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| title: Who killed Laura Palmer? | |
| emoji: 🗻🗻 | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: streamlit | |
| sdk_version: 1.2.0 | |
| app_file: app.py | |
| pinned: false | |
| license: apache-2.0 | |
| # Who killed Laura Palmer? [](https://huggingface.co/spaces/anakin87/who-killed-laura-palmer) [](https://github.com/anakin87/who-killed-laura-palmer) | |
| [<img src="./data/readme_images/spaces_logo.png" align="center" style="display: block;margin-left: auto; | |
| margin-right: auto; max-width: 70%;}">](https://huggingface.co/spaces/anakin87/who-killed-laura-palmer) | |
| ## 🗻🗻 Twin Peaks Question Answering system | |
| WKLP is a simple Question Answering system, based on data crawled from [Twin Peaks Wiki](https://twinpeaks.fandom.com/wiki/Twin_Peaks_Wiki). It is built using [🔍 Haystack](https://github.com/deepset-ai/haystack), an awesome open-source framework for building search systems that work intelligently over large document collections. | |
| - [Project architecture 🧱](#project-architecture-) | |
| - [What can I learn from this project? 📚](#what-can-i-learn-from-this-project-) | |
| - [Repository structure 📁](#repository-structure-) | |
| - [Installation 💻](#installation-) | |
| - [Possible improvements ✨](#possible-improvements-) | |
| --- | |
| ## Project architecture 🧱 | |
| [](#) | |
| * Crawler: implemented using [Scrapy](https://github.com/scrapy/scrapy) and [fandom-py](https://github.com/NikolajDanger/fandom-py) | |
| * Question Answering pipelines: created with [Haystack](https://github.com/deepset-ai/haystack) | |
| * Web app: developed with [Streamlit](https://github.com/streamlit/streamlit) | |
| * Free hosting: [Hugging Face Spaces](https://huggingface.co/spaces) | |
| --- | |
| ## What can I learn from this project? 📚 | |
| - How to quickly ⌚ build a modern Question Answering system using [🔍 Haystack](https://github.com/deepset-ai/haystack) | |
| - How to generate questions based on your documents | |
| - How to build a nice [Streamlit](https://github.com/streamlit/streamlit) web app to show your QA system | |
| - How to optimize the web app to 🚀 deploy in [🤗 Spaces](https://huggingface.co/spaces) | |
| [](https://huggingface.co/spaces/anakin87/who-killed-laura-palmer) | |
| ## Repository structure 📁 | |
| - [app.py](./app.py): Streamlit web app | |
| - [app_utils folder](./app_utils/): python modules used in the web app | |
| - [crawler folder](./crawler/): Twin Peaks crawler, developed with Scrapy and fandom-py | |
| - [notebooks folder](./notebooks/): Jupyter/Colab notebooks to create the Search pipeline and generate questions (using Haystack) | |
| - [data folder](./data/): all necessary data | |
| - [presentations](./presentations/): Video presentation and slides (PyCon Italy 2022) | |
| Within each folder, you can find more in-depth explanations. | |
| ## Installation 💻 | |
| To install this project locally, follow these steps: | |
| - `git clone https://github.com/anakin87/who-killed-laura-palmer` | |
| - `cd who-killed-laura-palmer` | |
| - `pip install -r requirements.txt` | |
| To run the web app, simply type: `streamlit run app.py` | |
| ## Possible improvements ✨ | |
| ### Project structure | |
| - The project is optimized to be deployed in Hugging Face Spaces and consists of an all-in-one Streamlit web app. In more structured production environments, I suggest dividing the software into three parts: | |
| - Haystack backend API (as explained in [the official documentation](https://haystack.deepset.ai/components/rest-api)) | |
| - Document store service | |
| - Streamlit web app | |
| ### Reader | |
| - The reader model (`deepset/roberta-base-squad2`) is a good compromise between speed and accuracy, running on CPU. There are certainly better (and more computationally expensive) models, as you can read in the [Haystack documentation](https://haystack.deepset.ai/pipeline_nodes/reader). | |
| - You can also think about preparing a Twin Peaks QA dataset and fine-tuning the reader model to get better accuracy, as explained in this [Haystack tutorial](https://haystack.deepset.ai/tutorials/fine-tuning-a-model). | |