services/models/data_model.py

from config.get_config import master_config
from datetime import datetime
from pydantic import Field, BaseModel
from typing import Optional, List, Literal, Dict
from typing_extensions import TypedDict
from uuid import uuid4



tzinfo = master_config.tzinfo

LOGIC_NUMERIC = Literal["greater than", 
                         "less than", 
                         "equal",
                         "greater than or equal",
                         "less than or equal" 
                         ]

LOGIC_CATEGORICAL = Literal[ 
                         "equal",
                         "similar", 
                         "not similar"]


class RawProfile(TypedDict):
    profile_id: str
    content_type: Literal["pdf", "docx", "txt"] = "pdf"
    filename: str
    content: str

class AIProfile(TypedDict):
    fullname: str = Field(description="Fullname of the candidate", default="-")
    # gender: str = Field(description="Gender of the candidate, if available", default="null")
    # age: int = Field(description="Age in number")
    gpa_edu_1: float = Field(description="""GPA of candidate's bachelor degree (same like sarjana, s1, undergradute), if exists.""", default=0)
    univ_edu_1: str = Field(description="""University where candidate take bachelor degree, if exists.""", default="-")
    major_edu_1: str = Field(description="""Major of candidate's bachelor degree, if exists.""", default="-")
    
    gpa_edu_2: float = Field(description="""GPA of candidate's master degree (same like master, s2, postgraduate), if exists.""", default=0)
    univ_edu_2: str = Field(description="""University where candidate take master degree, if exists.""", default="-")
    major_edu_2: str = Field(description="""Major of candidate's master degree, if exists.""", default="-")
    
    gpa_edu_3: float = Field(description="""GPA of candidate's doctoral or phd degree (same like phd, s3, doctoral), if exists.""", default=0)
    univ_edu_3: str = Field(description="""University where candidate take doctoral or phd degree, if exists.""", default="-")
    major_edu_3: str = Field(description="""Major of candidate's doctoral or phd degree, if exists.""", default="-")
    
    domicile: str = Field(description="Current domicile of the candidate", default="-")

    yoe: float = Field(description="The candidate's total years of experience (as an float)", default=0)
    hardskills: Optional[List[str]] = Field(description="List of the candidate's hard skills",default_factory=list)
    softskills: Optional[List[str]] = Field(description="List of the candidate's soft skills", default_factory=list)
    certifications: Optional[List[str]] = Field(description="List of the candidate's certifications", default_factory=list)
    business_domain: Optional[List[str]] = Field(description="List of the candidate's business domain experience based on working experience or project", default_factory=list)

class AIProfileTbScore(TypedDict):
    fullname: str = Field(description="Fullname of the candidate", default="-")
    # gender: str = Field(description="Gender of the candidate, if available", default="null")
    # age: int = Field(description="Age in number")
    gpa_edu_1: float = Field(description="""GPA of candidate's bachelor degree (same like sarjana, s1, undergradute), if exists.""", default=0)
    univ_edu_1: list = Field(description="""University where candidate take bachelor degree, if exists.""", default="-")
    major_edu_1: list = Field(description="""Major of candidate's bachelor degree, if exists.""", default="-")
    
    gpa_edu_2: float = Field(description="""GPA of candidate's master degree (same like master, s2, postgraduate), if exists.""", default=0)
    univ_edu_2: list = Field(description="""University where candidate take master degree, if exists.""", default="-")
    major_edu_2: list = Field(description="""Major of candidate's master degree, if exists.""", default="-")
    
    gpa_edu_3: float = Field(description="""GPA of candidate's doctoral or phd degree (same like phd, s3, doctoral), if exists.""", default=0)
    univ_edu_3: list = Field(description="""University where candidate take doctoral or phd degree, if exists.""", default="-")
    major_edu_3: list = Field(description="""Major of candidate's doctoral or phd degree, if exists.""", default="-")
    
    domicile: str = Field(description="Current domicile of the candidate", default="-")

    yoe: float = Field(description="The candidate's total years of experience (as an float)", default=0)
    hardskills: Optional[List[str]] = Field(description="List of the candidate's hard skills",default_factory=list)
    softskills: Optional[List[str]] = Field(description="List of the candidate's soft skills", default_factory=list)
    certifications: Optional[List[str]] = Field(description="List of the candidate's certifications", default_factory=list)
    business_domain: Optional[List[str]] = Field(description="List of the candidate's business domain experience based on working experience or project", default_factory=list)


