Autocomplete implementation

README.md

@@ -1 +1,104 @@
-# PDataViewer
+# PDataViewer
+
+PDataViewer is a web application that lets you explore the PD data landscape and identify cohort datasets that suit your research needs.
+
+- [PDataViewer](#pdataviewer)
+  - [Introduction](#introduction)
+  - [Requirements](#requirements)
+  - [Installation](#installation)
+    - [Clone the Repository](#clone-the-repository)
+    - [Install the Backend Requirements](#install-the-backend-requirements)
+    - [Install the Frontend Requirements](#install-the-frontend-requirements)
+  - [Usage](#usage)
+    - [Starting the Backend Locally](#starting-the-backend-locally)
+    - [Run the Backend via Docker](#run-the-backend-via-docker)
+    - [Starting the Frontend Locally](#starting-the-frontend-locally)
+    - [Run the Frontend via Docker](#run-the-frontend-via-docker)
+
+
+## Introduction
+Data collected in cohort studies lay the groundwork for a plethora of Parkinson’s disease (PD) research endeavors. PDataViewer lets you explore this PD data landscape and identify cohort datasets that suit your research needs. We accessed and curated major PD cohort datasets in a purely data-driven manner with the aim of:
+
+1) characterizing their underlying data
+2) assessing the quantity and availability of data
+3) evaluating the interoperability across these distinct cohort datasets. 
+
+## Requirements
+- Python >= 3.10
+- [Angular = 17.1.0](https://angular.io/guide/setup-local)
+- [Node.js (LTS) >= 18.13](https://nodejs.org/en/download/package-manager)
+- TypeScript >= 5.2.0 < 5.4.0
+
+## Installation
+### Clone the Repository
+
+```bash
+git clone https://github.com/SCAI-BIO/PDataViewer
+cd PDataViewer
+```
+
+### Install the Backend Requirements
+
+```bash
+cd backend
+pip install -r requirements.txt
+```
+
+### Install the Frontend Requirements
+
+```bash
+curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.7/install.sh | bash
+nvm install 20
+npm install -g @angular/cli
+```
+
+## Usage
+
+### Starting the Backend Locally
+You can access the backend functionalities by accessing the provided REST API. <br>
+Run the Backend API on port 5000:
+
+```python
+cd backend
+uvicorn api.routes:app --reload -port 5000
+```
+
+### Run the Backend via Docker
+The API can also be run via docker. <br>
+You can either build the docker container locally or download the latest build from the PDataViewer GitHub package registry.
+
+```bash
+docker build . -t ghcr.io/scai-bio/pdataviewer/backend:latest
+docker pull ghcr.io/scai-bio/pdataviewer/backend:latest
+```
+
+After build/download you will be able to start the container and access the PDataViewer API per default on [localhost:8000](http://localhost:8000/):
+
+```bash
+docker run -p 8000:80 ghcr.io/pdataviewer/scai-bio/backend:latest
+```
+
+### Starting the Frontend Locally
+You can deploy a local version of the web application via Angular <br>
+You can access the web application on [localhost:4200](http://localhost:4200):
+
+```bash
+cd frontend
+npm install
+ng serve
+```
+
+### Run the Frontend via Docker
+You can deploy a local version of the web application via docker. <br>
+You can either build the docker container locally or download the latest build from the PDataViewer GitHub package registry.
+
+```bash
+docker build . -t ghcr.io/scai-bio/pdataviewer/frontend:latest
+docker pull ghcr.io/scai-bio/pdataviewer/frontend:latest
+```
+
+After build/download you will be able to start the container and access the PDataViewer web application per default on [localhost:8080](http://localhost:8080/):
+
+```bash
+docker run -p 8080:80 ghcr.io/pdataviewer/scai-bio/frontend:latest
+```

backend/README.md

@@ -1,13 +1,49 @@
+# Backend
+
+- [Backend](#backend)
+  - [Tutorial](#tutorial)
+  - [Requirements](#requirements)
+  - [Installation](#installation)
+  - [Usage](#usage)
+    - [Starting the Backend Locally](#starting-the-backend-locally)
+    - [Run the Backend via Docker](#run-the-backend-via-docker)
+
+
+## Tutorial
+
 https://fastapi.tiangolo.com/tutorial/
 
