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80 lines
2.1 KiB
Python
80 lines
2.1 KiB
Python
import instructor
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from io import StringIO
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from typing import Annotated, Any
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from collections.abc import Iterable
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from pydantic import (
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BeforeValidator,
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InstanceOf,
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WithJsonSchema,
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BaseModel,
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)
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import pandas as pd
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from openai import OpenAI
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import logfire
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openai_client = OpenAI()
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logfire.configure(pydantic_plugin=logfire.PydanticPlugin(record="all"))
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logfire.instrument_openai(openai_client)
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client = instructor.from_openai(openai_client, mode=instructor.Mode.MD_JSON)
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def md_to_df(data: Any) -> Any:
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# Convert markdown to DataFrame
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if isinstance(data, str):
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return (
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pd.read_csv(
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StringIO(data), # Process data
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sep="|",
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index_col=1,
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)
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.dropna(axis=1, how="all")
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.iloc[1:]
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.applymap(lambda x: x.strip())
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)
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return data
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MarkdownDataFrame = Annotated[
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InstanceOf[pd.DataFrame],
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BeforeValidator(md_to_df),
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WithJsonSchema(
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{
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"type": "string",
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"description": "The markdown representation of the table, each one should be tidy, do not try to join tables that should be separate",
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}
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),
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]
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class Table(BaseModel):
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caption: str
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dataframe: MarkdownDataFrame
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@logfire.instrument("extract-table", extract_args=True)
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def extract_table_from_image(url: str) -> Iterable[Table]:
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return client.chat.completions.create(
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model="gpt-4-vision-preview",
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response_model=Iterable[Table],
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max_tokens=1800,
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messages=[
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": "Extract out a table from the image. Only extract out the total number of skiiers.",
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},
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{"type": "image_url", "image_url": {"url": url}},
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],
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}
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],
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)
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url = "https://cdn.statcdn.com/Infographic/images/normal/16330.jpeg"
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tables = extract_table_from_image(url)
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for table in tables:
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print(table.caption, end="\n")
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print(table.dataframe.to_markdown())
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