class Profile(AIProfile):
    profile_id: str
    created_at: datetime = datetime.now().replace(tzinfo=tzinfo)

class Profiles(TypedDict):
    profiles: List[Profile]

class Criteria(TypedDict):
    gpa_edu_1: Optional[float] = 0
    univ_edu_1: Optional[List] = []
    major_edu_1: Optional[List] = []
    gpa_edu_2: Optional[float] = 0
    univ_edu_2: Optional[List] = []
    major_edu_2: Optional[List] = []
    gpa_edu_3: Optional[float] = 0
    univ_edu_3: Optional[List] = []
    major_edu_3: Optional[List] = []
    domicile: Optional[str] = None
    yoe: Optional[int] = 0
    hardskills: Optional[List] = []
    softskills: Optional[List] = []
    certifications: Optional[List] = []
    business_domain: Optional[List] = []


class CriteriaWeight(TypedDict):
    gpa_edu_1: Optional[float] = 0
    univ_edu_1: Optional[float] = 0
    major_edu_1: Optional[float] = 0
    gpa_edu_2: Optional[float] = 0
    univ_edu_2: Optional[float] = 0
    major_edu_2: Optional[float] = 0
    gpa_edu_3: Optional[float] = 0
    univ_edu_3: Optional[float] = 0
    major_edu_3: Optional[float] = 0
    domicile: Optional[float] = 0
    yoe: Optional[float] = 0
    hardskills: Optional[float] = 0
    softskills: Optional[float] = 0
    certifications: Optional[float] = 0
    business_domain: Optional[float] = 0


# class InputScoring(AIProfile):
#     profile_id: str = Field(description="profile id")
#     criteria: Criteria = Field(description="Criteria to be matched with the profile")
#     criteria_weight: CriteriaWeight = Field(description="Criteria weight to be applied when profile matching")

class InputScoring(TypedDict):
    profile_id: str = Field(description="profile id")
    weight_id: str =  Field(description="weight id")

class InputScoringBulk(TypedDict): #TODO: USE THIS ON /v2/calculate_score
    criteria: Criteria = Field(description="Criteria to be matched with the profile")
    criteria_weight: CriteriaWeight = Field(description="Criteria weight to be applied when profile matching")

class PayloadExtractOne(TypedDict):
    profile_id: str = str(uuid4())
    filename: str
    content_type: Literal["pdf", "docx", "txt"] = "pdf"
    content: str
class DataResponseExtractOne(TypedDict):
    profile_id: Optional[str]
class ResponseExtractOne(TypedDict):
    status: Literal["success", "failed", "canceled"]
    message: Optional[str] = "empty"
    data: Optional[DataResponseExtractOne] = None
    

# EXTRACT PROFILE BULK
class DataResponseExtractBulk(TypedDict):
    status_ids: Dict
    criteria_id: str
class PayloadExtractBulk(TypedDict):
    profile_id: str
    content_type: Literal["pdf", "docx", "txt"] = "pdf"
    content: str
class ResponseExtractBulk(TypedDict):
    status: Literal["success", "partial-success", "failed", "canceled"]
    message: Optional[str]
    data: Optional[DataResponseExtractBulk] = None


# MATCH PROFILE ONE
class PayloadMatchOne(TypedDict):
    profile_id: str
    criteria: Criteria
    criteria_weight: CriteriaWeight

class Score(TypedDict):
    profile_id: str
    score: float
class DataResponseMatchOne(TypedDict):
    profile_id: str
    criteria_id: Optional[str] = None
    matching_id: Optional[str] = None
    scoring_id: Optional[str] = None
    score: Optional[float] = None
class ResponseMatchOne(Score):
    status: Literal["success", "failed", "canceled"]
    message: Optional[str] = None
    data: Optional[DataResponseMatchOne] = None


# MATCH PROFILE BULK
class DataResponseMatchBulk(TypedDict):
    status_ids: Dict
    criteria_id: str
class PayloadMatchBulk(TypedDict):
    filter: List[str]
    criteria: Criteria
    criteria_weight: CriteriaWeight
class ResponseMatchBulk(TypedDict):
    status: Literal["success", "partial-success", "failed", "canceled"]
    message: Optional[str]
    data: Optional[DataResponseExtractBulk] = None

