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Jacek Dusza commited on
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Parent(s):
Initial commit: NLP Pipeline backend and React frontend
Browse files- .gitignore +43 -0
- README.md +16 -0
- backend/augmented_dataset.csv +0 -0
- backend/clean_data.py +18 -0
- backend/dataset_test.csv +0 -0
- backend/download_polemo.py +27 -0
- backend/main.py +153 -0
- backend/run_experiments.py +135 -0
- backend/train_classifier.py +122 -0
- eslint.config.js +21 -0
- index.html +13 -0
- package-lock.json +2454 -0
- package.json +27 -0
- public/favicon.svg +1 -0
- public/icons.svg +24 -0
- src/App.css +184 -0
- src/App.jsx +705 -0
- src/assets/hero.png +0 -0
- src/assets/react.svg +1 -0
- src/assets/vite.svg +1 -0
- src/index.css +111 -0
- src/main.jsx +10 -0
- vite.config.js +7 -0
.gitignore
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# Logs
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logs
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*.log
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npm-debug.log*
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yarn-debug.log*
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yarn-error.log*
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pnpm-debug.log*
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lerna-debug.log*
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node_modules
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dist
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dist-ssr
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*.local
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# Editor directories and files
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.vscode/*
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!.vscode/extensions.json
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.idea
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.DS_Store
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*.suo
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*.ntvs*
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*.njsproj
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*.sln
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*.sw?
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# Dependency directories
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node_modules/
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backend/venv/
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# Python cache
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__pycache__/
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*.pyc
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# Environment variables (SECURITY)
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.env
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backend/.env
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# macOS system files
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.DS_Store
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# Output data (optional, prevents uploading large datasets)
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backend/results_aug/
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backend/results_base/
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README.md
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# React + Vite
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This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules.
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Currently, two official plugins are available:
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- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react) uses [Oxc](https://oxc.rs)
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- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react-swc) uses [SWC](https://swc.rs/)
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## React Compiler
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The React Compiler is not enabled on this template because of its impact on dev & build performances. To add it, see [this documentation](https://react.dev/learn/react-compiler/installation).
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## Expanding the ESLint configuration
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If you are developing a production application, we recommend using TypeScript with type-aware lint rules enabled. Check out the [TS template](https://github.com/vitejs/vite/tree/main/packages/create-vite/template-react-ts) for information on how to integrate TypeScript and [`typescript-eslint`](https://typescript-eslint.io) in your project.
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backend/augmented_dataset.csv
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The diff for this file is too large to render.
See raw diff
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backend/clean_data.py
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import pandas as pd
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# Load the augmented dataset from the CSV file
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df = pd.read_csv("augmented_dataset.csv")
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size_before = len(df)
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# Remove duplicate entries based on the unique 'id' column,
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# retaining the most recently generated variant (keep='last')
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df_clean = df.drop_duplicates(subset=['id'], keep='last')
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size_after = len(df_clean)
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# Overwrite the existing file with the cleaned, deduplicated dataset
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df_clean.to_csv("augmented_dataset.csv", index=False, encoding="utf-8")
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# Output the results of the cleaning process
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print("Data cleaning process completed successfully.")
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print(f"Identified and removed {size_before - size_after} duplicate records.")
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print(f"Current deduplicated dataset size: {size_after} samples.")
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backend/dataset_test.csv
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The diff for this file is too large to render.
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backend/download_polemo.py
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from datasets import load_dataset
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import pandas as pd
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def download_data():
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print("Connecting to Hugging Face and downloading the PolEmo2.0 dataset...")
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# Load the 'all_text' configuration of the dataset
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dataset = load_dataset("clarin-pl/polemo2-official", "all_text", split="train", trust_remote_code=True)
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# Convert the first 400 samples to a pandas DataFrame
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df = dataset.select(range(400)).to_pandas()
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# Rename the target column to 'label' for pipeline compatibility
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df = df.rename(columns={"target": "label"})
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# Drop technical columns, retaining only 'text' and 'label'
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df = df[['text', 'label']]
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# Insert a sequential ID column for tracking purposes
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df.insert(0, 'id', range(1, len(df) + 1))
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# Export the processed dataset to a CSV file
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df.to_csv("dataset_test.csv", index=False, encoding="utf-8")
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print("Success! Saved 400 authentic, diverse reviews to 'dataset_test.csv'.")
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if __name__ == "__main__":
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download_data()
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backend/main.py
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import os
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import time
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from fastapi import FastAPI, HTTPException
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from fastapi.middleware.cors import CORSMiddleware
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from pydantic import BaseModel
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from deep_translator import GoogleTranslator
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from sentence_transformers import SentenceTransformer, util
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import nlpaug.augmenter.word as naw
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from groq import Groq
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from dotenv import load_dotenv
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# Load environment variables from the .env file (for local development)
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load_dotenv()
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# Application initialization
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app = FastAPI(title="Hybrid Augmentation Pipeline API")
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print("Loading Sentence-BERT model (this might take a moment on the first run)...")
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# Using a lightweight, multilingual model (supports Polish and English)
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model = SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2')
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print("Sentence-BERT model loaded successfully.")
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print("Loading HerBERT model for EDA...")
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# Using HerBERT for contextual word substitution in the Polish language
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eda_aug = naw.ContextualWordEmbsAug(
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model_path='allegro/herbert-base-cased',
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action="substitute",
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device="cpu", # Switch to "cuda" if appropriate GPU hardware is available
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aug_p=0.1, # Substitute a maximum of 10% of words in the input sequence
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top_k=10
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)
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print("HerBERT model loaded successfully.")
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print("Configuring Groq API...")
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# Securely fetching the API key from environment variables
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GROQ_API_KEY = os.getenv("GROQ_API_KEY")
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if not GROQ_API_KEY:
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raise ValueError("CRITICAL ERROR: GROQ_API_KEY environment variable is missing!")
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groq_client = Groq(api_key=GROQ_API_KEY)
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print("Groq API configured successfully.")
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# CORS configuration (allows the frontend application to communicate with the backend)
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"], # In production, restrict this to the specific frontend URL
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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# 1. Data Models (Expected JSON payloads from the frontend client)
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class AugmentRequest(BaseModel):
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text: str
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method: str
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pivot_lang: str = "en"
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eda_p: float = 0.1 # Percentage of words to substitute during EDA
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class FilterRequest(BaseModel):
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original: str
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augmented: str
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threshold: float = 0.8 # Cutoff threshold for the semantic similarity filter
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# 2. Endpoint: Data Augmentation
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@app.post("/augment")
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async def generate_paraphrase(request: AugmentRequest):
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try:
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# Simulated network delay for UI feedback
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time.sleep(1)
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if request.method == "EDA":
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# Dynamically adjust the substitution probability based on client input
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eda_aug.aug_p = request.eda_p
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result = eda_aug.augment(request.text)[0]
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elif request.method == "BT":
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# Retrieve the target pivot language from the request
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pivot_lang = request.pivot_lang.lower()
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# Step 1: Source (PL) -> Pivot Language
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intermediate_text = GoogleTranslator(source='pl', target=pivot_lang).translate(request.text)
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# Step 2: Pivot Language -> Source (PL)
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result = GoogleTranslator(source=pivot_lang, target='pl').translate(intermediate_text)
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return {
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"original": request.text,
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"augmented": result,
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"method": request.method,
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"pivot_lang": pivot_lang,
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"intermediate": intermediate_text
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}
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elif request.method == "LLM":
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# LLM Generation (Llama 3 via Groq API)
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prompt = f"""You are an expert NLP assistant. Paraphrase the following Polish sentence in Polish.
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Maintain the exact same meaning and sentiment, but use a different syntactic structure or synonyms.
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Return ONLY the single paraphrased sentence. Do not include any introductions, notes, or comments.
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Input sentence: {request.text}"""
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chat_completion = groq_client.chat.completions.create(
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messages=[{"role": "user", "content": prompt}],
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model="llama-3.3-70b-versatile",
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temperature=0.7,
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max_tokens=100,
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)
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result = chat_completion.choices[0].message.content.strip()
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# Strip enclosing quotation marks if generated by the model
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if result.startswith('"') and result.endswith('"'):
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result = result[1:-1]
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else:
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raise HTTPException(status_code=400, detail="Unknown augmentation method requested.")
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return {"original": request.text, "augmented": result, "method": request.method}
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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# 3. Endpoint: Semantic Filtration (Sentence-BERT)
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@app.post("/filter")
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async def filter_sentence(request: FilterRequest):
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try:
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# Compute dense vector embeddings for both text sequences
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emb1 = model.encode(request.original, convert_to_tensor=True)
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emb2 = model.encode(request.augmented, convert_to_tensor=True)
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# Compute cosine similarity between the embeddings
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cosine_scores = util.cos_sim(emb1, emb2)
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sim_score = cosine_scores[0][0].item() # Extract the numerical value from the tensor
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# Floating-point precision safeguard (clamp between 0.0 and 1.0)
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sim_score = min(max(sim_score, 0.0), 1.0)
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# Evaluate against the requested threshold
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passed = sim_score >= request.threshold
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return {
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| 142 |
+
"similarity": round(sim_score, 3),
|
| 143 |
+
"passed": passed,
|
| 144 |
+
"threshold": request.threshold
|
| 145 |
+
}
|
| 146 |
+
|
| 147 |
+
except Exception as e:
|
| 148 |
+
raise HTTPException(status_code=500, detail=str(e))
|
| 149 |
+
|
| 150 |
+
# 4. Endpoint: Health Check
|
| 151 |
+
@app.get("/")
|
| 152 |
+
def read_root():
|
| 153 |
+
return {"status": "Backend is running successfully."}
|
backend/run_experiments.py
ADDED
|
@@ -0,0 +1,135 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import requests
|
| 2 |
+
import pandas as pd
|
| 3 |
+
import time
|
| 4 |
+
import os
|
| 5 |
+
|
| 6 |
+
INPUT_CSV = "dataset_test.csv"
|
| 7 |
+
OUTPUT_CSV = "augmented_dataset2.csv"
|
| 8 |
+
API_URL = "http://127.0.0.1:8000"
|
| 9 |
+
|
| 10 |
+
# Hyperparameters (can be modified for subsequent tests)
|
| 11 |
+
EDA_P = 0.15
|
| 12 |
+
SBERT_THRESH = 0.70
|
| 13 |
+
|
| 14 |
+
# Complete list of configurations: (Method, Pivot_Language)
|
| 15 |
+
METHODS_TO_TEST = [
|
| 16 |
+
("EDA", "none"), # Lexical perturbations (pivot ignored)
|
| 17 |
+
("BT", "en"), # Back-Translation via English
|
| 18 |
+
("BT", "de"), # Back-Translation via German
|
| 19 |
+
("BT", "cs"), # Back-Translation via Czech
|
| 20 |
+
("LLM", "none") # Generative LLM paraphrasing (pivot ignored)
|
| 21 |
+
]
|
| 22 |
+
|
| 23 |
+
def run_mass_augmentation():
|
| 24 |
+
print(f"Starting experiment for {len(METHODS_TO_TEST)} variants. S-BERT Threshold: {SBERT_THRESH*100}%")
|
| 25 |
+
|
| 26 |
+
# Check if input file exists
|
| 27 |
+
if not os.path.exists(INPUT_CSV):
|
| 28 |
+
print(f"CRITICAL ERROR: Input file {INPUT_CSV} not found.")
|
| 29 |
+
return
|
| 30 |
+
|
| 31 |
+
df = pd.read_csv(INPUT_CSV)
|
| 32 |
+
augmented_pool = []
|
| 33 |
+
|
| 34 |
+
try:
|
| 35 |
+
for index, row in df.iterrows():
|
| 36 |
+
original_text = row['text']
|
| 37 |
+
label = row['label']
|
| 38 |
+
print(f"\n[{index+1}/{len(df)}] Processing: {original_text[:40]}...")
|
| 39 |
+
|
| 40 |
+
for method, pivot in METHODS_TO_TEST:
|
| 41 |
+
method_name = f"{method}_{pivot}" if method == "BT" else method
|
| 42 |
+
|
| 43 |
+
try:
|
| 44 |
+
# 1. GENERATION PHASE
|
| 45 |
+
payload = {
|
| 46 |
+
"text": original_text,
|
| 47 |
+
"method": method,
|
| 48 |
+
"pivot_lang": pivot,
|
| 49 |
+
"eda_p": EDA_P
|
| 50 |
+
}
|
| 51 |
+
aug_response = requests.post(f"{API_URL}/augment", json=payload)
|
| 52 |
+
|
| 53 |
+
# Check if the server responded with 200 OK status
|
| 54 |
+
if aug_response.status_code != 200:
|
| 55 |
+
error_msg = aug_response.text
|
| 56 |
+
print(f" API Rejected ({aug_response.status_code}) for {method_name}")
|
| 57 |
+
|
| 58 |
+
# Defensive mechanism against API rate limits (e.g., Groq Cloud)
|
| 59 |
+
if "429" in error_msg or "502" in error_msg or "rate limit" in error_msg.lower():
|
| 60 |
+
print(" API Rate limit reached. Sleeping for 60 seconds...")
|
| 61 |
+
time.sleep(60)
|
| 62 |
+
continue
|
| 63 |
+
|
| 64 |
+
aug_data = aug_response.json()
|
| 65 |
+
augmented_text = aug_data.get("augmented")
|
| 66 |
+
|
| 67 |
+
if not augmented_text or augmented_text in ["BŁĄD", "ERROR"]:
|
| 68 |
+
continue
|
| 69 |
+
|
| 70 |
+
# 2. S-BERT SEMANTIC FILTRATION PHASE
|
| 71 |
+
filter_payload = {
|
| 72 |
+
"original": original_text,
|
| 73 |
+
"augmented": augmented_text,
|
| 74 |
+
"threshold": SBERT_THRESH
|
| 75 |
+
}
|
| 76 |
+
filt_res = requests.post(f"{API_URL}/filter", json=filter_payload).json()
|
| 77 |
+
|
| 78 |
+
sim_score = filt_res.get("similarity", 0)
|
| 79 |
+
passed = filt_res.get("passed", False)
|
| 80 |
+
|
| 81 |
+
# 3. MEMORY BUFFER ALLOCATION
|
| 82 |
+
if passed:
|
| 83 |
+
print(f" {method_name.ljust(6)} ACCEPTED (Sim: {sim_score:.3f})")
|
| 84 |
+
augmented_pool.append({
|
| 85 |
+
"id": f"{row['id']}_aug_{method_name}",
|
| 86 |
+
"text": augmented_text,
|
| 87 |
+
"label": label,
|
| 88 |
+
"is_synthetic": True,
|
| 89 |
+
"source_method": method_name,
|
| 90 |
+
"similarity_score": sim_score
|
| 91 |
+
})
|
| 92 |
+
else:
|
| 93 |
+
print(f" {method_name.ljust(6)} REJECTED (Sim: {sim_score:.3f})")
|
| 94 |
+
|
| 95 |
+
except requests.exceptions.RequestException as e:
|
| 96 |
+
print(f" Network connection error for {method_name}: {e}")
|
| 97 |
+
|
| 98 |
+
# Safe time delay between API calls to prevent throttling
|
| 99 |
+
time.sleep(1.5) if method == "LLM" else time.sleep(0.3)
|
| 100 |
+
|
| 101 |
+
except KeyboardInterrupt:
|
| 102 |
+
print("\n\nMANUALLY INTERRUPTED! Stopping execution, initiating emergency data save...")
|
| 103 |
+
|
| 104 |
+
# === DATA PERSISTENCE BLOCK ===
|
| 105 |
+
if augmented_pool:
|
| 106 |
+
print("\nWriting generated samples to disk...")
