♻️ refactor(frontend): Fix ESLint warnings & small improvements

Author: LauraRandl

src/frontend-nextjs/app/api/post/route.ts

@@ -12,6 +12,7 @@ import { createNewPost } from '@/services/api/CreatePostService';
 export async function POST(request: Request): Promise<NextResponse> {
     const body = await request.json();
     let header = request.headers.get('header');
+
     if (header) {
         header = Buffer.from(header, 'latin1').toString('utf-8');
     }

src/frontend-nextjs/app/post/page.tsx

@@ -30,10 +30,10 @@ function SinglePost() {
                 <PostComponent
                     body={postData.body}
                     imageUrl={postData.imageUrl}
+                    isSpamPredictedLabel={postData.isSpamPredictedLabel}
                     postId={postData.postId}
                     timestamp={postData.timestamp}
                     username={postData.username}
-                    isSpamPredictedLabel={postData.isSpamPredictedLabel}
                 />
             )}
         </div>

src/frontend-nextjs/components/SwaggerUIReact.tsx

@@ -21,6 +21,7 @@ const HideTryItOutPlugin = () => ({
 
 function ReactSwagger({ spec }: Props) {
     const specWithoutServers = { ...spec };
+
     delete specWithoutServers.servers;
 
     return <SwaggerUI spec={specWithoutServers} plugins={[HideAuthorizePlugin, HideTryItOutPlugin]} />;

src/frontend-nextjs/components/Timeline/PostSpamPrediction.tsx

@@ -15,6 +15,7 @@ export interface PostSpamPredictionProps {
 
 export function PostSpamPrediction(props: Readonly<PostSpamPredictionProps>) {
     const { isLoggedIn } = useCheckLogin();
+
     if (props.isSpamPredictedLabel == null) return null;
 
     const color = props.isSpamPredictedLabel ? 'danger' : 'primary';

src/frontend-nextjs/components/Timeline/SpamPredictionUserRating.tsx

@@ -23,15 +23,16 @@ export function SpamPredictionUserRating(props: Readonly<SpamPredictionUserRatin
     return (
         <div>
             <Button
-                className=' text-default-600 bg-transparent'
+                className='bg-transparent text-default-600 px-2 min-w-0 gap-1'
                 name='upvoteSpamRating'
                 onPress={() => handleSpamPredictionUpvote()}
             >
                 <p>{spamPredictionUserRatingData?.spamPredictionUserUpvotes}</p>
                 {spamPredictionUserRatingData?.isUpvotedByUser ? <BsHandThumbsUpFill /> : <BsHandThumbsUp />}
             </Button>
+
             <Button
-                className=' text-default-600 bg-transparent'
+                className='bg-transparent text-default-600 px-2 min-w-0 gap-1'
                 name='downvoteSpamRating'
                 onPress={() => handleSpamPredictionDownvote()}
             >

src/microblog-service/README.md

@@ -60,3 +60,5 @@ To get more information about the JAEGER config options, see https://www.jaegert
 | JAEGER_SAMPLER_TYPE       | const                     | (optional) Set to const to get all traces                    |
 | JAEGER_SAMPLER_PARAM      | 1                         | (optional) Set to 1 while sampler is const to get all traces |
 | USER_AUTH_SERVICE_ADDRESS | unguard-user-auth-service | Change to hostname/IP of user-auth-service instance          |
+| RAG_SERVICE_ADDRESS       | unguard-rag-service       | Change to hostname/IP of rag-service instance                |
+| RAG_SERVICE_PORT          | 8000                      | Change to port number of rag-service instance                |

src/rag-service/README.md

@@ -10,6 +10,22 @@ A microservice for spam classification using Retrieval-Augmented Generation (RAG
 - **[Langdock](https://www.langdock.com)**: OpenAI LLM and embeddings
 - **[Ollama](https://ollama.com/)**: Local open-source LLM and embeddings
 
+## Environment Variables
+| Name                                            | Example Value                    | Description                                                                       |
+|-------------------------------------------------|----------------------------------|-----------------------------------------------------------------------------------|
+| LLM_MODEL                                       | llama3.2:latest                  | The LLM model that will be used by the RAG service                                |
+| EMBEDDINGS_MODEL                                | nomic-embed-text                 | The embeddings model that will be used by the RAG service                         |
+| MODEL_PROVIDER                                  | Ollama                           | Can be either "Ollama" or "LangDock"                                              |
+| MODEL_PROVIDER_BASE_URL                         | http://localhost:11434           | Base url to your model                                                            |
+| LANGDOCK_API_KEY                                | <your_langdock_api_key>          | (optional) Langdock API key, only needed when using LangDock                      |
+| EVALUATE_AFTER_ATTACK                           | false                            | Configure whether the RAG service performance should be evaluated after an attack |
+| LIMIT_EVALUATION_SAMPLES                        | 0                                |                                                                                   |
+| LIMIT_KEYWORD_ATTACK_SUCCESS_EVALUATION_SAMPLES | 0                                | Change to hostname/IP of rag-service instance                                     |
+| USE_DATA_POISONING_DETECTION                    | falses                           | Change to port number of rag-service instance                                     |
+| DATA_POISONING_DETECTION_STRATEGY               | embedding_similarity_entry_level | The data poisoning detection strategy to use                                      |
+| LABEL_CONSISTENCY_DETECTION_DECISION_VARIANT    | majority_voting                  | The variant of the label consistency detection strategy to use                    |
+
+
 ## Getting Started for running the RAG Service locally
 
 When starting for the first time, you need to create a Virtual Environment, activate it and install the dependencies: