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LLM Spending Tracker

A Streamlit app for ingesting PDF bank statements, classifying spending, correcting categories, and exploring trends with interactive charts.

Purpose

This project is a personal finance workflow for turning PDF bank statements into structured transactions, category mappings, grouped spending views, and chart-based analysis with a lightweight local SQLite store.

Current Limitations

  • Statement ingestion depends on extractable PDF text; scanned PDFs still need OCR before parsing can work well.
  • LLM-backed parsing and categorisation depend on external provider credentials when you want automated classification.
  • The app is designed for local use with a single-user SQLite database rather than concurrent multi-user access.

Features

  • Drag-and-drop PDF statement uploads for multiple bank accounts
  • Automatic transaction extraction and storage in SQLite
  • LLM-based statement parsing through LangChain
  • LLM-first transaction categorisation for new merchants, with persistent mappings for known merchants
  • Merchant/category mappings learned from both LLM classification and user corrections
  • Filters by date, category, account, and transaction type
  • Card-based grouped spending views by month, year, category, account, and more
  • Interactive Plotly visualisations for spending over time

Project Structure

  • app.py: Streamlit home page with overview and grouped spending cards
  • pages/: Streamlit pages for Upload, Mappings, and Charts
  • spending_tracker/db.py: SQLite persistence
  • spending_tracker/parser.py: PDF statement parsing
  • spending_tracker/categorizer.py: Mapping + LLM categorisation logic
  • spending_tracker/analytics.py: Aggregation helpers
  • spending_tracker/services.py: Statement ingestion workflow
  • spending_tracker/config.py: Environment-based app and LLM configuration
  • ui/: Shared Streamlit helpers and page renderers

Running Locally

  1. Create a virtual environment.
  2. Install dependencies:
pip install -e ".[dev]"
  1. Optionally configure LLM provider credentials:
export LLM_PROVIDER=openai
export OPENAI_API_KEY=your_key
export LLM_MODEL=gpt-4.1-mini

Optional runtime configuration:

export SPENDING_TRACKER_DB_PATH=/absolute/path/to/spending_tracker.db

Supported LLM_PROVIDER values:

  • openai
  • anthropic
  • google

If no provider is configured, uncategorised transactions remain for user review until you configure an LLM or map them manually.

  1. Start the app:
streamlit run app.py

Development

Lint the repo:

ruff check .

Format the repo:

ruff format .

Notes

  • Statement ingestion now uses an LLM-based extractor by default. The app expects the model to return ISO-formatted transaction dates, with a light validator before records are stored.
  • If the PDF is scanned and contains no extractable text, OCR is still required before the LLM has anything useful to parse.
  • The first successful LLM category for a new merchant is stored as that merchant's reusable mapping, and user corrections can override it later.
  • The local SQLite database is ignored by git and generated on demand. By default it lives at spending_tracker.db in the project root unless SPENDING_TRACKER_DB_PATH is set.

About

Streamlit app for turning PDF bank statements into categorized transactions, grouped spend views, reusable merchant mappings, and interactive charts.

