Forms and intake

from typing import List, Optional

from agno.agent import Agent
from agno.media import File
from agno.models.openai import OpenAIResponses
from pydantic import BaseModel, Field


class Employment(BaseModel):
    company: str
    title: Optional[str] = None
    start_date: Optional[str] = None
    end_date: Optional[str] = Field(None, description="Null if current")
    summary: Optional[str] = Field(None, description="Bullet points joined into one string")


class Education(BaseModel):
    institution: str
    degree: Optional[str] = None
    field_of_study: Optional[str] = None
    graduation_year: Optional[int] = None


class Resume(BaseModel):
    full_name: Optional[str] = None
    email: Optional[str] = None
    phone: Optional[str] = None
    location: Optional[str] = None
    headline: Optional[str] = Field(None, description="Top-of-page summary line")
    employment: List[Employment] = Field(default_factory=list)
    education: List[Education] = Field(default_factory=list)
    skills: List[str] = Field(default_factory=list)


agent = Agent(
    model=OpenAIResponses(id="gpt-5.5"),
    instructions=(
        "Extract every field from the attached resume PDF. Preserve the "
        "candidate's wording for titles and summaries. Use null when a "
        "field is missing. Do not infer skills that are not on the page."
    ),
    output_schema=Resume,
)

resume = agent.run(
    "Extract this resume.",
    files=[File(filepath="resume.pdf")],
).content
# Illustrative output. Your values depend on resume.pdf:
# Resume(full_name='Sarah Johnson', email='sarah@example.com',
#        headline='Senior Platform Engineer',
#        employment=[Employment(company='Acme Corp', title='Staff Engineer',
#                               start_date='2023-02', end_date=None, ...),
#                    Employment(company='Beta Labs', title='Senior Engineer',
#                               start_date='2019-06', end_date='2023-01', ...)],
#        education=[Education(institution='University of Texas',
#                             degree='B.S.', field_of_study='Computer Science',
#                             graduation_year=2018)],
#        skills=['Python', 'PostgreSQL', 'Kubernetes', 'Terraform'])

class KYCSubmission(BaseModel):
    full_name: str
    date_of_birth: Optional[str] = Field(None, description="ISO 8601")
    country_of_residence: Optional[str] = Field(None, description="ISO 3166-1 alpha-2")
    national_id_type: Optional[str] = Field(None, description="passport, driver_license, national_id")
    national_id_number: Optional[str] = Field(None, description="As printed, including any leading zeros")
    address: Optional[str] = None
    declared_source_of_funds: Optional[str] = None

class Application(BaseModel):
    candidate: Resume
    cover_letter: Optional[str] = None
    references: List[str] = Field(default_factory=list)


application_agent = Agent(
    model=OpenAIResponses(id="gpt-5.5"),
    instructions=(
        "Extract the application from the attached documents: a resume, a cover "
        "letter, and a reference list. Use null when a field is missing."
    ),
    output_schema=Application,
)

application = application_agent.run(
    "Extract this application.",
    files=[
        File(filepath="applications/sjohnson/cover-letter.pdf"),
        File(filepath="applications/sjohnson/resume.pdf"),
        File(filepath="applications/sjohnson/references.pdf"),
    ],
).content

class References(BaseModel):
    items: List[str] = Field(default_factory=list)