semvec/pypi-packages huggingface dataset:
semantic-search-faiss-nandhu1595 0.0.2
Semantic search to query covid related papers
License: MIT
Keywords: natural language processing,semantic search,pytorch

# Semantic search with FAISS
[![PyPI version shields.io](https://img.shields.io/badge/pypi-0.0.2-blue)](https://pypi.org/project/cassava-classifier/)  [![Downloads](https://pepy.tech/badge/cassava-classifier)](https://pepy.tech/project/cassava-classifier)


The idea of this project is to build a semantic search engine which can search across multiple research papers related to covid and return the response. This can pretty much help many ppl who want to know about ongoing research wrt covid

We have used - `retrieval-ranking method with faiss index` for retrieving data for the query.


## Web app
[![Open Web App in Streamlit](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://share.streamlit.io/p-s-vishnu/cassava_app/main/web/app.py)
<img src="static/cassava.gif" alt="inference" style="width:80%;" />


## Swagger documentation for API
[![API Link](https://img.shields.io/badge/Launch%20Cassava%20API-Swagger-blue?style=for-the-badge&logo=microsoft%20azure)](http://52.224.254.7:8003/docs)
<img src="static/api.gif" alt="inference" style="width:80%;" />


## Installation

`pip install cassava-classifier`


## Inference example

```python
import PIL import Image
from cassava.pretrained import get_model

image = Image.open("<insert your image path here>")

# Use cassava.list_models() to list of available trained models
model = get_model(name:str)
model.predict_as_json(image: np.array)
>> {"class_name":str, "confidence": np.float}

```
Try out the inference code either on google colab or kaggle.

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1gPLY6nqF6P4WdvIRIAH_aYQn-iWkzvqs?usp=sharing) [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://www.kaggle.com/vpkprasanna/cassava-inference-from-pypi)

## Other details
- Training data can be found on the [Kaggle competition page](https://www.kaggle.com/c/cassava-leaf-disease-classification)

- Streamlit app code can be found [here](https://github.com/p-s-vishnu/cassava_app).

[Github discussion forum](https://github.com/p-s-vishnu/cassava-leaf-disease-classification/discussins)


## Kaggle

[https://www.kaggle.com/nandhuelan/semantic-search]



## Acknowledgements

We would like to thank Kaggle community as a whole for providing an avenue to learn and discuss latest data science/machine learning advancements but a hat tip to whose code was used / who inspired us.

1. Vladimir Iglovikov for his wonderful article ["I trained a model. What is next?"](https://ternaus.blog/tutorial/2020/08/28/Trained-model-what-is-next.html)

2. [Xhululu](https://www.kaggle.com/xhlulu/cord-19-eda-parse-json-and-generate-clean-csv) for the dataset.