semvec/pypi-packages huggingface dataset:
stocklens-sdk 1.0.1
AI-Powered Stock Sentiment and Trend Analysis SDK
Requires Python: >=3.8


# 📊 StockLens SDK

StockLens SDK is an AI-powered Python toolkit designed for stock sentiment analysis, news summarization, and trend prediction.  
It enables traders, analysts, and developers to extract, analyze, and interpret financial insights from real-time market data using natural language processing (NLP) and machine learning.

---

## 🚀 Features

- 📰 Fetch and analyze stock market news sentiment via NLP (TextBlob)  
- 🧠 Generate summaries using transformer-based models (Hugging Face BART)  
- 🔊 Convert summaries to speech with gTTS for auditory insights  
- 📈 Compute technical indicators like RSI, MACD, and Bollinger Bands  
- ⚙️ Predict short-term stock trends using combined sentiment and technical signals  

---

## 🧩 Installation


pip install stocklens-sdk


---

## 💻 Usage Example


from stocklens_sdk import analyze_sentiment, generate_summary, generate_audio

# Example text
text = "Apple reported strong quarterly earnings with increased iPhone sales."

# Analyze and summarize
summary = generate_summary([{"headline": "Apple Q2 Results", "summary": text}])
audio = generate_audio(summary, "AAPL")

print("Summary:", summary)
print("Audio File:", audio)


---

## 🧠 Modules Overview

| Module | Description |
|---------|--------------|
| news_fetcher.py | Fetches latest stock news via n8n webhook |
| sentiment_utils.py | Performs sentiment scoring using TextBlob |
| summarizer.py | Generates AI-powered natural summaries |
| tts_generator.py | Converts summaries to speech (MP3 format) |
| indicators.py | Computes RSI, MACD, and Bollinger Bands for trend insight |

---

## 🏗 Architecture Overview


Frontend or Marketfeed API Call
          ↓
     Flask Backend
          ↓
     StockLens SDK
          ├── Fetch News
          ├── Analyze Sentiment
          ├── Summarize Text
          ├── Generate Audio
          ↓
  Returns JSON + Audio Summary


---

## 🧾 Acknowledgment

This project, StockLens – AI-Powered Stock Sentiment and Trend Analyzer, was developed with the guidance and support of the Centre of Excellence, Artificial Intelligence and Robotics (AIR).  


---



## 🧑‍💻 Developer Notes

The SDK can be integrated with web platforms like Marketfeed as a modular AI plugin.  
It can process financial data, summarize insights, and output both text and speech results.  
For enterprise-scale usage, batch or async processing can be enabled.

---

## 📜 License

MIT License © 2025 — Developed by Karthik R S

---

## 🌐 Project Links

- PyPI: [https://pypi.org/project/stocklens-sdk/](https://pypi.org/project/stocklens-sdk/)
- GitHub: [https://github.com/karthikrajesh10/stocklens_sdk](https://github.com/karthikrajesh10/stocklens_sdk)
```
atptools 0.2.31
Dataset manipulation tool.
Requires Python: >=3.11
Keywords: records, csv, dataset, measurement, list, json, yaml, pandas

# pkgpy-atp-tools

Simple tools for data manipulation.

## URLs

- [GitHub](https://github.com/atp-things/pkgpy-atp-tools)
- [PyPI](https://pypi.org/project/atptools/)

## Installation

### From PyPI

```bash
pip install atptools
```

```bash
pipenv install atptools
```

## Content

### File io (async)

### Records

### DictDefault

### Csv
tts-text-norm 1.0.0
多语言文本规范化库，支持中文、日语和英语
Requires Python: >=3.8
Keywords: text normalization, nlp, chinese, japanese, english, text processing

# Text Normalizer

## 基本使用

```python
from tts_text_norm import TextNormalizer

# 创建规范化器实例
normalizer = TextNormalizer()

# 中文文本规范化
text_zh = "这是123个测试文本"
result_zh = normalizer.text_normalize(text_zh, language="中文")
print(result_zh)  # 输出：这是一百二十三 test

# 日语文本规范化
text_jp = "これは123のテストです"
result_jp = normalizer.text_normalize(text_jp, language="日语")
print(result_jp)

# 英语文本规范化
text_en = "This is a test with 123 numbers"
result_en = normalizer.text_normalize(text_en, language="英语")
print(result_en)
```
scout-ch-djangocms-who-is-who 0.1.0
This plugin serves to include the data generated by the Who-Is-Who application in a django-cms application
Requires Python: >=3.13

# django CMS Who-Is-Who

**django CMS Who-Is-Who** allows users of a django CMS application to integrate the results of the [Who-Is-Who application](https://github.com/scout-ch/who-is-who) on an arbitrary webpage.

It serves soley as a connecting plugin and manages only the connection information required to load Who-Is-Who pages.

## Installation

- run `pip install scout-ch-djangocms-who-is-who`
- add `djangocms_who_is_who` to your `INSTALLED_APPS`
- run `python manage.py migrate djangocms_who_is_who`
traceAI-openai-agents 0.1.5
OpenTelemetry instrumentation for OpenAI Agents
Requires Python: <3.14,>3.9

# OpenAI Agents OpenTelemetry Integration

## Overview
This integration provides support for using OpenTelemetry with the OpenAI Agents framework. It enables tracing and monitoring of applications built with OpenAI Agents.

## Installation

1. **Install traceAI OpenAI Agents**

```bash
pip install traceAI-openai-agents
```


### Set Environment Variables
Set up your environment variables to authenticate with FutureAGI

```python
import os

os.environ["FI_API_KEY"] = FI_API_KEY
os.environ["FI_SECRET_KEY"] = FI_SECRET_KEY
```

## Quickstart

### Register Tracer Provider
Set up the trace provider to establish the observability pipeline. The trace provider:

```python
from fi_instrumentation import register
from fi_instrumentation.fi_types import ProjectType

trace_provider = register(
    project_type=ProjectType.OBSERVE,
    project_name="openai_agents_app"
)
```

### Configure OpenAI Agents Instrumentation
Instrument the OpenAI Agents client to enable telemetry collection. This step ensures that all interactions with the OpenAI Agents SDK are tracked and monitored.

```python
from traceai_openai_agents import OpenAIAgentsInstrumentor

OpenAIAgentsInstrumentor().instrument(tracer_provider=trace_provider)
```

### Create OpenAI Agents Components
Set up your OpenAI Agents client with built-in observability.

```python
from agents import Agent, Runner

agent = Agent(name="Assistant", instructions="You are a helpful assistant")
result = Runner.run_sync(agent, "Write a haiku about recursion in programming.")

print(result.final_output)

```
django-twined 0.8.17
A django app to manage octue services
Requires Python: >=3.10
Keywords: django, services, octue, twined

[![PyPI version](https://badge.fury.io/py/django-twined.svg)](https://badge.fury.io/py/django-twined)
[![codecov](https://codecov.io/gh/octue/django-twined/branch/master/graph/badge.svg)](https://codecov.io/gh/octue/django-twined)
[![Documentation Status](https://readthedocs.org/projects/django-twined/badge/?version=latest)](https://django-twined.readthedocs.io/en/latest/?badge=latest)
[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://github.com/pre-commit/pre-commit)
[![black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)

# Django Twined

This is a plugin for django, enabling you to orchestrate [twined-based octue services](https://octue.readthedocs.io) services from your own django
server.

Documentation is [here](https://django-twined.readthedocs.io/en/latest/).

This is great for advanced use cases where:

- you have specific security/firewalling requirements, or
- you want to manage your own auth, or
- you have specific/unusual data integration needs, or
- already have a web based data service, and want to expose it in the twined ecosystem.

**Health warning:** to use this plugin to deploy your services, you'll need to handle all your own data
storage/orchestration, devops, server management, security and auth. So for most users we'd recommend getting in touch at
[octue.com](https://www.octue.com/contact) so we can help!
os_utils 0.2.0
os utils to reduce code copy paste
Requires Python: >=3.14
Keywords: dirs, files, logging, path

Utils for python
====================

os utils for python to reduce copy-pasting same functionality and unity functions to handle common problems with delete of file or directory or creating directory
- logging configuration
- path operations (delete, creating)
- files operation (like md5 calculation or zipping a folder)

## Installation

This project uses [uv](https://docs.astral.sh/uv/) as the package manager.

### Install uv

