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127 lines
3.6 KiB
Python
127 lines
3.6 KiB
Python
from openai import OpenAI
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from io import StringIO
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from typing import Annotated, Any
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from pydantic import (
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BaseModel,
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BeforeValidator,
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PlainSerializer,
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InstanceOf,
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WithJsonSchema,
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)
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import instructor
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import pandas as pd
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from langsmith.wrappers import wrap_openai
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from langsmith import traceable
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client = wrap_openai(OpenAI())
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client = instructor.from_openai(
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client, mode=instructor.processing.function_calls.Mode.MD_JSON
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)
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def md_to_df(data: Any) -> Any:
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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), # Get rid of whitespaces
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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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.map(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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PlainSerializer(lambda x: x.to_markdown()),
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WithJsonSchema(
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{
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"type": "string",
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"description": """
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The markdown representation of the table,
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each one should be tidy, do not try to join tables
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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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class MultipleTables(BaseModel):
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tables: list[Table]
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example = MultipleTables(
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tables=[
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Table(
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caption="This is a caption",
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dataframe=pd.DataFrame(
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{
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"Chart A": [10, 40],
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"Chart B": [20, 50],
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"Chart C": [30, 60],
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}
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),
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)
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]
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)
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@traceable(name="extract-table")
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def extract(url: str) -> MultipleTables:
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tables = client.chat.completions.create(
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model="gpt-4-vision-preview",
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max_tokens=4000,
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response_model=MultipleTables,
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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": f"Describe this data accurately as a table in markdown format. {example.model_dump_json(indent=2)}",
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},
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{
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"type": "image_url",
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"image_url": {"url": url},
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},
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{
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"type": "text",
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"text": """
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First take a moment to reason about the best set of headers for the tables.
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Write a good h1 for the image above. Then follow up with a short description of the what the data is about.
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Then for each table you identified, write a h2 tag that is a descriptive title of the table.
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Then follow up with a short description of the what the data is about.
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Lastly, produce the markdown table for each table you identified.
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Make sure to escape the markdown table properly, and make sure to include the caption and the dataframe.
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including escaping all the newlines and quotes. Only return a markdown table in dataframe, nothing else.
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""",
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},
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],
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}
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],
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)
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return tables.model_dump()
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urls = [
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"https://a.storyblok.com/f/47007/2400x1260/f816b031cb/uk-ireland-in-three-charts_chart_a.png/m/2880x0",
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"https://a.storyblok.com/f/47007/2400x2000/bf383abc3c/231031_uk-ireland-in-three-charts_table_v01_b.png/m/2880x0",
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]
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for url in urls:
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tables = extract(url)
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print(tables)
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