ams-meta-analysis 0.1.10

Installable Streamlit GUI for R-backed Python meta-analysis runners.

Meta-Analysis Streamlit App

An installable Streamlit GUI for the R-backed Python meta-analysis runners in /Volumes/Firecuda-4TB/R-code_to-python.

Author: Anuraj Sudhakaran.

The original source directory is treated as read-only. The runner files were copied into this package under meta_analysis_streamlit/runners/ so the app can be installed and launched without editing the source folder.

What It Runs

• Workbook-level auto routing through the original main.py
• Binary pairwise meta-analysis
• Continuous pairwise meta-analysis
• Single-arm proportion and mean meta-analysis
• Diagnostic accuracy meta-analysis
• Frequentist and Bayesian network meta-analysis
• LM Studio model detection, with manual study-characteristic controls when LM Studio is not reachable
• Ollama local model support at http://localhost:11434/v1, with hardware-aware model suggestions and CLI setup guidance when LM Studio is not available

Install

From PyPI after the first release:

python3 -m pip install ams-meta_analysis
meta-analysis-install-r --dry-run
meta-analysis-install-r
meta-analysis-doctor
ams-meta_analysis

If R is installed but Rscript is not on PATH, the app and command-line runner automatically try common R install locations and add the discovered Rscript folder to the current process and analysis runs. To add it permanently in your shell too, run the command shown by meta-analysis-doctor, for example:

export PATH="/Library/Frameworks/R.framework/Resources/bin:$PATH"

From this folder:

python3 -m pip install -e ".[dev]"

For a normal local wheel install:

python3 -m pip install .

Launch the GUI:

ams-meta_analysis

Examples

Binary adverse events. Upload a sheet with study labels plus treatment and control event/total columns. Choose Binary pairwise or let the app assess the sheet, then run to generate summaries, forest plots, and logs.

Multi-outcome workbook. Upload an Excel workbook with sheets such as Adverse Events, Clinical Success, Reintervention, and Length of Stay. Choose Select all sheets with Auto route workbook so the app can route each outcome to the appropriate runner and collect outputs.

Subgroups and meta-regression. Use the GUI to select columns such as etiology, control type, study design, publication year, or sample size. Supported pairwise runners pass those choices into subgroup and meta-regression analyses.

Local model assistance. If LM Studio is unavailable, select Ollama in the sidebar. The app suggests a local model based on available hardware and shows the CLI commands to install Ollama and pull the model.

Updates

The Streamlit sidebar includes a Package updates panel. It checks PyPI for a newer ams-meta_analysis release and can run the upgrade command from inside the app:

python -m pip install --upgrade ams-meta_analysis

After an in-app update, restart Streamlit so the running process loads the new package files.

Local LLM Options

The app first checks LM Studio. If LM Studio is not reachable, it can use Ollama as a local OpenAI-compatible provider. The sidebar estimates whether the machine has GPU support and suggests one model:

• CPU-only or lower-memory systems: llama3.2:3b
• GPU systems with typical memory: llama3.1:8b
• Higher-memory GPU systems: qwen3:14b

Install and start Ollama from the command line, then pull the suggested model:

# macOS with Homebrew
brew install --cask ollama

# Linux
curl -fsSL https://ollama.com/install.sh | sh

# Windows
winget install Ollama.Ollama

ollama serve
ollama pull llama3.2:3b

Run the original workbook orchestrator from the installed package:

meta-analysis-runner "/path/to/workbook.xlsx" --yes

Check the local environment:

meta-analysis-doctor

Install or repair the external R runtime after pip install:

meta-analysis-install-r --dry-run
meta-analysis-install-r

If R is already installed outside PATH, meta-analysis-install-r uses the discovered absolute Rscript path automatically when installing R packages. When R is missing, the Streamlit sidebar also shows an Install R Runtime button that runs the same setup helper from inside the app.

External Requirements

Python dependencies are installed by pip, but the statistical analyses still need R and the R packages used by the original generated scripts. The package does not silently install system software during pip install; instead it installs a helper command that shows and runs the platform-specific R setup:

meta-analysis-install-r --dry-run
meta-analysis-install-r

If you prefer to manage R yourself, install R, make sure Rscript is on PATH, then install the common R packages:

install.packages(c(
  "meta",
  "metafor",
  "readxl",
  "dplyr",
  "ggplot2",
  "netmeta",
  "gemtc",
  "rjags",
  "RTSA"
))

Bayesian network meta-analysis also needs JAGS installed on the system.

Trial sequential analysis can use the external TSA engine expected by the original code. If needed, point to it with:

export TSA_ANALYSIS_PY="/path/to/tsa_analysis.py"

LLM Provider Behavior

By default, the app checks http://localhost:1234/v1 for LM Studio. If LM Studio is not available, it can use OpenAI-compatible hosted APIs instead.

Supported GUI options:

• LM Studio: http://localhost:1234/v1
• OpenAI: https://api.openai.com/v1
• Gemini through Google's OpenAI-compatible endpoint: https://generativelanguage.googleapis.com/v1beta/openai
• Custom OpenAI-compatible endpoint
• Manual/no LLM

The app auto-selects the first available option in this order: LM Studio, OPENAI_API_KEY, GEMINI_API_KEY or GOOGLE_API_KEY, META_ANALYSIS_LLM_API_KEY, then manual mode.

For command-line runs, hosted APIs can also be used with the original --lmstudio-url option because the packaged runners now support OpenAI-compatible authentication headers:

export META_ANALYSIS_LLM_API_KEY="$OPENAI_API_KEY"
meta-analysis-runner "/path/to/workbook.xlsx" \
  --lmstudio-url "https://api.openai.com/v1" \
  --model "your-model" \
  --yes

For Gemini:

export META_ANALYSIS_LLM_API_KEY="$GEMINI_API_KEY"
meta-analysis-runner "/path/to/workbook.xlsx" \
  --lmstudio-url "https://generativelanguage.googleapis.com/v1beta/openai" \
  --model "your-gemini-model" \
  --yes

When no provider is reachable, the GUI still works: it passes --no-lmstudio and asks you to choose basic study characteristics such as analysis route, binary effect size, single-arm outcome type, diagnostic mode, and network model settings.

Output Location

Uploaded workbooks and generated outputs are staged under:

~/.meta_analysis_streamlit/runs/

The app writes a run log and offers a zip download for completed outputs.

Development

Run the tests:

pytest

Build a wheel:

python3 -m build
python3 -m twine check dist/*

See PUBLISHING.md for PyPI release steps, including Trusted Publishing through GitHub Actions.

The project is ready to commit and push from /Volumes/Firecuda-4TB/meta-streamlit-app.