| Name | fendi JSON |
| Version |
0.0.0.13
JSON |
| download |
| home_page | |
| Summary | An auto py builder for ChatBots on top of streamlit app's - LLaMa's powered APS |
| upload_time | 2023-12-11 12:17:43 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.9 |
| license | Copyright (c) 2016 The Python Packaging Authority (PyPA) Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
setuptools
development
genai
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Fendi project
## Overview
Fendi is a powerful Python package designed to simplify the creation of a
Streamlit-powered chatbot infused with AI capabilities. This chatbot extracts valuable
information from user CVs and LinkedIn profiles, offering a personalized and interactive
experience.
## Features
- **AI-Powered Chatbot:** Leverage advanced natural language processing (NLP) algorithms to create an intelligent and responsive chatbot.
- **CV Integration:** Extract relevant information from user CVs to enhance the chatbot's understanding and responses.
- **⚠️LinkedIn Data Integration:** Utilize data from LinkedIn profiles to personalize the chatbot's interactions.[Only linkedin PDF resume for now ]
- **Streamlit App:** Integrated with Streamlit, allowing for easy deployment and a user-friendly interface.
- **Customizable:** Easily tailor the chatbot behavior and appearance to suit specific requirements.
## Installation
Create you virtual environment using the following command (make sure that the virtual env is using py 3.10.13)
```bash
conda create -n myenv_39 python==3.10.13
```
Activate myenv_39:
```bash
conda activate myenv_39
```
Install the package using the following command:
```bash
pip install fendi
```
## Desclaimer
This project does not aim to cover best practices for Python project
development as a whole. For example, it does not provide guidance or tool
recommendations for version control, documentation, or testing.
Feel free to contact me directly, so that I can give you a walk through.
## Getting Started
**TL;DR**
> [Colab Tutorial]
> Just take a glimpse of this quick tutorial on Google Colab.
> [](https://colab.research.google.com/drive/18Bw5EBs94M8c-iQob-WIS-KU8uyA9iJz#scrollTo=hqOdwn1sJZSx)
On
> Make sure that you virtual environment is activated.
1. Import Fendi in Your Python Script:
```py
from fendi import fedi
```
2. Define User Information:
```py
info = {
"Pronoun": "his",
"Subject": "he"}
```
Feel free to adapt this to your specific needs, adding or removing fields as required.
⚠️ The top required ones are :
``Pronoun``, ``Subject``, ``Name``, ``Full Name``, ``Intro and About`` .
3. Specify CV Path:
```py
cv_path = r"./Profile.pdf"
```
Again the resume file should be downloaded from the Linkedin, see next section for more details.
4. Create Streamlit App:
> Invoke the create_app function provided by Fendi:
```python
fedi.create_app(info, cv_path)
```
5. Run Your Streamlit App:
> Execute your Streamlit app script to launch the chatbot interface:
```python
streamlit run your_script.py
```
> ⚠️ ***Replace your_script.py with the name of your Python script.*** ️⚠️
6. Interact with the Chatbot:
Open your web browser and navigate to the provided Streamlit URL: http://localhost:8501/. Interact with the chatbot by
asking questions or providing input based on the user information.
7. Explore Example Usage:
Check out the `example.py` file
### Example Script
```python [exmple.py]
from fendi import fedi
def main():
info = {
# ... (your personal information)
}
cv_path = r"./Profile.pdf"
fedi.create_app(info, cv_path)
if __name__ == '__main__':
main()
```
:warning: you should create a directory where you will put your script. It shall be this way:
```commandline
Directory Structure:
.
├── example.py
│ (Description: Your main Python script or example file.)
└── Profile.pdf
(Description: The Resume file you downloaded from LinkedIn.)
```
## Contributions
Contributions to Fendi are welcome!
Feel free to open issues or submit pull requests on the [GitHub repository][src].
[The source for this project is available here][src].
----
This is the README file for the project.
The PDF file should use UTF-8 encoding and can be downloaded from you Linkedin profile.
<img src="https://resumeworded.com/linkedin-review/img/sample.gif" alt="Your GIF" width="300"/>
[reStructuredText][rst] or [markdown][md use] with the appropriate [key set][md
use].
## What's New
Nothing for now. But I won't be supporting this package for long.
I use :
[reStructuredText][rst] or [markdown][md use] with the appropriate [key set][md
use].
