visual_content_preference": self.visual_content_preference
}
class TraitBasedBehaviorModel:
"""
Handles behavior modeling based on granular personality traits.
"""
def compute_action_probability(self, persona, action: Action) -> float:
"""
Computes the probability of an action based on persona traits.
"""
traits = persona.get("behavioral_traits") or TraitProfile().to_dict()
base_prob = 0.5
# Influence of traits on common actions
if action.type == "SHARE":
base_prob = self.apply_trait_modifiers(base_prob, {"extraversion": traits.get("extraversion", 0.5)})
elif action.type == "COMMENT":
base_prob = self.apply_trait_modifiers(base_prob, {"agreeableness": traits.get("agreeableness", 0.5)})
return base_prob
def apply_trait_modifiers(self, base_probability: float, traits: Dict[str, float]) -> float:
"""
Applies trait-based modifiers to a base probability.
"""
modified_prob = base_probability
for trait, value in traits.items():
# Simplistic linear modification for now
modified_prob *= (0.5 + value)
return min(max(modified_prob, 0.0), 1.0)
@staticmethod
def generate_trait_profile_from_description(description: str) -> TraitProfile:
"""
Uses LLM to infer traits from a persona description.
"""
# Placeholder for LLM-based trait extraction
# In a real implementation, this would call openai_utils.client().send_message(...)
# Example logic for synthetic variation
return TraitProfile(
openness_to_new_ideas=random.uniform(0.1, 0.9),
conscientiousness=random.uniform(0.1, 0.9),
extraversion=random.uniform(0.1, 0.9),
agreeableness=random.uniform(0.1, 0.9),
controversiality_tolerance=random.uniform(0.1, 0.9),
information_seeking_behavior=random.uniform(0.1, 0.9),
visual_content_preference=random.uniform(0.1, 0.9)
)
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