https://github.com/luca683/telesummary
Telegram Bot that summarizes messages in a telegram group.
https://github.com/luca683/telesummary
docker elasticsearch fluentd huggingface-transformers kafka kibana machine-learning spark telegram telegram-bot
Last synced: 4 months ago
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Telegram Bot that summarizes messages in a telegram group.
- Host: GitHub
- URL: https://github.com/luca683/telesummary
- Owner: Luca683
- Created: 2024-07-26T07:49:13.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-08-30T09:14:24.000Z (almost 2 years ago)
- Last Synced: 2025-02-01T22:01:45.215Z (over 1 year ago)
- Topics: docker, elasticsearch, fluentd, huggingface-transformers, kafka, kibana, machine-learning, spark, telegram, telegram-bot
- Language: Jupyter Notebook
- Homepage:
- Size: 1.59 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# TeleSummary
TeleSummary is a telegram bot that summarizes all the messages that are sent by various users in a Telegram group.
## Application goals
Especially in the workplace, Telegram has become one of the most used messaging systems among teams, so a system like TeleSummary could help large organizations with distributed work teams monitor internal communications and make decisions about them more quickly.
## Technologies
- Data source: **[python-telegram-bot](https://python-telegram-bot.org/)**.
- Data ingestion: **[Fluentd](https://www.fluentd.org/)**.
- Data streaming: **[Kafka](https://kafka.apache.org/)**.
- Centralized Service: **[Zookeeper](https://zookeeper.apache.org/)**.
- Data Processing: **[Apache Spark](https://spark.apache.org/)**.
- Data Indexing: **[Elasticsearch](https://www.elastic.co/elasticsearch)**.
- Data Visualization: **[Kibana](https://www.elastic.co/kibana)**.
## Project pipeline
## Project setup
### Requirements
- [Docker](https://www.docker.com/)
- Docker Compose
- Telegram Bot: place the bot token inside a .env file in the root directory:
```
MY_TOKEN = insertyourtoken
```
### Downloads
- Kafka: download from this [link](https://downloads.apache.org/kafka/3.6.2/kafka_2.13-3.6.2.tgz) and put it in kafka/setup directory.
- Spark: download from this [link](https://dlcdn.apache.org/spark/spark-3.4.3/spark-3.4.3-bin-hadoop3.tgz) and put it in spark/setup directory.
### Application launch
1. Create container virtual network:
```
$ docker network create --subnet=10.0.100.0/24 tap
```
2. Launch app:
```
$ docker compose up
```
### Results
Here is services IP table:
| Container | URL | Description |
| ------------- | ------------------------------------------ | ----------------------------------------------- |
| Kafka UI | http://localhost:8080 | Open kafka UI to monitor Kafka Topics |
| Elasticsearch | http://localhost:9200 | Open Elasticsearch to manage indexes |
| Kibana | http://localhost:5601 | Open Kibana to view data and create a dashboard |
These are my visualizations:
To import the same:
- Open [Kibana](localhost:5601).
- Go to "Stack Management" and after go to "Saved Objects".
- Import from file.
- Choose kibana/Dashboard_TAP.ndjson
# Author
TeleSummary has been developed by Luca Strano, Computer Science student at Department of Mathematics and Computer Science, University of Catania, for the "Technologies for Advanced Programming" course.
Email: stranoluca469955@gmail.com