fundas 0.1.1

AI-powered Python library that extends pandas to import and analyze complex, unstructured files

Fundas

Fundamental Data Source - An AI-powered Python library that extends pandas to import and analyze complex, unstructured files.

Overview

Fundas leverages the OpenRouter API and generative AI to intelligently extract features and structured data from various file types based on simple prompts. It seamlessly converts any file into a clean pandas DataFrame for immediate analysis.

Features

• 📄 read_pdf() - Extract structured data from PDF documents
• 🖼️ read_image() - Extract data and text from images
• 🎵 read_audio() - Process audio files and extract information
• 🌐 read_webpage() - Scrape and structure web content
• 🎥 read_video() - Analyze video content from frames, audio, or both

All functions return pandas DataFrames, making the data ready for immediate analysis!

Installation

pip install fundas

Or install from source:

git clone https://github.com/AMSeify/fundas.git
cd fundas
pip install -e .

Quick Start

Setup

First, set your OpenRouter API key. You can either:

Option 1: Use environment variable

export OPENROUTER_API_KEY="your-api-key-here"

Option 2: Use .env file (recommended)

# Copy the example file
cp .env.example .env

# Edit .env and add your credentials:
# OPENROUTER_API_KEY=your-api-key-here
# OPENROUTER_MODEL=openai/gpt-3.5-turbo  # Optional: set default model

Option 3: Pass directly to functions

import fundas as fd

df = fd.read_pdf("document.pdf", api_key="your-api-key-here")

Basic Usage

Read PDF Files

import fundas as fd

# Extract invoice data
df = fd.read_pdf(
    "invoice.pdf",
    prompt="Extract invoice items with product name, quantity, and price"
)
print(df)

Read Images

# Extract data from a chart or screenshot
df = fd.read_image(
    "sales_chart.png",
    prompt="Extract the sales data points from this chart"
)
print(df)

# Process a receipt
df = fd.read_image(
    "receipt.jpg",
    prompt="Extract items and their prices",
    columns=["item", "price", "quantity"]
)

Read Webpages

# Scrape product information
df = fd.read_webpage(
    "https://example.com/products",
    prompt="Extract product names, descriptions, and prices"
)
print(df)

# Extract article data
df = fd.read_webpage(
    "https://news.example.com/article",
    columns=["title", "author", "date", "content"]
)

Read Audio Files

# Transcribe and extract meeting notes
df = fd.read_audio(
    "meeting.mp3",
    prompt="Extract speaker names and key discussion points"
)

Read Video Files

# Analyze video frames
df = fd.read_video(
    "presentation.mp4",
    prompt="Extract slide titles and key points from this presentation",
    from_="pics"  # Extract from video frames
)

# Process audio track
df = fd.read_video(
    "lecture.mp4",
    prompt="Transcribe the lecture and identify key topics",
    from_="audios"  # Extract from audio track
)

# Analyze both video and audio
df = fd.read_video(
    "interview.mp4",
    prompt="Extract interview questions and answers",
    from_="both"  # or from_=["pics", "audios"]
)

Advanced Usage

Specify Columns

You can specify which columns you want to extract:

df = fd.read_pdf(
    "report.pdf",
    prompt="Extract quarterly financial data",
    columns=["quarter", "revenue", "expenses", "profit"]
)

Custom AI Models

Use different AI models via OpenRouter:

# Option 1: Pass model parameter to each function
df = fd.read_image(
    "complex_diagram.png",
    prompt="Extract relationships between components",
    model="anthropic/claude-3-opus"
)

# Option 2: Set default model in .env file
# OPENROUTER_MODEL=anthropic/claude-3-sonnet
df = fd.read_image("diagram.png", prompt="Extract data")  # Uses model from .env

# Option 3: Set via environment variable
import os
os.environ["OPENROUTER_MODEL"] = "openai/gpt-4"
df = fd.read_pdf("document.pdf", prompt="Extract info")

DataFrame Operations

Since all functions return pandas DataFrames, you can immediately use pandas operations:

import fundas as fd

# Read and analyze in one workflow
df = fd.read_pdf("sales.pdf", prompt="Extract sales data")
print(df.head())
print(df.describe())
print(df.groupby('region')['sales'].sum())

Requirements

• Python >= 3.8
• pandas >= 1.3.0
• requests >= 2.25.0
• PyPDF2 >= 3.0.0
• Pillow >= 10.3.0
• beautifulsoup4 >= 4.9.0
• opencv-python >= 4.8.1.78

Advanced Features

Caching

Fundas includes an intelligent caching system to reduce redundant API calls:

import fundas as fd

# Enable caching (enabled by default)
df = fd.read_pdf("document.pdf", prompt="Extract data")

# The same file with the same prompt will use cached results
df2 = fd.read_pdf("document.pdf", prompt="Extract data")  # No API call

# Disable caching if needed
from fundas import OpenRouterClient
client = OpenRouterClient(api_key="key", use_cache=False)

Exporting Data

Export your DataFrames with AI-powered summarization:

import fundas as fd
import pandas as pd

# Create a DataFrame
df = pd.DataFrame({"product": ["A", "B", "C"], "sales": [100, 200, 150]})

# Export to CSV
fd.to_summarized_csv(df, "output.csv")

# Export to Excel with AI summary
fd.to_summarized_excel(
    df,
    "summary.xlsx",
    prompt="Add a summary row with totals"
)

