wehub-resource-sync
97e91a83f3
Ruff / Ruff (push) Has been cancelled
Test / Core Tests (push) Has been cancelled
Test / Offline Coverage Tests (Python 3.10) (push) Has been cancelled
Test / Offline Coverage Tests (Python 3.11) (push) Has been cancelled
Test / Offline Coverage Tests (Python 3.12) (push) Has been cancelled
Test / Offline Coverage Tests (Python 3.13) (push) Has been cancelled
Test / Offline Coverage Tests (Python 3.9) (push) Has been cancelled
Test / Full Coverage (Python 3.11) (push) Has been cancelled
Test / Core Provider Tests (OpenAI) (push) Has been cancelled
Test / Core Provider Tests (Anthropic) (push) Has been cancelled
Test / Core Provider Tests (Google) (push) Has been cancelled
Test / Core Provider Tests (Other) (push) Has been cancelled
Test / Anthropic Tests (push) Has been cancelled
Test / Gemini Tests (push) Has been cancelled
Test / Google GenAI Tests (push) Has been cancelled
Test / Vertex AI Tests (push) Has been cancelled
Test / OpenAI Tests (push) Has been cancelled
Test / Writer Tests (push) Has been cancelled
Test / Auto Client Tests (push) Has been cancelled
ty / type-check (push) Has been cancelled
153 lines
5.2 KiB
Python
153 lines
5.2 KiB
Python
from openai import OpenAI
|
|
from io import StringIO
|
|
from typing import Annotated, Any
|
|
from pydantic import (
|
|
BaseModel,
|
|
BeforeValidator,
|
|
PlainSerializer,
|
|
InstanceOf,
|
|
WithJsonSchema,
|
|
)
|
|
import instructor
|
|
import pandas as pd
|
|
from rich.console import Console
|
|
|
|
console = Console()
|
|
client = instructor.from_openai(
|
|
client=OpenAI(),
|
|
mode=instructor.Mode.TOOLS,
|
|
)
|
|
|
|
|
|
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())
|
|
) # type: ignore
|
|
return data
|
|
|
|
|
|
MarkdownDataFrame = Annotated[
|
|
InstanceOf[pd.DataFrame],
|
|
BeforeValidator(md_to_df),
|
|
PlainSerializer(lambda x: x.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 separate""",
|
|
}
|
|
),
|
|
]
|
|
|
|
|
|
class Table(BaseModel):
|
|
caption: str
|
|
dataframe: MarkdownDataFrame
|
|
|
|
|
|
class MultipleTables(BaseModel):
|
|
tables: list[Table]
|
|
|
|
|
|
example = MultipleTables(
|
|
tables=[
|
|
Table(
|
|
caption="This is a caption",
|
|
dataframe=pd.DataFrame(
|
|
{
|
|
"Chart A": [10, 40],
|
|
"Chart B": [20, 50],
|
|
"Chart C": [30, 60],
|
|
}
|
|
),
|
|
)
|
|
]
|
|
)
|
|
|
|
|
|
def extract(url: str) -> MultipleTables:
|
|
return client.chat.completions.create(
|
|
model="gpt-4-turbo",
|
|
max_tokens=4000,
|
|
response_model=MultipleTables,
|
|
messages=[
|
|
{
|
|
"role": "user",
|
|
"content": [
|
|
{
|
|
"type": "image_url",
|
|
"image_url": {"url": url},
|
|
},
|
|
{
|
|
"type": "text",
|
|
"text": """
|
|
First, analyze the image to determine the most appropriate headers for the tables.
|
|
Generate a descriptive h1 for the overall image, followed by a brief summary of the data it contains.
|
|
For each identified table, create an informative h2 title and a concise description of its contents.
|
|
Finally, output the markdown representation of each table.
|
|
|
|
|
|
Make sure to escape the markdown table properly, and make sure to include the caption and the dataframe.
|
|
including escaping all the newlines and quotes. Only return a markdown table in dataframe, nothing else.
|
|
""",
|
|
},
|
|
],
|
|
}
|
|
],
|
|
)
|
|
|
|
|
|
urls = [
|
|
"https://a.storyblok.com/f/47007/2400x1260/f816b031cb/uk-ireland-in-three-charts_chart_a.png/m/2880x0",
|
|
"https://a.storyblok.com/f/47007/2400x2000/bf383abc3c/231031_uk-ireland-in-three-charts_table_v01_b.png/m/2880x0",
|
|
]
|
|
|
|
for url in urls:
|
|
for table in extract(url).tables:
|
|
console.print(table.caption, "\n", table.dataframe)
|
|
"""
|
|
Growth in app installations and sessions across different app categories in Q3 2022 compared to Q2 2022 for Ireland and U.K.
|
|
Install Growth (%) Session Growth (%)
|
|
Category
|
|
Education 7 6
|
|
Games 13 3
|
|
Social 4 -3
|
|
Utilities 6 -0.4
|
|
Top 10 Grossing Android Apps in Ireland, October 2023
|
|
App Name Category
|
|
Rank
|
|
1 Google One Productivity
|
|
2 Disney+ Entertainment
|
|
3 TikTok - Videos, Music & LIVE Entertainment
|
|
4 Candy Crush Saga Games
|
|
5 Tinder: Dating, Chat & Friends Social networking
|
|
6 Coin Master Games
|
|
7 Roblox Games
|
|
8 Bumble - Dating & Make Friends Dating
|
|
9 Royal Match Games
|
|
10 Spotify: Music and Podcasts Music & Audio
|
|
Top 10 Grossing iOS Apps in Ireland, October 2023
|
|
App Name Category
|
|
Rank
|
|
1 Tinder: Dating, Chat & Friends Social networking
|
|
2 Disney+ Entertainment
|
|
3 YouTube: Watch, Listen, Stream Entertainment
|
|
4 Audible: Audio Entertainment Entertainment
|
|
5 Candy Crush Saga Games
|
|
6 TikTok - Videos, Music & LIVE Entertainment
|
|
7 Bumble - Dating & Make Friends Dating
|
|
8 Roblox Games
|
|
9 LinkedIn: Job Search & News Business
|
|
10 Duolingo - Language Lessons Education
|
|
"""
|