```bash
# On macOS and Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# On Windows
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"
```

### Install the package

```bash
# Install from PyPI
uv pip install os_utils

# Or install in development mode
uv pip install -e .
```

## Development

```bash
# Sync dependencies
uv sync

# Run Python with uv
uv run python your_script.py
```
spec-cli 0.1.6
AI 规则仓库自动配置工具，用于下载和配置 airules 的规则文件
Requires Python: >=3.8

## spec-cli

AI 规则仓库自动配置工具，用于下载和配置 airules 的规则文件

## 🚀 快速上手

### 前置条件

请确保你的环境满足以下条件：

* ✅  **Python** ：版本 >=3.8
* ✅  **AI 编程工具** ：Cursor 或 ClaudeCode

### 一键安装

安装spec-cli

```bash
# 通过 pip 安装
pip install spec-cli

# 或者通过uv 安装
uv tool install spec-cli
```

接着在你的项目根目录打开终端，执行以下命令，即可一键安装 airules 规则文件：

```bash
# 使用指定仓库中的规则文件
spec init --template-repository http://git.xxxx/xxx/xxx.git
```

安装过程日志示例：

```
==========================================
  AI 规则仓库自动配置脚本
==========================================

[INFO] 目标安装目录: D:\Users\xxx\workspace\xxx\xxx\spec-dev\xxx
[INFO] 模板仓库地址: http://git.xxx.com/xxxx/airules.git

[INFO] 检查前置条件...
[INFO] 前置条件检查通过

请选择您使用的 AI 客户端工具：
1) Cursor
2) ClaudeCode

请输入选项 [1-2]: 1
[INFO] 您选择了: Cursor
[INFO] 开始下载规则仓库: http://git.xxx/xxx/xxxx.git
[WARNING] 临时目录已存在，正在清理...
[INFO] 仓库下载成功
[INFO] 目标配置目录: D:\Users\xxx\workspace\xxx\xxx\spec-dev\xxx\.cursor
[INFO] 正在拷贝 rules 文件夹...
[INFO] 正在拷贝 commands 文件夹...
[INFO] 文件拷贝完成！

[INFO] 配置文件已安装到: D:\Users\xxx\workspace\xxx\xxx\spec-dev\xxx\.cursor
[INFO]   - rules 目录: D:\Users\xxx\workspace\xxx\xxx\spec-dev\xxx\.cursor\rules
[INFO]   - commands 目录: D:\Users\xxx\workspace\xxx\xxx\spec-dev\xxx\commands
[INFO] 清理临时文件...

==========================================
[INFO] 配置完成！请重启您的 AI 工具以使配置生效
==========================================
```

### 安装结果

安装完成后，项目目录结构如下：

```
your-project/
└── .cursor/           # Cursor 配置目录
    ├── rules/         # 规则文件（上下文库）
    │   ├── common/          # 通用规范
    │   ├── languages/       # 编程语言
    │   ├── frameworks/      # 技术框架
    │   ├── project/         # 项目知识（需生成）
    │   └── workflow/        # 工作流定义
    └── commands/      # 斜杠命令
        ├── req.md           # /req 命令
        ├── tech.md          # /tech 命令
        ├── coding.md        # /coding 命令
        └── dev.md           # /dev 命令

```

### 验证安装

打开 Cursor，在对话框中输入 /，如果看到 /req、/tech、/coding、/dev 等命令，说明安装成功！