# Generate AI summary
summary = fd.summarize_dataframe(df, prompt="Summarize sales performance")
print(summary)

Error Handling

Fundas includes robust error handling with automatic retries:

import fundas as fd

try:
    df = fd.read_pdf("document.pdf", prompt="Extract data")
except FileNotFoundError:
    print("File not found")
except ValueError as e:
    print(f"Invalid parameters: {e}")
except RuntimeError as e:
    print(f"API error: {e}")

Cache Management

Control the cache behavior:

from fundas import get_cache

cache = get_cache()

# Clear all cache entries
cache.clear()

# Clear only expired entries
cache.clear_expired()

# Disable/enable cache
cache.disable()
cache.enable()

API Reference

Read Functions

All read functions share similar parameters:

Common Parameters:

• filepath or url (str | Path): Source file or URL
• prompt (str): Description of what data to extract
• columns (List[str], optional): Column names to extract
• api_key (str, optional): OpenRouter API key
• model (str, optional): AI model to use (default: gpt-3.5-turbo)

Returns: pandas DataFrame

Export Functions

All export functions accept:

Parameters:

• df (pd.DataFrame): DataFrame to export
• filepath (str | Path): Output file path
• prompt (str, optional): AI transformation prompt
• api_key (str, optional): OpenRouter API key
• model (str, optional): AI model to use

read_pdf(filepath, prompt, columns=None, api_key=None, model=None)

Extract structured data from PDF files.

Parameters:

• filepath (str | Path): Path to the PDF file
• prompt (str): Description of what data to extract
• columns (List[str], optional): Column names to extract
• api_key (str, optional): OpenRouter API key
• model (str, optional): AI model to use

Returns: pandas DataFrame

read_image(filepath, prompt, columns=None, api_key=None, model=None)

Extract structured data from image files.

Parameters:

• filepath (str | Path): Path to the image file
• prompt (str): Description of what data to extract
• columns (List[str], optional): Column names to extract
• api_key (str, optional): OpenRouter API key
• model (str, optional): AI model to use

Returns: pandas DataFrame

read_audio(filepath, prompt, columns=None, api_key=None, model=None)

Extract structured data from audio files.

Parameters:

• filepath (str | Path): Path to the audio file
• prompt (str): Description of what data to extract
• columns (List[str], optional): Column names to extract
• api_key (str, optional): OpenRouter API key
• model (str, optional): AI model to use

Returns: pandas DataFrame

read_webpage(url, prompt, columns=None, api_key=None, model=None)

Extract structured data from web pages.

Parameters:

• url (str): URL of the webpage
• prompt (str): Description of what data to extract
• columns (List[str], optional): Column names to extract
• api_key (str, optional): OpenRouter API key
• model (str, optional): AI model to use

Returns: pandas DataFrame

read_video(filepath, prompt, from_='both', columns=None, api_key=None, model=None, sample_rate=30)

Extract structured data from video files.

Parameters:

• filepath (str | Path): Path to the video file
• prompt (str): Description of what data to extract
• from_ (str | List[str]): Source to extract from - 'pics', 'audios', or 'both'
• columns (List[str], optional): Column names to extract
• api_key (str, optional): OpenRouter API key
• model (str, optional): AI model to use
• sample_rate (int): Frame sampling rate (default: 30)

Returns: pandas DataFrame

to_summarized_csv(df, filepath, prompt=None, api_key=None, model=None, **kwargs)

Export DataFrame to CSV with optional AI-powered summarization.

Parameters:

• df (pd.DataFrame): DataFrame to export
• filepath (str | Path): Path to save the CSV file
• prompt (str, optional): Prompt to transform/summarize data
• api_key (str, optional): OpenRouter API key
• model (str, optional): AI model to use
• **kwargs: Additional arguments for pd.DataFrame.to_csv()

to_summarized_excel(df, filepath, prompt=None, sheet_name="Sheet1", api_key=None, model=None, **kwargs)

Export DataFrame to Excel with optional AI-powered summarization.

to_summarized_json(df, filepath, prompt=None, api_key=None, model=None, orient="records", **kwargs)

Export DataFrame to JSON with optional AI-powered summarization.

summarize_dataframe(df, prompt="Provide a summary of this data", api_key=None, model=None)

Generate an AI-powered summary of a DataFrame.

Returns: str (AI-generated summary)

Configuration

Environment Variables

• OPENROUTER_API_KEY: Your OpenRouter API key

Cache Settings

The cache is stored in ~/.fundas/cache/ by default. You can configure:

• Cache directory location
• Time-to-live (TTL) for cache entries
• Enable/disable caching

Performance Tips

1. Use caching: Keep caching enabled (default) to avoid redundant API calls
2. Specify columns: When you know what columns you need, specify them explicitly
3. Choose the right model: Balance speed, cost, and accuracy by selecting appropriate models
4. Batch operations: Process multiple files in sequence to leverage cache warming

License

MIT License - see LICENSE file for details

Contributing

Contributions are welcome! We appreciate bug fixes, new features, documentation improvements, and more.

Please see our Contributing Guide for details on:

• Setting up your development environment
• Coding standards and style guide
• Testing requirements
• Pull request process

Quick start:

1. Fork the repository
2. Create a feature branch
3. Make your changes with tests
4. Submit a pull request

Support

For issues and questions, please open an issue on GitHub.