[Portfolio]: https://fedihamdi.netlify.app/
[src]: https://github.com/fedihamdi
[rst]: http://docutils.sourceforge.net/rst.html
[md]: https://tools.ietf.org/html/rfc7764#section-3.5 "CommonMark variant"
[md use]: https://packaging.python.org/specifications/core-metadata/#description-content-type-optional
[Colab Tutorial]: https://colab.research.google.com/drive/18Bw5EBs94M8c-iQob-WIS-KU8uyA9iJz#scrollTo=hqOdwn1sJZSx
Raw data
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"description": "# Fendi project\r\n\r\n\r\n\r\n\r\n## Overview\r\n\r\nFendi is a powerful Python package designed to simplify the creation of a\r\nStreamlit-powered chatbot infused with AI capabilities. This chatbot extracts valuable\r\ninformation from user CVs and LinkedIn profiles, offering a personalized and interactive\r\nexperience.\r\n\r\n## Features\r\n\r\n- **AI-Powered Chatbot:** Leverage advanced natural language processing (NLP) algorithms to create an intelligent and responsive chatbot.\r\n- **CV Integration:** Extract relevant information from user CVs to enhance the chatbot's understanding and responses.\r\n- **\u26a0\ufe0fLinkedIn Data Integration:** Utilize data from LinkedIn profiles to personalize the chatbot's interactions.[Only linkedin PDF resume for now ]\r\n- **Streamlit App:** Integrated with Streamlit, allowing for easy deployment and a user-friendly interface.\r\n- **Customizable:** Easily tailor the chatbot behavior and appearance to suit specific requirements.\r\n\r\n## Installation\r\n\r\nCreate you virtual environment using the following command (make sure that the virtual env is using py 3.10.13)\r\n```bash\r\nconda create -n myenv_39 python==3.10.13\r\n```\r\nActivate myenv_39:\r\n```bash\r\nconda activate myenv_39\r\n```\r\nInstall the package using the following command:\r\n```bash\r\npip install fendi\r\n```\r\n\r\n## Desclaimer\r\nThis project does not aim to cover best practices for Python project\r\ndevelopment as a whole. For example, it does not provide guidance or tool\r\nrecommendations for version control, documentation, or testing.\r\nFeel free to contact me directly, so that I can give you a walk through.\r\n\r\n## Getting Started\r\n**TL;DR**\r\n> [Colab Tutorial]\r\n> Just take a glimpse of this quick tutorial on Google Colab.\r\n> [](https://colab.research.google.com/drive/18Bw5EBs94M8c-iQob-WIS-KU8uyA9iJz#scrollTo=hqOdwn1sJZSx)\r\n\r\nOn\r\n> Make sure that you virtual environment is activated.\r\n1. Import Fendi in Your Python Script:\r\n ```py\r\nfrom fendi import fedi\r\n```\r\n2. Define User Information:\r\n ```py\r\ninfo = {\r\n \"Pronoun\": \"his\",\r\n \"Subject\": \"he\"}\r\n```\r\nFeel free to adapt this to your specific needs, adding or removing fields as required.\r\n\r\n\u26a0\ufe0f The top required ones are :\r\n``Pronoun``, ``Subject``, ``Name``, ``Full Name``, ``Intro and About`` .\r\n\r\n3. Specify CV Path:\r\n```py\r\n cv_path = r\"./Profile.pdf\"\r\n```\r\nAgain the resume file should be downloaded from the Linkedin, see next section for more details.\r\n\r\n4. Create Streamlit App:\r\n> Invoke the create_app function provided by Fendi:\r\n```python\r\n fedi.create_app(info, cv_path)\r\n```\r\n5. Run Your Streamlit App:\r\n> Execute your Streamlit app script to launch the chatbot interface:\r\n```python\r\nstreamlit run your_script.py\r\n```\r\n> \u26a0\ufe0f ***Replace your_script.py with the name of your Python script.*** \ufe0f\u26a0\ufe0f\r\n6. Interact with the Chatbot:\r\nOpen your web browser and navigate to the provided Streamlit URL: http://localhost:8501/. Interact with the chatbot by\r\nasking questions or providing input based on the user information.\r\n7. Explore Example Usage:\r\nCheck out the `example.py` file\r\n### Example Script\r\n```python [exmple.py]\r\nfrom fendi import fedi\r\n\r\ndef main():\r\n info = {\r\n # ... (your personal information)\r\n }\r\n cv_path = r\"./Profile.pdf\"\r\n fedi.create_app(info, cv_path)\r\n\r\nif __name__ == '__main__':\r\n main()\r\n```\r\n\r\n:warning: you should create a directory where you will put your script. It shall be this way:\r\n```commandline\r\nDirectory Structure:\r\n.\r\n\u251c\u2500\u2500 example.py\r\n\u2502 (Description: Your main Python script or example file.)\r\n\u2514\u2500\u2500 Profile.pdf\r\n (Description: The Resume file you downloaded from LinkedIn.)\r\n```\r\n\r\n## Contributions\r\nContributions to Fendi are welcome!\r\nFeel free to open issues or submit pull requests on the [GitHub repository][src].\r\n\r\n[The source for this project is available here][src].\r\n\r\n----\r\n\r\nThis is the README file for the project.\r\n\r\nThe PDF file should use UTF-8 encoding and can be downloaded from you Linkedin profile.\r\n\r\n<img src=\"https://resumeworded.com/linkedin-review/img/sample.gif\" alt=\"Your GIF\" width=\"300\"/>\r\n\r\n[reStructuredText][rst] or [markdown][md use] with the appropriate [key set][md\r\nuse].\r\n\r\n## What's New\r\nNothing for now. But I won't be supporting this package for long.\r\nI use :\r\n[reStructuredText][rst] or [markdown][md use] with the appropriate [key set][md\r\nuse].\r\n\r\n\r\n[Portfolio]: https://fedihamdi.netlify.app/\r\n[src]: https://github.com/fedihamdi\r\n[rst]: http://docutils.sourceforge.net/rst.html\r\n[md]: https://tools.ietf.org/html/rfc7764#section-3.5 \"CommonMark variant\"\r\n[md use]: https://packaging.python.org/specifications/core-metadata/#description-content-type-optional\r\n[Colab Tutorial]: https://colab.research.google.com/drive/18Bw5EBs94M8c-iQob-WIS-KU8uyA9iJz#scrollTo=hqOdwn1sJZSx\r\n",
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