Resources

Readme
MIT license
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

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Packages

Contributors

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GitHub - mannmann2/spend-tracker: Streamlit app for turning PDF bank statements into categorized transactions, grouped spend views, reusable merchant mappings, and interactive charts. · GitHub
Skip to content
BranchesTags
Repository files navigation
LLM Spending Tracker
A Streamlit app for ingesting PDF bank statements, classifying spending, correcting categories, and exploring trends with interactive charts.
Purpose
This project is a personal finance workflow for turning PDF bank statements into structured transactions, category mappings, grouped spending views, and chart-based analysis with a lightweight local SQLite store.
Current Limitations
  • Statement ingestion depends on extractable PDF text; scanned PDFs still need OCR before parsing can work well.
  • LLM-backed parsing and categorisation depend on external provider credentials when you want automated classification.
  • The app is designed for local use with a single-user SQLite database rather than concurrent multi-user access.
Features
  • Drag-and-drop PDF statement uploads for multiple bank accounts
  • Automatic transaction extraction and storage in SQLite
  • LLM-based statement parsing through LangChain
  • LLM-first transaction categorisation for new merchants, with persistent mappings for known merchants
  • Merchant/category mappings learned from both LLM classification and user corrections
  • Filters by date, category, account, and transaction type
  • Card-based grouped spending views by month, year, category, account, and more
  • Interactive Plotly visualisations for spending over time
Project Structure
  • app.py: Streamlit home page with overview and grouped spending cards
  • pages/: Streamlit pages for Upload, Mappings, and Charts
  • spending_tracker/db.py: SQLite persistence
  • spending_tracker/parser.py: PDF statement parsing
  • spending_tracker/categorizer.py: Mapping + LLM categorisation logic
  • spending_tracker/analytics.py: Aggregation helpers
  • spending_tracker/services.py: Statement ingestion workflow
  • spending_tracker/config.py: Environment-based app and LLM configuration
  • ui/: Shared Streamlit helpers and page renderers
Running Locally
  1. Create a virtual environment.
  2. Install dependencies:
pip install -e ".[dev]"
  1. Optionally configure LLM provider credentials:
export LLM_PROVIDER=openai
export OPENAI_API_KEY=your_key
export LLM_MODEL=gpt-4.1-mini
Optional runtime configuration:
export SPENDING_TRACKER_DB_PATH=/absolute/path/to/spending_tracker.db
Supported LLM_PROVIDER values:
  • openai
  • anthropic
  • google
If no provider is configured, uncategorised transactions remain for user review until you configure an LLM or map them manually.
  1. Start the app:
streamlit run app.py
Development
Lint the repo:
ruff check .
Format the repo:
ruff format .
Notes
  • Statement ingestion now uses an LLM-based extractor by default. The app expects the model to return ISO-formatted transaction dates, with a light validator before records are stored.
  • If the PDF is scanned and contains no extractable text, OCR is still required before the LLM has anything useful to parse.
  • The first successful LLM category for a new merchant is stored as that merchant's reusable mapping, and user corrections can override it later.
  • The local SQLite database is ignored by git and generated on demand. By default it lives at spending_tracker.db in the project root unless SPENDING_TRACKER_DB_PATH is set.
About
Streamlit app for turning PDF bank statements into categorized transactions, grouped spend views, reusable merchant mappings, and interactive charts.
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
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Contributors
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GitHub - mannmann2/spend-tracker: Streamlit app for turning PDF bank statements into categorized transactions, grouped spend views, reusable merchant mappings, and interactive charts. · GitHub
Skip to content
BranchesTags
Repository files navigation
LLM Spending Tracker
A Streamlit app for ingesting PDF bank statements, classifying spending, correcting categories, and exploring trends with interactive charts.
Purpose
This project is a personal finance workflow for turning PDF bank statements into structured transactions, category mappings, grouped spending views, and chart-based analysis with a lightweight local SQLite store.
Current Limitations
  • Statement ingestion depends on extractable PDF text; scanned PDFs still need OCR before parsing can work well.
  • LLM-backed parsing and categorisation depend on external provider credentials when you want automated classification.
  • The app is designed for local use with a single-user SQLite database rather than concurrent multi-user access.
Features
  • Drag-and-drop PDF statement uploads for multiple bank accounts
  • Automatic transaction extraction and storage in SQLite
  • LLM-based statement parsing through LangChain
  • LLM-first transaction categorisation for new merchants, with persistent mappings for known merchants
  • Merchant/category mappings learned from both LLM classification and user corrections
  • Filters by date, category, account, and transaction type
  • Card-based grouped spending views by month, year, category, account, and more
  • Interactive Plotly visualisations for spending over time
Project Structure
  • app.py: Streamlit home page with overview and grouped spending cards
  • pages/: Streamlit pages for Upload, Mappings, and Charts
  • spending_tracker/db.py: SQLite persistence
  • spending_tracker/parser.py: PDF statement parsing
  • spending_tracker/categorizer.py: Mapping + LLM categorisation logic
  • spending_tracker/analytics.py: Aggregation helpers
  • spending_tracker/services.py: Statement ingestion workflow
  • spending_tracker/config.py: Environment-based app and LLM configuration
  • ui/: Shared Streamlit helpers and page renderers
Running Locally
  1. Create a virtual environment.
  2. Install dependencies:
pip install -e ".[dev]"
  1. Optionally configure LLM provider credentials:
export LLM_PROVIDER=openai
export OPENAI_API_KEY=your_key
export LLM_MODEL=gpt-4.1-mini
Optional runtime configuration:
export SPENDING_TRACKER_DB_PATH=/absolute/path/to/spending_tracker.db
Supported LLM_PROVIDER values:
  • openai
  • anthropic
  • google
If no provider is configured, uncategorised transactions remain for user review until you configure an LLM or map them manually.
  1. Start the app:
streamlit run app.py
Development
Lint the repo:
ruff check .
Format the repo:
ruff format .
Notes
  • Statement ingestion now uses an LLM-based extractor by default. The app expects the model to return ISO-formatted transaction dates, with a light validator before records are stored.
  • If the PDF is scanned and contains no extractable text, OCR is still required before the LLM has anything useful to parse.
  • The first successful LLM category for a new merchant is stored as that merchant's reusable mapping, and user corrections can override it later.
  • The local SQLite database is ignored by git and generated on demand. By default it lives at spending_tracker.db in the project root unless SPENDING_TRACKER_DB_PATH is set.
About
Streamlit app for turning PDF bank statements into categorized transactions, grouped spend views, reusable merchant mappings, and interactive charts.
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
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GitHub - mannmann2/spend-tracker: Streamlit app for turning PDF bank statements into categorized transactions, grouped spend views, reusable merchant mappings, and interactive charts. · GitHub
Skip to content
BranchesTags
Repository files navigation
LLM Spending Tracker
A Streamlit app for ingesting PDF bank statements, classifying spending, correcting categories, and exploring trends with interactive charts.
Purpose
This project is a personal finance workflow for turning PDF bank statements into structured transactions, category mappings, grouped spending views, and chart-based analysis with a lightweight local SQLite store.
Current Limitations
  • Statement ingestion depends on extractable PDF text; scanned PDFs still need OCR before parsing can work well.
  • LLM-backed parsing and categorisation depend on external provider credentials when you want automated classification.
  • The app is designed for local use with a single-user SQLite database rather than concurrent multi-user access.
Features
  • Drag-and-drop PDF statement uploads for multiple bank accounts
  • Automatic transaction extraction and storage in SQLite
  • LLM-based statement parsing through LangChain
  • LLM-first transaction categorisation for new merchants, with persistent mappings for known merchants
  • Merchant/category mappings learned from both LLM classification and user corrections
  • Filters by date, category, account, and transaction type
  • Card-based grouped spending views by month, year, category, account, and more
  • Interactive Plotly visualisations for spending over time
Project Structure
  • app.py: Streamlit home page with overview and grouped spending cards
  • pages/: Streamlit pages for Upload, Mappings, and Charts
  • spending_tracker/db.py: SQLite persistence
  • spending_tracker/parser.py: PDF statement parsing
  • spending_tracker/categorizer.py: Mapping + LLM categorisation logic
  • spending_tracker/analytics.py: Aggregation helpers
  • spending_tracker/services.py: Statement ingestion workflow
  • spending_tracker/config.py: Environment-based app and LLM configuration
  • ui/: Shared Streamlit helpers and page renderers
Running Locally
  1. Create a virtual environment.
  2. Install dependencies:
pip install -e ".[dev]"
  1. Optionally configure LLM provider credentials:
export LLM_PROVIDER=openai
export OPENAI_API_KEY=your_key
export LLM_MODEL=gpt-4.1-mini
Optional runtime configuration:
export SPENDING_TRACKER_DB_PATH=/absolute/path/to/spending_tracker.db
Supported LLM_PROVIDER values:
  • openai
  • anthropic
  • google
If no provider is configured, uncategorised transactions remain for user review until you configure an LLM or map them manually.
  1. Start the app:
streamlit run app.py
Development
Lint the repo:
ruff check .
Format the repo:
ruff format .
Notes
  • Statement ingestion now uses an LLM-based extractor by default. The app expects the model to return ISO-formatted transaction dates, with a light validator before records are stored.
  • If the PDF is scanned and contains no extractable text, OCR is still required before the LLM has anything useful to parse.
  • The first successful LLM category for a new merchant is stored as that merchant's reusable mapping, and user corrections can override it later.
  • The local SQLite database is ignored by git and generated on demand. By default it lives at spending_tracker.db in the project root unless SPENDING_TRACKER_DB_PATH is set.
About
Streamlit app for turning PDF bank statements into categorized transactions, grouped spend views, reusable merchant mappings, and interactive charts.
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