+## Requirements
+
+- Python >= 3.10
+
 ## Installation
 
-```bash
-pip install fastapi
+```python
+pip install -r requirements.txt
 ```
 
 ## Usage
 
+### Starting the Backend Locally
+You can access the backend functionalities by accessing the provided REST API. <br>
+Run the Backend API on port 5000:
+
 ```python
-uvicorn app.routes:app --reload
+uvicorn api.routes:app --reload -port 5000
+```
+
+### Run the Backend via Docker
+The API can also be run via docker. <br>
+You can either build the docker container locally or download the latest build from the PDataViewer GitHub package registry.
+
+```bash
+docker build . -t ghcr.io/scai-bio/pdataviewer/backend:latest
+docker pull ghcr.io/scai-bio/pdataviewer/backend:latest
+```
+
+After build/download you will be able to start the container and access the PDataViewer API per default on [localhost:8000](http://localhost:8000/):
+
+```bash
+docker run -p 8000:80 ghcr.io/pdataviewer/scai-bio/backend:latest
 ```

backend/api/routes.py

@@ -9,7 +9,9 @@
 
 import pandas as pd
 
-from fastapi import FastAPI
+from thefuzz import process, fuzz
+
+from fastapi import FastAPI, HTTPException
 from fastapi.responses import RedirectResponse
 
 from starlette.middleware.cors import CORSMiddleware
@@ -60,47 +62,87 @@ def swagger_redirect():
 
 @app.get("/version", tags=["info"])
 def get_current_version():
+    """
+    Get the version of the API.
+    """
     return app.version
 
 
 @app.get("/cdm", tags=["info"])
 def get_cdm():
+    """
+    Get PASSIONATE CDM.
+    """
     cdm = merge_modalities()
     return cdm.to_dict()
 
 
 @app.get("/cdm/cohorts", tags=["info"])
 def get_cohorts():
+    """
+    Get all cohorts available in PASSIONATE.
+    """
     cdm = merge_modalities()
     cdm = clean_extra_columns(cdm)
     return {idx: cohort for idx, cohort in enumerate(cdm.columns)}
 
 
 @app.get("/cdm/features", tags=["info"])
 def get_features():
+    """
+    Get all features available in PASSIONATE.
+    """
     features = merge_modalities(usecols=["Feature"])
     return features.to_dict()
 
 
 @app.get("/cdm/modalities", tags=["info"])
 def get_modalities():
+    """
+    Get all modalities available in PASSIONATE.
+    """
     files = [file.replace(".csv", "") for file in os.listdir("./cdm") if file.endswith(".csv")]
     return {idx: file for idx, file in enumerate(files)}
 
 
 @app.get("/cdm/modalities/{modality}", tags=["search"])
 def get_modality(modality: str):
-    mappings = pd.read_csv(f"{'./cdm'}/{modality}.csv", keep_default_na=False)
+    """
+    Get all features of a modality.
+    """
+    if not os.path.exists(f"./cdm/{modality}.csv"):
+        raise HTTPException(status_code=404, detail="Modality not found")
+    mappings = pd.read_csv(f"./cdm/{modality}.csv", keep_default_na=False)
     return mappings.to_dict()
 
 
 @app.post("/visualization/chords/{modality}", tags=["visualization"])
 def get_chords(modality: str, cohorts: list[str]):
+    """
+    Generates links between mappings to visualize with chord diagram.
+    """
+    if not os.path.exists(f"./cdm/{modality}.csv"):
+        raise HTTPException(status_code=404, detail="Modality not found")
     chords, decoder = generate_chords(modality, cohorts)
     return chords, decoder
 
 
 @app.post("/studypicker/rank", tags=["studypicker"])
 def get_ranked_cohorts(features: list[str]):
+    """
+    Ranks cohorts based on the availability of given features.
+    """
     ranked_cohorts = rank_cohorts(features)
     return ranked_cohorts.to_dict()
+
+
+@app.get("/autocompletion", tags=["autocompletion"])
+def autocomplete(text: str):
+    """
+    Autocomplete user's query.
+    """
+    features = merge_modalities(usecols=["Feature"])
+    features = features["Feature"].to_list()
+    threshold = 50
+    suggestions = process.extract(text, features, scorer=fuzz.partial_token_set_ratio, limit=10)
+    return [suggestions[0] for suggestions in suggestions if suggestions[1] >= threshold]

backend/preprocessing/studypicker.py

@@ -54,9 +54,11 @@ def rank_cohorts(features: list[str], folder: str="./cdm") -> pd.DataFrame:
         ranked_cohorts.loc[len(ranked_cohorts.index)] = [column, found_features, missing_features]
     # Sort values based on the number of successfully found features
     ranked_cohorts.sort_values(by="Successfully found", ascending=False, inplace=True)
+    # Reset the indices, otherwise creates an issue in json
+    ranked_cohorts.reset_index(drop=True, inplace=True)
     # Calculate the percentage of features found
-    percentage_found = ((ranked_cohorts['Successfully found'] / total_features) * 100).round(2)
+    percentage_found = ((ranked_cohorts["Successfully found"] / total_features) * 100).round(2)
     # Format the "Successfully found" column so that it displays the data in
     # "(found_features)/(total_features) (percentage_found)" format
-    ranked_cohorts['Successfully found'] = ranked_cohorts['Successfully found'].astype(str) + '/' + str(total_features) + ' (' + percentage_found.astype(str) + '%)'
+    ranked_cohorts["Successfully found"] = ranked_cohorts["Successfully found"].astype(str) + "/" + str(total_features) + " (" + percentage_found.astype(str) + "%)"
     return ranked_cohorts

backend/requirements.txt

@@ -5,17 +5,23 @@ fastapi==0.110.2
 h11==0.14.0
 httptools==0.6.1
 idna==3.7
+iniconfig==2.0.0
 numpy==1.26.4
+packaging==24.0
 pandas==2.2.2
+pluggy==1.5.0
 pydantic==2.7.0
 pydantic_core==2.18.1
+pytest==8.1.1
 python-dateutil==2.9.0.post0
 python-dotenv==1.0.1
 pytz==2024.1
 PyYAML==6.0.1
+rapidfuzz==3.8.1
 six==1.16.0
 sniffio==1.3.1
 starlette==0.37.2
+thefuzz==0.22.1
 typing_extensions==4.11.0
 tzdata==2024.1
 uvicorn==0.29.0

frontend/README.md

@@ -1,8 +1,23 @@
+# Frontend
+
+- [Frontend](#frontend)
+  - [Tutorial](#tutorial)
+  - [Requirements](#requirements)
+  - [Installation](#installation)
+  - [Usage](#usage)
+    - [Starting the Frontend Locally](#starting-the-frontend-locally)
+    - [Run the Frontend via Docker](#run-the-frontend-via-docker)
+
+
+## Tutorial
+
 https://angular.io/tutorial/
 
 ## Requirements
 
-- [Node.js >= 18.13](https://nodejs.org/en)
+- [Angular = 17.1.0](https://angular.io/guide/setup-local)
+- [Node.js (LTS) >= 18.13](https://nodejs.org/en/download/package-manager)
+- TypeScript >= 5.2.0 < 5.4.0
 
 ## Installation
 
@@ -12,6 +27,25 @@ npm install
 
 ## Usage
 
-```bash
+### Starting the Frontend Locally
+You can deploy a local version of the web application via Angular <br>
+You can access the web application on [localhost:4200](http://localhost:4200):
+
+``` bash
 ng serve
+```
+
+### Run the Frontend via Docker
+You can deploy a local version of the web application via docker. <br>
+You can either build the docker container locally or download the latest build from the PDataViewer GitHub package registry.
+
+``` bash
+docker build . -t ghcr.io/scai-bio/pdataviewer/frontend:latest
+docker pull ghcr.io/scai-bio/pdataviewer/frontend:latest
+```
+
+After build/download you will be able to start the container and access the PDataViewer web application per default on [localhost:8080](http://localhost:8080/):
+
+``` bash
+docker run -p 8080:80 ghcr.io/pdataviewer/scai-bio/frontend:latest
 ```