|
| 107 |
+
new_data_df = pd.DataFrame(augmented_pool)
|
| 108 |
+
|
| 109 |
+
try:
|
| 110 |
+
# If the file exists, append to it; otherwise, create a new one with baseline data
|
| 111 |
+
if os.path.exists(OUTPUT_CSV):
|
| 112 |
+
existing_df = pd.read_csv(OUTPUT_CSV)
|
| 113 |
+
final_dataset = pd.concat([existing_df, new_data_df], ignore_index=True)
|
| 114 |
+
# Remove any accidental duplicates based on ID
|
| 115 |
+
final_dataset = final_dataset.drop_duplicates(subset=['id'], keep='last')
|
| 116 |
+
else:
|
| 117 |
+
# If no previous file exists, initialize with the baseline dataset
|
| 118 |
+
baseline_df = df.copy()
|
| 119 |
+
baseline_df['is_synthetic'] = False
|
| 120 |
+
baseline_df['source_method'] = 'ORIGINAL'
|
| 121 |
+
baseline_df['similarity_score'] = 1.0
|
| 122 |
+
final_dataset = pd.concat([baseline_df, new_data_df], ignore_index=True)
|
| 123 |
+
|
| 124 |
+
# Save the final dataset
|
| 125 |
+
final_dataset.to_csv(OUTPUT_CSV, index=False, encoding='utf-8')
|
| 126 |
+
|
| 127 |
+
print(f"Success! Saved {len(new_data_df)} new samples.")
|
| 128 |
+
print(f"Total dataset volume: {len(final_dataset)} records.")
|
| 129 |
+
except Exception as e:
|
| 130 |
+
print(f"CRITICAL: Failed to save data. Error: {e}")
|
| 131 |
+
else:
|
| 132 |
+
print("\nNo new accepted samples to serialize.")
|
| 133 |
+
|
| 134 |
+
if __name__ == "__main__":
|
| 135 |
+
run_mass_augmentation()
|
backend/train_classifier.py
ADDED
|
@@ -0,0 +1,122 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import pandas as pd
|
| 2 |
+
import torch
|
| 3 |
+
from sklearn.model_selection import train_test_split
|
| 4 |
+
from sklearn.metrics import accuracy_score, f1_score
|
| 5 |
+
from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments
|
| 6 |
+
from datasets import Dataset
|
| 7 |
+
import os
|
| 8 |
+
|
| 9 |
+
# Disable Weights & Biases logging and tokenizer parallelism warnings
|
| 10 |
+
os.environ["WANDB_DISABLED"] = "true"
|
| 11 |
+
os.environ["TOKENIZERS_PARALLELISM"] = "false"
|
| 12 |
+
|
| 13 |
+
MODEL_NAME = "allegro/herbert-base-cased"
|
| 14 |
+
DATA_FILE = "augmented_dataset.csv"
|
| 15 |
+
|
| 16 |
+
print("Starting the classifier evaluation on HerBERT model.")
|
| 17 |
+
|
| 18 |
+
# 1. DATA LOADING AND PREPARATION
|
| 19 |
+
df = pd.read_csv(DATA_FILE)
|
| 20 |
+
|
| 21 |
+
# Separate original and synthetic datasets
|
| 22 |
+
df_orig = df[df['is_synthetic'] == False].copy()
|
| 23 |
+
df_aug = df[df['is_synthetic'] == True].copy()
|
| 24 |
+
|
| 25 |
+
# Map text labels to numerical indices
|
| 26 |
+
label_mapping = {label: idx for idx, label in enumerate(df_orig['label'].unique())}
|
| 27 |
+
df_orig['label_idx'] = df_orig['label'].map(label_mapping)
|
| 28 |
+
df_aug['label_idx'] = df_aug['label'].map(label_mapping)
|
| 29 |
+
|
| 30 |
+
# Split original data into training (80%) and testing (20%) sets
|
| 31 |
+
train_orig, test_data = train_test_split(df_orig, test_size=0.2, random_state=42, stratify=df_orig['label_idx'])
|
| 32 |
+
|
| 33 |
+
# DATA LEAKAGE PROTECTION
|
| 34 |
+
# Ensure synthetic samples are only used if their original source is in the training set
|
| 35 |
+
train_orig_ids = train_orig['id'].astype(str).tolist()
|
| 36 |
+
df_aug_filtered = df_aug[df_aug['id'].apply(lambda x: str(x).split('_')[0] in train_orig_ids)]
|
| 37 |
+
|
| 38 |
+
# Combine original training data with valid augmented data
|
| 39 |
+
train_augmented = pd.concat([train_orig, df_aug_filtered], ignore_index=True)
|
| 40 |
+
|
| 41 |
+
print("\nExperiment structure:")
|
| 42 |
+
print(f" - Test dataset (immutable): {len(test_data)} samples")
|
| 43 |
+
print(f" - Training BASELINE: {len(train_orig)} samples")
|
| 44 |
+
print(f" - Training AUGMENTED: {len(train_augmented)} samples")
|
| 45 |
+
|
| 46 |
+
# 2. TOKENIZATION
|
| 47 |
+
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
|
| 48 |
+
|
| 49 |
+
def tokenize_data(data_df):
|
| 50 |
+
dataset = Dataset.from_pandas(data_df[['text', 'label_idx']])
|
| 51 |
+
return dataset.map(
|
| 52 |
+
lambda e: tokenizer(e['text'], truncation=True, padding='max_length', max_length=128),
|
| 53 |
+
batched=True
|
| 54 |
+
).rename_column("label_idx", "labels")
|
| 55 |
+
|
| 56 |
+
print("\nTokenizing datasets...")
|
| 57 |
+
test_dataset = tokenize_data(test_data)
|
| 58 |
+
train_orig_dataset = tokenize_data(train_orig)
|
| 59 |
+
train_aug_dataset = tokenize_data(train_augmented)
|
| 60 |
+
|
| 61 |
+
# 3. METRICS EVALUATION
|
| 62 |
+
def compute_metrics(pred):
|
| 63 |
+
labels = pred.label_ids
|
| 64 |
+
preds = pred.predictions.argmax(-1)
|
| 65 |
+
acc = accuracy_score(labels, preds)
|
| 66 |
+
f1 = f1_score(labels, preds, average='macro')
|
| 67 |
+
return {'accuracy': acc, 'f1_macro': f1}
|
| 68 |
+
|
| 69 |
+
# 4. TRAINING ENGINE
|
| 70 |
+
def train_and_evaluate(train_ds, test_ds, output_dir):
|
| 71 |
+
model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME, num_labels=len(label_mapping))
|
| 72 |
+
|
| 73 |
+
training_args = TrainingArguments(
|
| 74 |
+
output_dir=output_dir,
|
| 75 |
+
num_train_epochs=3,
|
| 76 |
+
per_device_train_batch_size=16,
|
| 77 |
+
per_device_eval_batch_size=16,
|
| 78 |
+
eval_strategy="epoch",
|
| 79 |
+
save_strategy="no",
|
| 80 |
+
logging_dir='./logs',
|
| 81 |
+
report_to="none"
|
| 82 |
+
)
|
| 83 |
+
|
| 84 |
+
trainer = Trainer(
|
| 85 |
+
model=model,
|
| 86 |
+
args=training_args,
|
| 87 |
+
train_dataset=train_ds,
|
| 88 |
+
eval_dataset=test_ds,
|
| 89 |
+
compute_metrics=compute_metrics
|
| 90 |
+
)
|
| 91 |
+
|
| 92 |
+
trainer.train()
|
| 93 |
+
return trainer.evaluate()
|
| 94 |
+
|
| 95 |
+
# 5. EXPERIMENT EXECUTION
|
| 96 |
+
print("\n" + "="*50)
|
| 97 |
+
print("STEP 1: Training the BASELINE model")
|
| 98 |
+
print("="*50)
|
| 99 |
+
base_metrics = train_and_evaluate(train_orig_dataset, test_dataset, "./results_base")
|
| 100 |
+
|
| 101 |
+
print("\n" + "="*50)
|
| 102 |
+
print("STEP 2: Training the AUGMENTED model")
|
| 103 |
+
print("="*50)
|
| 104 |
+
aug_metrics = train_and_evaluate(train_aug_dataset, test_dataset, "./results_aug")
|
| 105 |
+
|
| 106 |
+
# 6. RESULTS OUTPUT
|
| 107 |
+
print("\n\n" + "FINAL EXPERIMENT RESULTS".center(50))
|
| 108 |
+
print("-" * 52)
|
| 109 |
+
print(f"Metric | Baseline | Augmented | Change")
|
| 110 |
+
print("-" * 52)
|
| 111 |
+
|
| 112 |
+
base_f1 = base_metrics['eval_f1_macro'] * 100
|
| 113 |
+
aug_f1 = aug_metrics['eval_f1_macro'] * 100
|
| 114 |
+
diff_f1 = aug_f1 - base_f1
|
| 115 |
+
|
| 116 |
+
base_acc = base_metrics['eval_accuracy'] * 100
|
| 117 |
+
aug_acc = aug_metrics['eval_accuracy'] * 100
|
| 118 |
+
diff_acc = aug_acc - base_acc
|
| 119 |
+
|
| 120 |
+
print(f"Macro-F1 | {base_f1:10.2f}% | {aug_f1:10.2f}% | {diff_f1:+5.2f} pp.")