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var __m = "youtube.com";
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GitHub - mannmann2/spend-tracker: Streamlit app for turning PDF bank statements into categorized transactions, grouped spend views, reusable merchant mappings, and interactive charts. · GitHub
Skip to content
BranchesTags
Repository files navigation
LLM Spending Tracker
A Streamlit app for ingesting PDF bank statements, classifying spending, correcting categories, and exploring trends with interactive charts.
Purpose
This project is a personal finance workflow for turning PDF bank statements into structured transactions, category mappings, grouped spending views, and chart-based analysis with a lightweight local SQLite store.
Current Limitations
  • Statement ingestion depends on extractable PDF text; scanned PDFs still need OCR before parsing can work well.
  • LLM-backed parsing and categorisation depend on external provider credentials when you want automated classification.
  • The app is designed for local use with a single-user SQLite database rather than concurrent multi-user access.
Features
  • Drag-and-drop PDF statement uploads for multiple bank accounts
  • Automatic transaction extraction and storage in SQLite
  • LLM-based statement parsing through LangChain
  • LLM-first transaction categorisation for new merchants, with persistent mappings for known merchants
  • Merchant/category mappings learned from both LLM classification and user corrections
  • Filters by date, category, account, and transaction type
  • Card-based grouped spending views by month, year, category, account, and more
  • Interactive Plotly visualisations for spending over time
Project Structure
  • app.py: Streamlit home page with overview and grouped spending cards
  • pages/: Streamlit pages for Upload, Mappings, and Charts
  • spending_tracker/db.py: SQLite persistence
  • spending_tracker/parser.py: PDF statement parsing
  • spending_tracker/categorizer.py: Mapping + LLM categorisation logic
  • spending_tracker/analytics.py: Aggregation helpers
  • spending_tracker/services.py: Statement ingestion workflow
  • spending_tracker/config.py: Environment-based app and LLM configuration
  • ui/: Shared Streamlit helpers and page renderers
Running Locally
  1. Create a virtual environment.
  2. Install dependencies:
pip install -e ".[dev]"
  1. Optionally configure LLM provider credentials:
export LLM_PROVIDER=openai
export OPENAI_API_KEY=your_key
export LLM_MODEL=gpt-4.1-mini
Optional runtime configuration:
export SPENDING_TRACKER_DB_PATH=/absolute/path/to/spending_tracker.db
Supported LLM_PROVIDER values:
  • openai
  • anthropic
  • google
If no provider is configured, uncategorised transactions remain for user review until you configure an LLM or map them manually.
  1. Start the app:
streamlit run app.py
Development
Lint the repo:
ruff check .
Format the repo:
ruff format .
Notes
  • Statement ingestion now uses an LLM-based extractor by default. The app expects the model to return ISO-formatted transaction dates, with a light validator before records are stored.
  • If the PDF is scanned and contains no extractable text, OCR is still required before the LLM has anything useful to parse.
  • The first successful LLM category for a new merchant is stored as that merchant's reusable mapping, and user corrections can override it later.
  • The local SQLite database is ignored by git and generated on demand. By default it lives at spending_tracker.db in the project root unless SPENDING_TRACKER_DB_PATH is set.
About
Streamlit app for turning PDF bank statements into categorized transactions, grouped spend views, reusable merchant mappings, and interactive charts.
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

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GitHub - mannmann2/spend-tracker: Streamlit app for turning PDF bank statements into categorized transactions, grouped spend views, reusable merchant mappings, and interactive charts. · GitHub
Skip to content
BranchesTags
Repository files navigation
LLM Spending Tracker
A Streamlit app for ingesting PDF bank statements, classifying spending, correcting categories, and exploring trends with interactive charts.
Purpose
This project is a personal finance workflow for turning PDF bank statements into structured transactions, category mappings, grouped spending views, and chart-based analysis with a lightweight local SQLite store.
Current Limitations
  • Statement ingestion depends on extractable PDF text; scanned PDFs still need OCR before parsing can work well.
  • LLM-backed parsing and categorisation depend on external provider credentials when you want automated classification.
  • The app is designed for local use with a single-user SQLite database rather than concurrent multi-user access.
Features
  • Drag-and-drop PDF statement uploads for multiple bank accounts
  • Automatic transaction extraction and storage in SQLite
  • LLM-based statement parsing through LangChain
  • LLM-first transaction categorisation for new merchants, with persistent mappings for known merchants
  • Merchant/category mappings learned from both LLM classification and user corrections
  • Filters by date, category, account, and transaction type
  • Card-based grouped spending views by month, year, category, account, and more
  • Interactive Plotly visualisations for spending over time
Project Structure
  • app.py: Streamlit home page with overview and grouped spending cards
  • pages/: Streamlit pages for Upload, Mappings, and Charts
  • spending_tracker/db.py: SQLite persistence
  • spending_tracker/parser.py: PDF statement parsing
  • spending_tracker/categorizer.py: Mapping + LLM categorisation logic
  • spending_tracker/analytics.py: Aggregation helpers
  • spending_tracker/services.py: Statement ingestion workflow
  • spending_tracker/config.py: Environment-based app and LLM configuration
  • ui/: Shared Streamlit helpers and page renderers
Running Locally
  1. Create a virtual environment.
  2. Install dependencies:
pip install -e ".[dev]"
  1. Optionally configure LLM provider credentials:
export LLM_PROVIDER=openai
export OPENAI_API_KEY=your_key
export LLM_MODEL=gpt-4.1-mini
Optional runtime configuration:
export SPENDING_TRACKER_DB_PATH=/absolute/path/to/spending_tracker.db
Supported LLM_PROVIDER values:
  • openai
  • anthropic
  • google
If no provider is configured, uncategorised transactions remain for user review until you configure an LLM or map them manually.
  1. Start the app:
streamlit run app.py
Development
Lint the repo:
ruff check .
Format the repo:
ruff format .
Notes
  • Statement ingestion now uses an LLM-based extractor by default. The app expects the model to return ISO-formatted transaction dates, with a light validator before records are stored.
  • If the PDF is scanned and contains no extractable text, OCR is still required before the LLM has anything useful to parse.
  • The first successful LLM category for a new merchant is stored as that merchant's reusable mapping, and user corrections can override it later.
  • The local SQLite database is ignored by git and generated on demand. By default it lives at spending_tracker.db in the project root unless SPENDING_TRACKER_DB_PATH is set.
About
Streamlit app for turning PDF bank statements into categorized transactions, grouped spend views, reusable merchant mappings, and interactive charts.
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GitHub - mannmann2/spend-tracker: Streamlit app for turning PDF bank statements into categorized transactions, grouped spend views, reusable merchant mappings, and interactive charts. · GitHub
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LLM Spending Tracker
A Streamlit app for ingesting PDF bank statements, classifying spending, correcting categories, and exploring trends with interactive charts.
Purpose
This project is a personal finance workflow for turning PDF bank statements into structured transactions, category mappings, grouped spending views, and chart-based analysis with a lightweight local SQLite store.
Current Limitations
  • Statement ingestion depends on extractable PDF text; scanned PDFs still need OCR before parsing can work well.
  • LLM-backed parsing and categorisation depend on external provider credentials when you want automated classification.
  • The app is designed for local use with a single-user SQLite database rather than concurrent multi-user access.
Features
  • Drag-and-drop PDF statement uploads for multiple bank accounts
  • Automatic transaction extraction and storage in SQLite
  • LLM-based statement parsing through LangChain
  • LLM-first transaction categorisation for new merchants, with persistent mappings for known merchants
  • Merchant/category mappings learned from both LLM classification and user corrections
  • Filters by date, category, account, and transaction type
  • Card-based grouped spending views by month, year, category, account, and more
  • Interactive Plotly visualisations for spending over time
Project Structure
  • app.py: Streamlit home page with overview and grouped spending cards
  • pages/: Streamlit pages for Upload, Mappings, and Charts
  • spending_tracker/db.py: SQLite persistence
  • spending_tracker/parser.py: PDF statement parsing
