semvec/pypi-packages huggingface dataset:
datamorph-airflow 0.0.98
DataMorph's application to dynamically build Airflow DAGs from JSON files 
License: MIT
Requires Python: >=3.6.0
Keywords: airflow

DataMorph's application to dynamically build Airflow DAGs from JSON files 


## [0.0.89] -
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### Changed
### Fixed
- variable name change- datamorph and glue action to include load parameters from a s3 location


## [0.0.87] -
### Added
### Changed
### Fixed
- Indentation issue and import issue

## [0.0.86] -
### Added
### Changed
-  exclude dm_prop_frontend and workflow_id
### Fixed

## [0.0.85] -
### Added
### Changed
-  exclude dm_prop_frontend and workflow_id
### Fixed

## [0.0.84] -
### Added
- loadparams action introduced and updated s3resources to read json file
### Changed
### Fixed
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## [0.0.83] -
### Added
- loadparams action introduced and updated s3resources to read json file
### Changed
### Fixed
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## [0.0.81] - 2025-01-31
### Added
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### Changed
- Try Catch block added Email functionality
### Fixed
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## [0.0.80] - 2025-01-31
### Added
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### Changed
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### Fixed
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## [0.0.79] - 2025-01-31
### Added
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### Fixed
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pebble-lang 2.2.0
Pebble programming language interpreter in Python
License: MIT
Requires Python: >=3.8

# Pebble Programming Language

Pebble is a beginner-friendly programming language built in Python.  
It’s designed to be simple, readable, and fun for learners and hobbyists.

---

## Features

- **Functions:** Define with `fnc` and return values with `out`
- **Loops:** `go` (for loops) and `until` (while loops)
- **Conditions:** `if`, with comparison keywords `big`, `sml`, `eql`, and boolean operators `and`, `or`, `not`
- **Collections:** Lists `{}` and dictionaries `[]` with easy syntax
- **Input:** `inp[...]` to get user input
- **Comments:** Use `!` to ignore text on a line
- **Math:** `+`, `-`, `*`, `/`, `//`, `%`, `^`
- **Easy to run:** Execute `.pb` files with a single command

---

## Installation

```bash
pip install pebble-lang
````

---

## Usage

### Running a Pebble program

```bash
pebble examples/hello.pb
```

### Example Pebble program (`hello.pb`)

```pebble
say "Hello Pebble!"

x is 2 ^ 3
say x
! Output: 8

nums is {1, 2, 3}
count is 0
go n in nums:
    count is count + 1
say count
! Output: 3

fnc greet(name):
    say "Hello " + name

greet("Rasa")
```

---

## Loops and Conditions

```pebble
nums is {0, 1, 2}
go i in nums:
    say "Hello"

x is 10
if x big 5:
    say "x is bigger than 5"
```

---

## Collections

```pebble
numbers is {10, 20, 30}
say numbers

person is [name: "Rasa", age: 14]
say person

go n in numbers:
    say n * 2

say person["name"]
say person["age"]
```

This will output:

```
[10, 20, 30]
{'name': 'Rasa', 'age': 14}
20
40
60
Rasa
14
```


---

## Input

```pebble
name is inp["Enter your name: "]
say "Hello " + name
```

---

## License

MIT License
See [LICENSE](LICENSE) for details.

---

## Contributing

Contributions are welcome! Please open issues or pull requests on the [GitHub repository](https://github.com/yourusername/pebble-lang).

---

## Contact

Created by Rasa8877
GitHub: [https://github.com/Rasa8877/pebble-lang](https://github.com/Rasa8877/pebble-lang)
momentoai 0.1.2
Python SDK for Momento AI — image vectorization, face search, and CLIP embeddings with user-supplied Supabase credentials.
License: MIT
Requires Python: >=3.8

# 🧠 MomentoAI — Python SDK

MomentoAI is a lightweight Python SDK that connects to the hosted MomentoAI FastAPI backend and uses **your own Supabase project** to store images and embeddings.
 
## Official PyPi published library: https://pypi.org/project/momentoai/

---

## 📦 Installation

Install MomentoAI via PyPI:

```bash
pip install momentoai
```

---

# ⚙️ Initialize the Client

```python
from MomentoAI import MomentoAIClient

client = MomentoAIClient(
    api_key="public-access",   # default key for open access
    api_url="https://momento-ai-1-42230574747.asia-south1.run.app",

    # — your own Supabase credentials —
    supabase_url="https://yourproject.supabase.co",
    supabase_service_key="YOUR_SUPABASE_SERVICE_KEY",
    supabase_bucket="YOUR_BUCKET_NAME"
)
```

---

# 🩺 Check Backend Status

Use `.health()` to verify the backend is live:

```python
print(client.health())
```

**Expected output:**

```json
{
  "models_loaded": true
}
```

---

# 📸 Core SDK Functions

Below are the main operations supported by the MomentoAI SDK.

---

## 1️⃣ `vectorize_image()`

Uploads an image → extracts embeddings → stores them in your Supabase project.

```python
response = client.vectorize_image(
    "me.jpg",
    event_id="event1",
    business_id="business1"
)

print(response)
```

---

## 2️⃣ `find_face()`

Finds similar faces in your stored Supabase embeddings.

```python
matches = client.find_face(
    "person.jpg",
    event_id="event1",
    business_id="business1"
)

print(matches)
```

---

## 3️⃣ `search_images()`

Text → Image retrieval using CLIP embeddings.

```python
results = client.search_images(
    "a man smiling outdoors",
    event_id="event1",
    business_id="business1"
)

print(results)
```

---

## 4️⃣ `list_embeddings()`

Retrieve all embeddings for a specific event + business.

```python
records = client.list_embeddings(
    event_id="event1",
    business_id="business1"
)

print(records)
```

---

## 5️⃣ `delete_embedding()`

Delete a specific embedding record from Supabase.

```python
client.delete_embedding("some-embedding-id")
```

---

# 🪪 License

MIT License  
© 2025 — Manil Modi