semvec/pypi-packages huggingface dataset:
neutrophils-classifier-app 1.0.2
A GUI application for neutrophil classification using deep learning models
License: MIT
Requires Python: >=3.9

# Neutrophils Maturation Classifier App

![alt text](./doc/screenshot.png "Screenshot")

A minimal 3D neutrophil maturation classification app with convolution neural network.

## Overview

This application allows for the automated classification of neutrophils at different maturation stages using a 3D convolutional neural network. It's designed to assist researchers and clinicians in analyzing 3D neutrophil morphology with geometric and CNN embedded features.

**Note**: GPU runtime only available on Linux.

## Features

- 3D visualization of neutrophil isosurfaces
- Real-time classification of neutrophil maturation stages
- Support for TIFF image input
- Batch processing capabilities

## Installation
### Installation with Conda (Mamba)

1. [Download Miniforge](https://github.com/conda-forge/miniforge#miniforge3) and install it if you don't have conda/mamba.
2. Create and activate a new environment:
    ```bash
    mamba create -n neutrophils python=3.10
    mamba activate neutrophils
    ```
3. Install the package:
    ```bash
    pip install neutrophils-classifier-app
    ```

**Note**: This package now uses the published `neutrophils-core` library from PyPI, which provides the core machine learning functionality.

### Run the App
```bash
neutrophil-classifier
```

*Note*: The first launch may take up to 10 minutes as the application performs initial setup and caching. Subsequent startups will be significantly faster.

## For Developers

### Development Setup
1. Set up the conda environment:
```bash
git clone git@github.com:bpi-oxford/Neutrophils-Classifier-App.git
cd Neutrophils-Classifier-App
git submodule update --init --recursive
mamba env create -f env.yaml
mamba activate neutrophils
```

2. Install in editable mode:
```bash
pip install -e .
```

3. Run the application:
```bash
neutrophil-classifier
```

### Packaging Setup
1. Set up the conda environment:
```bash
mamba env create -f env-minimal.yaml
mamba activate neutrophils-minimal
```

2. See [`BUILD_GUIDE.md`](BUILD_GUIDE.md) for detailed build and publish instructions.
flumes-ai 0.2.3
Flumes – unified memory infrastructure SDK for AI agents (Python)
License: MIT
Requires Python: >=3.9

# Flumes AI – Python SDK
![PyPI](https://img.shields.io/pypi/v/flumes-ai?logo=pypi) ![License](https://img.shields.io/github/license/alex-n-a/flumes-ai-sdk) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/alex-n-a/flumes-ai-sdk/blob/main/learning/agent_memory_tutorial_flumes-ai.ipynb)

Flumes AI is a **unified memory infrastructure** for LLM-powered agents and applications. This SDK lets any Python or LangChain stack plug into the [Flumes Memory API](https://docs.flumes.ai/) in **three lines of code**.

👉 Learn more at <https://www.flumes.ai/> – full docs at <https://docs.flumes.ai/>.

---

## 🚀 Installation

```bash
pip install flumes-ai  # includes the OpenAI client by default
```

Python ≥3.8.  No system packages or C-extensions required.

---

## 🔑 Configuration

Environment variables (preferred):

| Service | Variable | Example |
|---------|----------|---------|
| **Flumes** | `FLUMES_API_KEY` | `export FLUMES_API_KEY=sk_live_…` |
| **OpenAI** | `OPENAI_API_KEY` | `export OPENAI_API_KEY=sk-…` |

You can also pass `api_key="…"` directly to `MemoryClient`.

---

## ⚡ Quick-start (entity-first; no summarize route)

```python
from flumes import MemoryClient

client = MemoryClient(api_key="YOUR_KEY", agent_id="travel-bot")
u = client.for_entity("user_001", namespace="prod")

res = u.add("Planning a trip to France. Like wine, cheese and calm places.")
print(res.get("context", {}).get("summary"))

hits = u.search("trip recommendations", top_k=24)
print(len(hits.get("matches", [])))
```

Behind the scenes Flumes:
1. Adds the fact to long-term memory (summarised + deduplicated).
2. Retrieves relevant memories with semantic search.
3. Injects them into the LLM prompt (OpenAI by default).
4. Stores the assistant response for future context.

---

## 🛠️ Advanced (thin pass-through)

```python
from flumes import MemoryClient

mc = MemoryClient(api_key="YOUR_KEY")
mc.search("wine", entity_id="user_42")
mc.get_all(entity_id="user_42", limit=50)
```

All endpoints map 1-to-1 with the [REST reference](https://docs.flumes.ai/api-reference/).

---

## ✨ Key features

* **Semantic search** & vector similarity out-of-the-box.
* **Automatic summarisation & deduplication** (`infer=True` by default).
* **Pluggable LLM backend** – OpenAI today, Claude/Mistral coming.
* **Structured logging** – JSON events (`memory.add.request`, `llm.called`) for easy observability.
* **Timeout & retry helpers** – avoid first-call latency issues.

---

## 🗺 Roadmap

* Async transport with `httpx.AsyncClient`
* CLI: `flumes chat`, `flumes memories list`
* Policy plug-ins: custom summariser / retention strategies
* Local in-process transport (`flumes-core`) for offline testing

---

## 🤝 Contributing

1. Fork the repo and create a feature branch.
2. `make install && make test`
3. Open a PR – we ❤️ community help!

Please see [`LICENSE`](LICENSE) (MIT).

---

Made with 🧠 by the Flumes team – join us on the journey to give every agent a memory!