  • spending_tracker/categorizer.py: Mapping + LLM categorisation logic
  • spending_tracker/analytics.py: Aggregation helpers
  • spending_tracker/services.py: Statement ingestion workflow
  • spending_tracker/config.py: Environment-based app and LLM configuration
  • ui/: Shared Streamlit helpers and page renderers
Running Locally
  1. Create a virtual environment.
  2. Install dependencies:
pip install -e ".[dev]"
  1. Optionally configure LLM provider credentials:
export LLM_PROVIDER=openai
export OPENAI_API_KEY=your_key
export LLM_MODEL=gpt-4.1-mini
Optional runtime configuration:
export SPENDING_TRACKER_DB_PATH=/absolute/path/to/spending_tracker.db
Supported LLM_PROVIDER values:
  • openai
  • anthropic
  • google
If no provider is configured, uncategorised transactions remain for user review until you configure an LLM or map them manually.
  1. Start the app:
streamlit run app.py
Development
Lint the repo:
ruff check .
Format the repo:
ruff format .
Notes
  • Statement ingestion now uses an LLM-based extractor by default. The app expects the model to return ISO-formatted transaction dates, with a light validator before records are stored.
  • If the PDF is scanned and contains no extractable text, OCR is still required before the LLM has anything useful to parse.
  • The first successful LLM category for a new merchant is stored as that merchant's reusable mapping, and user corrections can override it later.
  • The local SQLite database is ignored by git and generated on demand. By default it lives at spending_tracker.db in the project root unless SPENDING_TRACKER_DB_PATH is set.
About
Streamlit app for turning PDF bank statements into categorized transactions, grouped spend views, reusable merchant mappings, and interactive charts.
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
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GitHub - mannmann2/spend-tracker: Streamlit app for turning PDF bank statements into categorized transactions, grouped spend views, reusable merchant mappings, and interactive charts. · GitHub
Skip to content
BranchesTags
Repository files navigation
LLM Spending Tracker
A Streamlit app for ingesting PDF bank statements, classifying spending, correcting categories, and exploring trends with interactive charts.
Purpose
This project is a personal finance workflow for turning PDF bank statements into structured transactions, category mappings, grouped spending views, and chart-based analysis with a lightweight local SQLite store.
Current Limitations
  • Statement ingestion depends on extractable PDF text; scanned PDFs still need OCR before parsing can work well.
  • LLM-backed parsing and categorisation depend on external provider credentials when you want automated classification.
  • The app is designed for local use with a single-user SQLite database rather than concurrent multi-user access.
Features
  • Drag-and-drop PDF statement uploads for multiple bank accounts
  • Automatic transaction extraction and storage in SQLite
  • LLM-based statement parsing through LangChain
  • LLM-first transaction categorisation for new merchants, with persistent mappings for known merchants
  • Merchant/category mappings learned from both LLM classification and user corrections
  • Filters by date, category, account, and transaction type
  • Card-based grouped spending views by month, year, category, account, and more
  • Interactive Plotly visualisations for spending over time
Project Structure
  • app.py: Streamlit home page with overview and grouped spending cards
  • pages/: Streamlit pages for Upload, Mappings, and Charts
  • spending_tracker/db.py: SQLite persistence
  • spending_tracker/parser.py: PDF statement parsing
  • spending_tracker/categorizer.py: Mapping + LLM categorisation logic
  • spending_tracker/analytics.py: Aggregation helpers
  • spending_tracker/services.py: Statement ingestion workflow
  • spending_tracker/config.py: Environment-based app and LLM configuration
  • ui/: Shared Streamlit helpers and page renderers
Running Locally
  1. Create a virtual environment.
  2. Install dependencies:
pip install -e ".[dev]"
  1. Optionally configure LLM provider credentials:
export LLM_PROVIDER=openai
export OPENAI_API_KEY=your_key
export LLM_MODEL=gpt-4.1-mini
Optional runtime configuration:
export SPENDING_TRACKER_DB_PATH=/absolute/path/to/spending_tracker.db
Supported LLM_PROVIDER values:
  • openai
  • anthropic
  • google
If no provider is configured, uncategorised transactions remain for user review until you configure an LLM or map them manually.
  1. Start the app:
streamlit run app.py
Development
Lint the repo:
ruff check .
Format the repo:
ruff format .
Notes
  • Statement ingestion now uses an LLM-based extractor by default. The app expects the model to return ISO-formatted transaction dates, with a light validator before records are stored.
  • If the PDF is scanned and contains no extractable text, OCR is still required before the LLM has anything useful to parse.
  • The first successful LLM category for a new merchant is stored as that merchant's reusable mapping, and user corrections can override it later.
  • The local SQLite database is ignored by git and generated on demand. By default it lives at spending_tracker.db in the project root unless SPENDING_TRACKER_DB_PATH is set.
About
Streamlit app for turning PDF bank statements into categorized transactions, grouped spend views, reusable merchant mappings, and interactive charts.
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages