LLM

WIP

cool package instructor

manual indexing was beaten out of me at my first job, so when I saw this online I had to check it out.

looks like a cool package too.

#| eval: false
from dotenv import load_dotenv
load_dotenv('../.env')

#| eval: false
from io import StringIO
from typing import Annotated, Any, Iterable
from openai import OpenAI
from pydantic import (
    BaseModel,
    BeforeValidator,
    PlainSerializer,
    InstanceOf,
    WithJsonSchema,
)
import pandas as pd
import instructor


client = instructor.patch(OpenAI(), mode=instructor.function_calls.Mode.MD_JSON)


def to_markdown(df: pd.DataFrame) -> str:
    return df.to_markdown()


def md_to_df(data: Any) -> Any:
    if isinstance(data, str):
        return (
            pd.read_csv(
                StringIO(data),  # Get rid of whitespaces
                sep="|",
                index_col=1,
            )
            .dropna(axis=1, how="all")
            .iloc[1:]
            .map(lambda x: x.strip())
        )
    return data


MarkdownDataFrame = Annotated[
    InstanceOf[pd.DataFrame],
    BeforeValidator(md_to_df),
    PlainSerializer(to_markdown),
    WithJsonSchema(
        {
            "type": "string",
            "description": """
                The markdown representation of the table, 
                each one should be tidy, do not try to join tables
                that should be seperate""",
        }
    ),
]


class Table(BaseModel):
    caption: str
    dataframe: MarkdownDataFrame


def extract_table(url: str) -> Iterable[Table]:
    return client.chat.completions.create(
        model="gpt-4-vision-preview",
        response_model=Iterable[Table],
        max_tokens=1800,
        messages=[
            {
                "role": "user",
                "content": [
                    {
                        "type": "text",
                        "text": """Extract the table from the image, and describe it. 
                        Each table should be tidy, do not try to join tables that 
                        should be seperately described.""",
                    },
                    {
                        "type": "image_url",
                        "image_url": {"url": url},
                    },
                ],
            }
        ],
    )

#| eval: false
url = "https://a.storyblok.com/f/47007/2400x2000/bf383abc3c/231031_uk-ireland-in-three-charts_table_v01_b.png"
tables = extract_table(url)
for tbl in tables:
    print(tbl.caption, end="\n")
    print(tbl.dataframe)