# class DataResponseExtractBulk(TypedDict):
#     status_ids: Dict
#     criteria_id: str
# class PayloadExtractBulk(TypedDict):
#     profile_id: str
#     content_type: Literal["pdf", "docx", "txt"] = "pdf"
#     content: str
# class ResponseExtractBulk(TypedDict):
#     status: Literal["success", "partial-success", "failed", "canceled"]
#     message: Optional[str]
#     data: Optional[DataResponseExtractBulk] = None


desc_AIMatchProfile = "choose 1 if match else 0"
class AIMatchProfile(TypedDict):
    gpa_edu_1: Optional[Literal[1, 0]] = Field(description=desc_AIMatchProfile, default=None)
    univ_edu_1: Optional[Literal[1, 0]] = Field(description=desc_AIMatchProfile, default=None)
    major_edu_1: Optional[Literal[1, 0]] = Field(description=desc_AIMatchProfile, default=None)
    gpa_edu_2: Optional[Literal[1, 0]] = Field(description=desc_AIMatchProfile, default=None)
    univ_edu_2: Optional[Literal[1, 0]] = Field(description=desc_AIMatchProfile, default=None)
    major_edu_2: Optional[Literal[1, 0]] = Field(description=desc_AIMatchProfile, default=None)
    gpa_edu_3: Optional[Literal[1, 0]] = Field(description=desc_AIMatchProfile, default=None)
    univ_edu_3: Optional[Literal[1, 0]] = Field(description=desc_AIMatchProfile, default=None)
    major_edu_3: Optional[Literal[1, 0]] = Field(description=desc_AIMatchProfile, default=None)
    domicile: Optional[Literal[1, 0]] = Field(description=desc_AIMatchProfile, default=None)
    yoe: Optional[Literal[1, 0]] = Field(description=desc_AIMatchProfile, default=None)
    hardskills: Optional[Literal[1, 0]] = Field(description=desc_AIMatchProfile, default=None)
    softskills: Optional[Literal[1, 0]] = Field(description=desc_AIMatchProfile, default=None)
    certifications: Optional[Literal[1, 0]] = Field(description=desc_AIMatchProfile, default=None)
    business_domain: Optional[Literal[1, 0]] = Field(description=desc_AIMatchProfile, default=None)


class OutProfile(BaseModel):
    fullname: str = Field(description="Fullname of the candidate", default="-")
    # gender: str = Field(description="Gender of the candidate, if available", default="null")
    # age: int = Field(description="Age in number")
    high_edu_univ_1: str = Field(description="""University where candidate take bachelor degree, if exists.""", default="-")
    high_edu_major_1: str = Field(description="""Major of candidate's bachelor degree, if exists.""", default="-")
    high_edu_gpa_1: float = Field(description="""GPA of candidate's bachelor degree, if exists.""", default=0)
    
    high_edu_univ_2: str = Field(description="""University where candidate take master degree, if exists.""", default="-")
    high_edu_major_2: str = Field(description="""Major of candidate's master degree, if exists.""", default="-")
    high_edu_gpa_2: float = Field(description="""GPA of candidate's master degree, if exists.""", default=0)
    
    high_edu_univ_3: str = Field(description="""University where candidate take doctoral or phd degree, if exists.""", default="-")
    high_edu_major_3: str = Field(description="""Major of candidate's doctoral or phd degree, if exists.""", default="-")
    high_edu_gpa_3: float = Field(description="""GPA of candidate's doctoral or phd degree, if exists.""", default=0)
    domicile: str = Field(description="Current domicile of the candidate", default="-")

    yoe: float = Field(description="The candidate's total years of experience (as an float)", default=0)
    hardskills: Optional[List[str]] = Field(description="List of the candidate's hard skills", default=[])
    softskills: Optional[List[str]] = Field(description="List of the candidate's soft skills", default=[])
    certifications: Optional[List[str]] = Field(description="List of the candidate's certifications", default=[])
    business_domain_experiences: Optional[List[str]] = Field(description="List of the candidate's business domain experience based on working experience or project", default=[])

class OutMatching(BaseModel):
    score: int = Field(description="Score of profile matching, in range 0-100. If profile and criteria is closed then will give higher score.", default=0)
    reason: str = Field(description="Reason behind why you give that such score to current profile.", default="-")