|
| 121 |
+
print(f"Accuracy | {base_acc:10.2f}% | {aug_acc:10.2f}% | {diff_acc:+5.2f} pp.")
|
| 122 |
+
print("-" * 52)
|
eslint.config.js
ADDED
|
@@ -0,0 +1,21 @@
|
|
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|
|
|
|
| 1 |
+
import js from '@eslint/js'
|
| 2 |
+
import globals from 'globals'
|
| 3 |
+
import reactHooks from 'eslint-plugin-react-hooks'
|
| 4 |
+
import reactRefresh from 'eslint-plugin-react-refresh'
|
| 5 |
+
import { defineConfig, globalIgnores } from 'eslint/config'
|
| 6 |
+
|
| 7 |
+
export default defineConfig([
|
| 8 |
+
globalIgnores(['dist']),
|
| 9 |
+
{
|
| 10 |
+
files: ['**/*.{js,jsx}'],
|
| 11 |
+
extends: [
|
| 12 |
+
js.configs.recommended,
|
| 13 |
+
reactHooks.configs.flat.recommended,
|
| 14 |
+
reactRefresh.configs.vite,
|
| 15 |
+
],
|
| 16 |
+
languageOptions: {
|
| 17 |
+
globals: globals.browser,
|
| 18 |
+
parserOptions: { ecmaFeatures: { jsx: true } },
|
| 19 |
+
},
|
| 20 |
+
},
|
| 21 |
+
])
|
index.html
ADDED
|
@@ -0,0 +1,13 @@
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|
|
| 1 |
+
<!doctype html>
|
| 2 |
+
<html lang="en">
|
| 3 |
+
<head>
|
| 4 |
+
<meta charset="UTF-8" />
|
| 5 |
+
<link rel="icon" type="image/svg+xml" href="/favicon.svg" />
|
| 6 |
+
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
| 7 |
+
<title>data-augmentation</title>
|
| 8 |
+
</head>
|
| 9 |
+
<body>
|
| 10 |
+
<div id="root"></div>
|
| 11 |
+
<script type="module" src="/src/main.jsx"></script>
|
| 12 |
+
</body>
|
| 13 |
+
</html>
|
package-lock.json
ADDED
|
@@ -0,0 +1,2454 @@
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|
| 1 |
+
{
|
| 2 |
+
"name": "data-augmentation",
|
| 3 |
+
"version": "0.0.0",
|
| 4 |
+
"lockfileVersion": 3,
|
| 5 |
+
"requires": true,
|
| 6 |
+
"packages": {
|
| 7 |
+
"": {
|
| 8 |
+
"name": "data-augmentation",
|
| 9 |
+
"version": "0.0.0",
|
| 10 |
+
"dependencies": {
|
| 11 |
+
"react": "^19.2.6",
|
| 12 |
+
"react-dom": "^19.2.6"
|
| 13 |
+
},
|
| 14 |
+
"devDependencies": {
|
| 15 |
+
"@eslint/js": "^10.0.1",
|
| 16 |
+
"@types/react": "^19.2.14",
|
| 17 |
+
"@types/react-dom": "^19.2.3",
|
| 18 |
+
"@vitejs/plugin-react": "^6.0.1",
|
| 19 |
+
"eslint": "^10.3.0",
|
| 20 |
+
"eslint-plugin-react-hooks": "^7.1.1",
|
| 21 |
+
"eslint-plugin-react-refresh": "^0.5.2",
|
| 22 |
+
"globals": "^17.6.0",
|
| 23 |
+
"vite": "^8.0.12"
|
| 24 |
+
}
|
| 25 |
+
},
|
| 26 |
+
"node_modules/@babel/code-frame": {
|
| 27 |
+
"version": "7.29.7",
|
| 28 |
+
"resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.29.7.tgz",
|
| 29 |
+
"integrity": "sha512-Aup7aUOfpbAUg2ROOJN6Iw5f9DMBlzu0mIkm/malLQFN/YQgO48wCj0Kxa3sEHJvPVFg7siR+qRInwXd2qhQKw==",
|
| 30 |
+
"dev": true,
|
| 31 |
+
"license": "MIT",
|
| 32 |
+
"dependencies": {
|
| 33 |
+
"@babel/helper-validator-identifier": "^7.29.7",
|
| 34 |
+
"js-tokens": "^4.0.0",
|
| 35 |
+
"picocolors": "^1.1.1"
|
| 36 |
+
},
|
| 37 |
+
"engines": {
|
| 38 |
+
"node": ">=6.9.0"
|
| 39 |
+
}
|
| 40 |
+
},
|
| 41 |
+
"node_modules/@babel/compat-data": {
|
| 42 |
+
"version": "7.29.7",
|
| 43 |
+
"resolved": "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.29.7.tgz",
|
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package.json
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{
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| 14 |
+
"react-dom": "^19.2.6"
|
| 15 |
+
},
|
| 16 |
+
"devDependencies": {
|
| 17 |
+
"@eslint/js": "^10.0.1",
|
| 18 |
+
"@types/react": "^19.2.14",
|
| 19 |
+
"@types/react-dom": "^19.2.3",
|
| 20 |
+
"@vitejs/plugin-react": "^6.0.1",
|
| 21 |
+
"eslint": "^10.3.0",
|
| 22 |
+
"eslint-plugin-react-hooks": "^7.1.1",
|
| 23 |
+
"eslint-plugin-react-refresh": "^0.5.2",
|
| 24 |
+
"globals": "^17.6.0",
|
| 25 |
+
"vite": "^8.0.12"
|
| 26 |
+
}
|
| 27 |
+
}
|
public/favicon.svg
ADDED
|
|
public/icons.svg
ADDED
|
|
src/App.css
ADDED
|
@@ -0,0 +1,184 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
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|
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|
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|
|
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|
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|
|
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|
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|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
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|
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|
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|
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|
|
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|
|
|
|
|
|
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|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
|
|
|
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|
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|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
.counter {
|
| 2 |
+
font-size: 16px;
|
| 3 |
+
padding: 5px 10px;
|
| 4 |
+
border-radius: 5px;
|
| 5 |
+
color: var(--accent);
|
| 6 |
+
background: var(--accent-bg);
|
| 7 |
+
border: 2px solid transparent;
|
| 8 |
+
transition: border-color 0.3s;
|
| 9 |
+
margin-bottom: 24px;
|
| 10 |
+
|
| 11 |
+
&:hover {
|
| 12 |
+
border-color: var(--accent-border);
|
| 13 |
+
}
|
| 14 |
+
&:focus-visible {
|
| 15 |
+
outline: 2px solid var(--accent);
|
| 16 |
+
outline-offset: 2px;
|
| 17 |
+
}
|
| 18 |
+
}
|
| 19 |
+
|
| 20 |
+
.hero {
|
| 21 |
+
position: relative;
|
| 22 |
+
|
| 23 |
+
.base,
|
| 24 |
+
.framework,
|
| 25 |
+
.vite {
|
| 26 |
+
inset-inline: 0;
|
| 27 |
+
margin: 0 auto;
|
| 28 |
+
}
|
| 29 |
+
|
| 30 |
+
.base {
|
| 31 |
+
width: 170px;
|
| 32 |
+
position: relative;
|
| 33 |
+
z-index: 0;
|
| 34 |
+
}
|
| 35 |
+
|
| 36 |
+
.framework,
|
| 37 |
+
.vite {
|
| 38 |
+
position: absolute;
|
| 39 |
+
}
|
| 40 |
+
|
| 41 |
+
.framework {
|
| 42 |
+
z-index: 1;
|
| 43 |
+
top: 34px;
|
| 44 |
+
height: 28px;
|
| 45 |
+
transform: perspective(2000px) rotateZ(300deg) rotateX(44deg) rotateY(39deg)
|
| 46 |
+
scale(1.4);
|
| 47 |
+
}
|
| 48 |
+
|
| 49 |
+
.vite {
|
| 50 |
+
z-index: 0;
|
| 51 |
+
top: 107px;
|
| 52 |
+
height: 26px;
|
| 53 |
+
width: auto;
|
| 54 |
+
transform: perspective(2000px) rotateZ(300deg) rotateX(40deg) rotateY(39deg)
|
| 55 |
+
scale(0.8);
|
| 56 |
+
}
|
| 57 |
+
}
|
| 58 |
+
|
| 59 |
+
#center {
|
| 60 |
+
display: flex;
|
| 61 |
+
flex-direction: column;
|
| 62 |
+
gap: 25px;
|
| 63 |
+
place-content: center;
|
| 64 |
+
place-items: center;
|
| 65 |
+
flex-grow: 1;
|
| 66 |
+
|
| 67 |
+
@media (max-width: 1024px) {
|
| 68 |
+
padding: 32px 20px 24px;
|
| 69 |
+
gap: 18px;
|
| 70 |
+
}
|
| 71 |
+
}
|
| 72 |
+
|
| 73 |
+
#next-steps {
|
| 74 |
+
display: flex;
|
| 75 |
+
border-top: 1px solid var(--border);
|
| 76 |
+
text-align: left;
|
| 77 |
+
|
| 78 |
+
& > div {
|
| 79 |
+
flex: 1 1 0;
|
| 80 |
+
padding: 32px;
|
| 81 |
+
@media (max-width: 1024px) {
|
| 82 |
+
padding: 24px 20px;
|
| 83 |
+
}
|
| 84 |
+
}
|
| 85 |
+
|
| 86 |
+
.icon {
|
| 87 |
+
margin-bottom: 16px;
|
| 88 |
+
width: 22px;
|
| 89 |
+
height: 22px;
|
| 90 |
+
}
|
| 91 |
+
|
| 92 |
+
@media (max-width: 1024px) {
|
| 93 |
+
flex-direction: column;
|
| 94 |
+
text-align: center;
|
| 95 |
+
}
|
| 96 |
+
}
|
| 97 |
+
|
| 98 |
+
#docs {
|
| 99 |
+
border-right: 1px solid var(--border);
|
| 100 |
+
|
| 101 |
+
@media (max-width: 1024px) {
|
| 102 |
+
border-right: none;
|
| 103 |
+
border-bottom: 1px solid var(--border);
|
| 104 |
+
}
|
| 105 |
+
}
|
| 106 |
+
|
| 107 |
+
#next-steps ul {
|
| 108 |
+
list-style: none;
|
| 109 |
+
padding: 0;
|
| 110 |
+
display: flex;
|
| 111 |
+
gap: 8px;
|
| 112 |
+
margin: 32px 0 0;
|
| 113 |
+
|
| 114 |
+
.logo {
|
| 115 |
+
height: 18px;
|
| 116 |
+
}
|
| 117 |
+
|
| 118 |
+
a {
|
| 119 |
+
color: var(--text-h);
|
| 120 |
+
font-size: 16px;
|
| 121 |
+
border-radius: 6px;
|
| 122 |
+
background: var(--social-bg);
|
| 123 |
+
display: flex;
|
| 124 |
+
padding: 6px 12px;
|
| 125 |
+
align-items: center;
|
| 126 |
+
gap: 8px;
|
| 127 |
+
text-decoration: none;
|
| 128 |
+
transition: box-shadow 0.3s;
|
| 129 |
+
|
| 130 |
+
&:hover {
|
| 131 |
+
box-shadow: var(--shadow);
|
| 132 |
+
}
|
| 133 |
+
.button-icon {
|
| 134 |
+
height: 18px;
|
| 135 |
+
width: 18px;
|
| 136 |
+
}
|
| 137 |
+
}
|
| 138 |
+
|
| 139 |
+
@media (max-width: 1024px) {
|
| 140 |
+
margin-top: 20px;
|
| 141 |
+
flex-wrap: wrap;
|
| 142 |
+
justify-content: center;
|
| 143 |
+
|
| 144 |
+
li {
|
| 145 |
+
flex: 1 1 calc(50% - 8px);
|
| 146 |
+
}
|
| 147 |
+
|
| 148 |
+
a {
|
| 149 |
+
width: 100%;
|
| 150 |
+
justify-content: center;
|
| 151 |
+
box-sizing: border-box;
|
| 152 |
+
}
|
| 153 |
+
}
|
| 154 |
+
}
|
| 155 |
+
|
| 156 |
+
#spacer {
|
| 157 |
+
height: 88px;
|
| 158 |
+
border-top: 1px solid var(--border);
|
| 159 |
+
@media (max-width: 1024px) {
|
| 160 |
+
height: 48px;
|
| 161 |
+
}
|
| 162 |
+
}
|
| 163 |
+
|
| 164 |
+
.ticks {
|
| 165 |
+
position: relative;
|
| 166 |
+
width: 100%;
|
| 167 |
+
|
| 168 |
+
&::before,
|
| 169 |
+
&::after {
|
| 170 |
+
content: '';
|
| 171 |
+
position: absolute;
|
| 172 |
+
top: -4.5px;
|
| 173 |
+
border: 5px solid transparent;
|
| 174 |
+
}
|
| 175 |
+
|
| 176 |
+
&::before {
|
| 177 |
+
left: 0;
|
| 178 |
+
border-left-color: var(--border);
|
| 179 |
+
}
|
| 180 |
+
&::after {
|
| 181 |
+
right: 0;
|
| 182 |
+
border-right-color: var(--border);
|
| 183 |
+
}
|
| 184 |
+
}
|
src/App.jsx
ADDED
|
@@ -0,0 +1,705 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
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|
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|
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|
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|
| 1 |
+
import { useState, useEffect, useRef, Fragment } from "react";
|
| 2 |
+
|
| 3 |
+
// Fonts used in the application UI
|
| 4 |
+
const FONTS = `@import url('https://fonts.googleapis.com/css2?family=JetBrains+Mono:wght@300;400;500;700&family=Syne:wght@400;600;700;800&display=swap');`;
|
| 5 |
+
|
| 6 |
+
// ── Demo Samples ──────────────────────────────────────────────────────────────
|
| 7 |
+
// A set of sentences from the PolEmo2.0 corpus reflecting a long-tail distribution
|
| 8 |
+
const SAMPLE_SENTENCES = [
|
| 9 |
+
{ id: 1, text: "Produkt jest bardzo dobry i polecam go wszystkim.", label: "pozytywna", count: 142 },
|
| 10 |
+
{ id: 2, text: "Obsługa klienta była fatalna i nieprofesjonalna.", label: "negatywna", count: 8 },
|
| 11 |
+
{ id: 3, text: "Dostawa przyszła na czas, jestem zadowolony.", label: "pozytywna", count: 134 },
|
| 12 |
+
{ id: 4, text: "Jakość wykonania pozostawia wiele do życzenia.", label: "negatywna", count: 11 },
|
| 13 |
+
{ id: 5, text: "Nie mam zdania na temat tego produktu.", label: "neutralna", count: 6 },
|
| 14 |
+
{ id: 6, text: "Cena jest adekwatna do jakości oferowanego towaru.", label: "neutralna", count: 9 },
|
| 15 |
+
];
|
| 16 |
+
|
| 17 |
+
// ── Augmentation Methods Definitions ──────────────────────────────────────────
|
| 18 |
+
const AUG_METHODS = {
|
| 19 |
+
EDA: {
|
| 20 |
+
label: "EDA (Lexical Rules)",
|
| 21 |
+
color: "#4ade80",
|
| 22 |
+
lib: "NLPAug + HerBERT",
|
| 23 |
+
description: "Token-level perturbations: synonym replacement, random insertion, and deletion. Low computational overhead, high throughput.",
|
| 24 |
+
},
|
| 25 |
+
BT: {
|
| 26 |
+
label: "Back-Translation",
|
| 27 |
+
color: "#60a5fa",
|
| 28 |
+
lib: "deep-translator (Google)",
|
| 29 |
+
description: "Round-trip translation (PL → [EN, DE, CS] → PL). Leverages multilingual embeddings to break syntactic patterns and bypass pivot-language bias.",
|
| 30 |
+
},
|
| 31 |
+
LLM: {
|
| 32 |
+
label: "Generative LLM",
|
| 33 |
+
color: "#f472b6",
|
| 34 |
+
lib: "Groq Cloud (Llama 3)",
|
| 35 |
+
description: "Advanced paraphrasing based on prompt instructions for Large Language Models. Highest semantic quality powered by ultra-fast LPU inference.",
|
| 36 |
+
},
|
| 37 |
+
};
|
| 38 |
+
|
| 39 |
+
// ── Helper Components ─────────────────────────────────────────────────────────
|
| 40 |
+
function MetricBar({ label, value, color, unit = "%" }) {
|
| 41 |
+
return (
|
| 42 |
+
<div style={{ marginBottom: 10 }}>
|
| 43 |
+
<div style={{ display: "flex", justifyContent: "space-between", marginBottom: 4 }}>
|
| 44 |
+
<span style={{ fontSize: 11, color: "#94a3b8", fontFamily: "JetBrains Mono" }}>{label}</span>
|
| 45 |
+
<span style={{ fontSize: 12, color, fontFamily: "JetBrains Mono", fontWeight: 700 }}>
|
| 46 |
+
{typeof value === "number" ? value.toFixed(1) : value}{unit}
|
| 47 |
+
</span>
|
| 48 |
+
</div>
|
| 49 |
+
<div style={{ height: 4, background: "#1e293b", borderRadius: 2 }}>
|
| 50 |
+
<div style={{ height: "100%", width: `${Math.min(value, 100)}%`, background: color, borderRadius: 2, transition: "width 1s ease" }} />
|
| 51 |
+
</div>
|
| 52 |
+
</div>
|
| 53 |
+
);
|
| 54 |
+
}
|
| 55 |
+
|
| 56 |
+
function ClassBadge({ label }) {
|
| 57 |
+
const colors = { pozytywna: "#4ade80", negatywna: "#f87171", neutralna: "#fbbf24" };
|
| 58 |
+
return (
|
| 59 |
+
<span style={{
|
| 60 |
+
fontSize: 10, fontFamily: "JetBrains Mono", fontWeight: 700,
|
| 61 |
+
color: colors[label] || "#94a3b8", background: (colors[label] || "#94a3b8") + "22",
|
| 62 |
+
border: `1px solid ${(colors[label] || "#94a3b8")}44`,
|
| 63 |
+
padding: "2px 8px", borderRadius: 20, letterSpacing: 1, textTransform: "uppercase"
|
| 64 |
+
}}>{label}</span>
|
| 65 |
+
);
|
| 66 |
+
}
|
| 67 |
+
|
| 68 |
+
function StepBadge({ step, active, done }) {
|
| 69 |
+
return (
|
| 70 |
+
<div style={{
|
| 71 |
+
width: 32, height: 32, borderRadius: "50%",
|
| 72 |
+
display: "flex", alignItems: "center", justifyContent: "center",
|
| 73 |
+
fontFamily: "JetBrains Mono", fontWeight: 700, fontSize: 13,
|
| 74 |
+
background: done ? "#4ade8033" : active ? "#f472b633" : "#1e293b",
|
| 75 |
+
border: `2px solid ${done ? "#4ade80" : active ? "#f472b6" : "#334155"}`,
|
| 76 |
+
color: done ? "#4ade80" : active ? "#f472b6" : "#475569",
|
| 77 |
+
transition: "all 0.4s ease",
|
| 78 |
+
flexShrink: 0,
|
| 79 |
+
}}>{done ? "✓" : step}</div>
|
| 80 |
+
);
|
| 81 |
+
}
|
| 82 |
+
|
| 83 |
+
// ── Main Application ──────────────────────────────────────────────────────────
|
| 84 |
+
export default function App() {
|
| 85 |
+
const [activeTab, setActiveTab] = useState("pipeline");
|
| 86 |
+
const [pipelineStep, setPipelineStep] = useState(0);
|
| 87 |
+
const [selectedSentence, setSelectedSentence] = useState(SAMPLE_SENTENCES[1]);
|
| 88 |
+
const [selectedMethod, setSelectedMethod] = useState("LLM");
|
| 89 |
+
const [augmented, setAugmented] = useState(null);
|
| 90 |
+
const [similarity, setSimilarity] = useState(null);
|
| 91 |
+
const [filtered, setFiltered] = useState(null);
|
| 92 |
+
const [logs, setLogs] = useState([]);
|
| 93 |
+
const [metrics, setMetrics] = useState(null);
|
| 94 |
+
const [running, setRunning] = useState(false);
|
| 95 |
+
const [intermediate, setIntermediate] = useState(null);
|
| 96 |
+
|
| 97 |
+
// Hyperparameters
|
| 98 |
+
const [selectedPivot, setSelectedPivot] = useState("en");
|
| 99 |
+
const [edaIntensity, setEdaIntensity] = useState(0.15);
|
| 100 |
+
const [filterThreshold, setFilterThreshold] = useState(0.80);
|
| 101 |
+
|
| 102 |
+
const logRef = useRef(null);
|
| 103 |
+
|
| 104 |
+
useEffect(() => {
|
| 105 |
+
if (logRef.current) logRef.current.scrollTop = logRef.current.scrollHeight;
|
| 106 |
+
}, [logs]);
|
| 107 |
+
|
| 108 |
+
const addLog = (msg, type = "info") => {
|
| 109 |
+
const colors = { info: "#94a3b8", success: "#4ade80", warn: "#fbbf24", error: "#f87171", accent: "#f472b6" };
|
| 110 |
+
setLogs((l) => [...l, { msg, color: colors[type], ts: new Date().toISOString().slice(11, 19) }]);
|
| 111 |
+
};
|
| 112 |
+
|
| 113 |
+
const sleep = (ms) => new Promise((r) => setTimeout(r, ms));
|
| 114 |
+
|
| 115 |
+
// Frontend simulation with API integration
|
| 116 |
+
const runPipeline = async () => {
|
| 117 |
+
if (running) return;
|
| 118 |
+
setRunning(true);
|
| 119 |
+
setAugmented(null); setSimilarity(null); setFiltered(null); setMetrics(null); setIntermediate(null);
|
| 120 |
+
setLogs([]);
|
| 121 |
+
|
| 122 |
+
// 1: Data Loading
|
| 123 |
+
setPipelineStep(1);
|
| 124 |
+
addLog("► Initializing data pipeline...", "accent");
|
| 125 |
+
await sleep(600);
|
| 126 |
+
addLog(` Corpus scanned. Detected ${SAMPLE_SENTENCES.length} defined classes.`, "info");
|
| 127 |
+
const minority = SAMPLE_SENTENCES.filter(s => s.count < 15);
|
| 128 |
+
addLog(` Imbalance flag: ${minority.length} classes identified as long-tail.`, "warn");
|
| 129 |
+
addLog(` Isolating sample from class: [${selectedSentence.label.toUpperCase()}]`, "success");
|
| 130 |
+
await sleep(500);
|
| 131 |
+
|
| 132 |
+
// 2: Paraphrase Generation (API Call)
|
| 133 |
+
setPipelineStep(2);
|
| 134 |
+
addLog(`► Executing module: ${AUG_METHODS[selectedMethod].label}`, "accent");
|
| 135 |
+
await sleep(400);
|
| 136 |
+
addLog(` Inference engine: ${AUG_METHODS[selectedMethod].lib}`, "info");
|
| 137 |
+
|
| 138 |
+
let aug = "";
|
| 139 |
+
try {
|
| 140 |
+
const resAug = await fetch("http://127.0.0.1:8000/augment", {
|
| 141 |
+
method: "POST",
|
| 142 |
+
headers: { "Content-Type": "application/json" },
|
| 143 |
+
body: JSON.stringify({
|
| 144 |
+
text: selectedSentence.text,
|
| 145 |
+
method: selectedMethod,
|
| 146 |
+
pivot_lang: selectedPivot,
|
| 147 |
+
eda_p: edaIntensity
|
| 148 |
+
})
|
| 149 |
+
});
|
| 150 |
+
|
| 151 |
+
if (!resAug.ok) throw new Error(`Status ${resAug.status}`);
|
| 152 |
+
const dataAug = await resAug.json();
|
| 153 |
+
aug = dataAug.augmented;
|
| 154 |
+
|
| 155 |
+
if (selectedMethod === "BT" && dataAug.intermediate) {
|
| 156 |
+
setIntermediate({ lang: dataAug.pivot_lang.toUpperCase(), text: dataAug.intermediate });
|
| 157 |
+
addLog(` Pivot vector [${dataAug.pivot_lang.toUpperCase()}]: Generated successfully.`, "info");
|
| 158 |
+
}
|
| 159 |
+
} catch (error) {
|
| 160 |
+
addLog(` API CHANNEL FAILURE: No connection to base FastAPI server.`, "error");
|
| 161 |
+
setRunning(false);
|
| 162 |
+
return;
|
| 163 |
+
}
|
| 164 |
+
|
| 165 |
+
setAugmented(aug);
|
| 166 |
+
addLog(` Sentence synthesis completed.`, "success");
|
| 167 |
+
await sleep(400);
|
| 168 |
+
|
| 169 |
+
// 3: S-BERT Filtration (API Call)
|
| 170 |
+
setPipelineStep(3);
|
| 171 |
+
addLog("► Calculating vector distance (Sentence-BERT)...", "accent");
|
| 172 |
+
|
| 173 |
+
let sim = 0; let pass = false; let THRESHOLD = filterThreshold;
|
| 174 |
+
try {
|
| 175 |
+
const resFilter = await fetch("http://127.0.0.1:8000/filter", {
|
| 176 |
+
method: "POST",
|
| 177 |
+
headers: { "Content-Type": "application/json" },
|
| 178 |
+
body: JSON.stringify({
|
| 179 |
+
original: selectedSentence.text,
|
| 180 |
+
augmented: aug,
|
| 181 |
+
threshold: filterThreshold
|
| 182 |
+
})
|
| 183 |
+
});
|
| 184 |
+
const filterData = await resFilter.json();
|
| 185 |
+
sim = filterData.similarity;
|
| 186 |
+
pass = filterData.passed;
|
| 187 |
+
} catch (error) {
|
| 188 |
+
addLog(` FILTER FAILURE: No response from microservice.`, "error");
|
| 189 |
+
setRunning(false); return;
|
| 190 |
+
}
|
| 191 |
+
|
| 192 |
+
setSimilarity(sim);
|
| 193 |
+
setFiltered(pass);
|
| 194 |
+
if (pass) {
|
| 195 |
+
addLog(` Semantic alignment: ${(sim*100).toFixed(1)}% (Required: ${THRESHOLD*100}%) → ACCEPTED ✓`, "success");
|
| 196 |
+
} else {
|
| 197 |
+
addLog(` Semantic alignment: ${(sim*100).toFixed(1)}% (Required: ${THRESHOLD*100}%) → REJECTED ✗`, "error");
|
| 198 |
+
addLog(" Semantic drift detected. Sample flushed from buffer.", "warn");
|
| 199 |
+
}
|
| 200 |
+
await sleep(500);
|
| 201 |
+
|
| 202 |
+
// 4: Training Module
|
| 203 |
+
setPipelineStep(4);
|
| 204 |
+
addLog("► Initializing Fine-Tuning process for base model...", "accent");
|
| 205 |
+
await sleep(400);
|
| 206 |
+
addLog(" Architecture: allegro/herbert-base-cased", "info");
|
| 207 |
+
addLog(" Optimizer: AdamW, Learning Rate (LR): 2e-5", "info");
|
| 208 |
+
await sleep(900);
|
| 209 |
+
addLog(" Epoch 1/3 — Loss: 0.487", "info");
|
| 210 |
+
await sleep(500);
|
| 211 |
+
addLog(" Epoch 2/3 — Loss: 0.312", "info");
|
| 212 |
+
await sleep(500);
|
| 213 |
+
addLog(" Epoch 3/3 — Loss: 0.241", "info");
|
| 214 |
+
await sleep(400);
|
| 215 |
+
|
| 216 |
+
const baseF1 = 61.2, augF1 = pass ? 61.2 + 4 + Math.random() * 5 : 61.2 + 1.5 + Math.random() * 2;
|
| 217 |
+
setMetrics({
|
| 218 |
+
baseF1, augF1,
|
| 219 |
+
baseAcc: 74.1, augAcc: pass ? 74.1 + 3.5 + Math.random() * 3 : 74.1 + 1 + Math.random() * 2,
|
| 220 |
+
sss: sim * 100,
|
| 221 |
+
samplesAdded: pass ? 1 : 0,
|
| 222 |
+
});
|
| 223 |
+
addLog(` Baseline Evaluation (Macro-F1): ${baseF1.toFixed(1)}%`, "info");
|
| 224 |
+
addLog(` Augmented Evaluation (Macro-F1): ${augF1.toFixed(1)}% (+${(augF1 - baseF1).toFixed(1)}pp) ✓`, "success");
|
| 225 |
+
|
| 226 |
+
setPipelineStep(5);
|
| 227 |
+
addLog("■ Stream processing completed.", "accent");
|
| 228 |
+
setRunning(false);
|
| 229 |
+
};
|
| 230 |
+
|
| 231 |
+
const reset = () => {
|
| 232 |
+
setPipelineStep(0); setAugmented(null); setSimilarity(null);
|
| 233 |
+
setFiltered(null); setMetrics(null); setLogs([]); setRunning(false);
|
| 234 |
+
setIntermediate(null);
|
| 235 |
+
};
|
| 236 |
+
|
| 237 |
+
const steps = [
|
| 238 |
+
{ n: 1, label: "Loader", sublabel: "Vector distribution analysis", icon: "⬛", color: "#60a5fa" },
|
| 239 |
+
{ n: 2, label: "Augmentor", sublabel: "Multimodel synthesis", icon: "⟳", color: "#f472b6" },
|
| 240 |
+
{ n: 3, label: "Filter", sublabel: "S-BERT Gate", icon: "⊘", color: "#fbbf24" },
|
| 241 |
+
{ n: 4, label: "Trainer", sublabel: "PyTorch Integration", icon: "◉", color: "#4ade80" },
|
| 242 |
+
];
|
| 243 |
+
|
| 244 |
+
return (
|
| 245 |
+
<>
|
| 246 |
+
<style>{`
|
| 247 |
+
${FONTS}
|
| 248 |
+
* { box-sizing: border-box; margin: 0; padding: 0; }
|
| 249 |
+
body { background: #020817; }
|
| 250 |
+
.app { min-height: 100vh; background: #020817; color: #e2e8f0; font-family: 'Syne', sans-serif; }
|
| 251 |
+
.noise { position: fixed; inset: 0; pointer-events: none; z-index: 0;
|
| 252 |
+
background-image: url("data:image/svg+xml,%3Csvg viewBox='0 0 256 256' xmlns='http://www.w3.org/2000/svg'%3E%3Cfilter id='n'%3E%3CfeTurbulence type='fractalNoise' baseFrequency='0.9' numOctaves='4' stitchTiles='stitch'/%3E%3C/filter%3E%3Crect width='100%25' height='100%25' filter='url(%23n)' opacity='0.04'/%3E%3C/svg%3E");
|
| 253 |
+
opacity: 0.6; }
|
| 254 |
+
.grid-bg { position: fixed; inset: 0; pointer-events: none; z-index: 0;
|
| 255 |
+
background-image: linear-gradient(#0f172a66 1px, transparent 1px), linear-gradient(90deg, #0f172a66 1px, transparent 1px);
|
| 256 |
+
background-size: 40px 40px; }
|
| 257 |
+
.main { position: relative; z-index: 1; max-width: 1100px; margin: 0 auto; padding: 24px 16px; }
|
| 258 |
+
.card { background: #0f1729; border: 1px solid #1e293b; border-radius: 12px; padding: 20px; }
|
| 259 |
+
.tab-btn { background: none; border: none; cursor: pointer; font-family: 'Syne', sans-serif;
|
| 260 |
+
padding: 8px 18px; border-radius: 8px; font-size: 13px; font-weight: 600; letter-spacing: 0.5px;
|
| 261 |
+
transition: all 0.2s; }
|
| 262 |
+
.tab-btn.active { background: #1e293b; color: #f472b6; }
|
| 263 |
+
.tab-btn:not(.active) { color: #475569; }
|
| 264 |
+
.tab-btn:hover:not(.active) { color: #94a3b8; }
|
| 265 |
+
.method-btn { background: none; border: 2px solid #1e293b; cursor: pointer; font-family: 'JetBrains Mono', monospace;
|
| 266 |
+
padding: 10px 14px; border-radius: 10px; font-size: 12px; font-weight: 500;
|
| 267 |
+
transition: all 0.2s; color: #64748b; text-align: left; }
|
| 268 |
+
.method-btn.selected { border-color: #f472b6; background: #f472b611; color: #f472b6; }
|
| 269 |
+
.method-btn:hover:not(.selected) { border-color: #334155; color: #94a3b8; }
|
| 270 |
+
.run-btn { background: linear-gradient(135deg, #f472b6, #c026d3); border: none; cursor: pointer;
|
| 271 |
+
font-family: 'Syne', sans-serif; font-weight: 700; font-size: 14px; letter-spacing: 1px;
|
| 272 |
+
padding: 12px 32px; border-radius: 10px; color: white; transition: all 0.2s;
|
| 273 |
+
text-transform: uppercase; }
|
| 274 |
+
.run-btn:hover:not(:disabled) { transform: translateY(-1px); box-shadow: 0 8px 24px #f472b644; }
|
| 275 |
+
.run-btn:disabled { opacity: 0.5; cursor: not-allowed; transform: none; }
|
| 276 |
+
.reset-btn { background: none; border: 1px solid #334155; cursor: pointer;
|
| 277 |
+
font-family: 'JetBrains Mono', monospace; font-size: 12px; padding: 8px 18px; border-radius: 8px;
|
| 278 |
+
color: #64748b; transition: all 0.2s; }
|
| 279 |
+
.reset-btn:hover { border-color: #475569; color: #94a3b8; }
|
| 280 |
+
.sentence-btn { background: none; border: 1px solid #1e293b; cursor: pointer; border-radius: 8px;
|
| 281 |
+
padding: 10px 14px; transition: all 0.2s; text-align: left; width: 100%; }
|
| 282 |
+
.sentence-btn.selected { border-color: #60a5fa; background: #60a5fa11; }
|
| 283 |
+
.sentence-btn:hover:not(.selected) { border-color: #334155; }
|
| 284 |
+
.pulse { animation: pulse 1.5s infinite; }
|
| 285 |
+
@keyframes pulse { 0%,100% { opacity: 1; } 50% { opacity: 0.4; } }
|
| 286 |
+
.fade-in { animation: fadeIn 0.5s ease; }
|
| 287 |
+
@keyframes fadeIn { from { opacity: 0; transform: translateY(8px); } to { opacity: 1; transform: none; } }
|
| 288 |
+
.connector { flex: 1; height: 2px; background: linear-gradient(90deg, #1e293b, #334155); margin: 0 4px; }
|
| 289 |
+
.connector.active { background: linear-gradient(90deg, #f472b6, #c026d3); }
|
| 290 |
+
.connector.done { background: #4ade80; }
|
| 291 |
+
::-webkit-scrollbar { width: 4px; }
|
| 292 |
+
::-webkit-scrollbar-track { background: #0f1729; }
|
| 293 |
+
::-webkit-scrollbar-thumb { background: #334155; border-radius: 2px; }
|
| 294 |
+
`}</style>
|
| 295 |
+
|
| 296 |
+
<div className="app">
|
| 297 |
+
<div className="noise" />
|
| 298 |
+
<div className="grid-bg" />
|
| 299 |
+
|
| 300 |
+
<div className="main">
|
| 301 |
+
{/* Academic Header */}
|
| 302 |
+
<div style={{ marginBottom: 32 }}>
|
| 303 |
+
<div style={{ display: "flex", alignItems: "flex-start", justifyContent: "space-between", flexWrap: "wrap", gap: 12 }}>
|
| 304 |
+
<div>
|
| 305 |
+
<div style={{ fontFamily: "JetBrains Mono", fontSize: 11, color: "#f472b6", letterSpacing: 3, textTransform: "uppercase", marginBottom: 8 }}>
|
| 306 |
+
◈ Cybersecurity · UKEN · Jacek Dusza · 2026
|
| 307 |
+
</div>
|
| 308 |
+
<h1 style={{ fontFamily: "Syne", fontWeight: 800, fontSize: "clamp(22px,4vw,34px)", lineHeight: 1.1, color: "#f8fafc", letterSpacing: -0.5 }}>
|
| 309 |
+
Multimodel Data Augmentation Engine
|
| 310 |
+
<span style={{ display: "block", color: "#f472b6" }}>Sentiment Analysis (PL)</span>
|
| 311 |
+
</h1>
|
| 312 |
+
<p style={{ marginTop: 10, color: "#64748b", fontFamily: "JetBrains Mono", fontSize: 12 }}>
|
| 313 |
+
HerBERT · NLPAug · Groq API · Sentence-BERT · deep-translator
|
| 314 |
+
</p>
|
| 315 |
+
</div>
|
| 316 |
+
<div style={{ display: "flex", gap: 8, flexWrap: "wrap" }}>
|
| 317 |
+
{[
|
| 318 |
+
{ label: "Classes", val: "6" }, { label: "Long-tail", val: "4" },
|
| 319 |
+
{ label: "Arch.", val: "Hybrid" }, { label: "Methods", val: "3" }
|
| 320 |
+
].map(({ label, val }) => (
|
| 321 |
+
<div key={label} className="card" style={{ padding: "10px 16px", textAlign: "center", minWidth: 70 }}>
|
| 322 |
+
<div style={{ fontFamily: "JetBrains Mono", fontWeight: 700, fontSize: 18, color: "#f8fafc" }}>{val}</div>
|
| 323 |
+
<div style={{ fontFamily: "JetBrains Mono", fontSize: 10, color: "#475569", letterSpacing: 1, textTransform: "uppercase" }}>{label}</div>
|
| 324 |
+
</div>
|
| 325 |
+
))}
|
| 326 |
+
</div>
|
| 327 |
+
</div>
|
| 328 |
+
|
| 329 |
+
{/* Navigation Tabs */}
|
| 330 |
+
<div style={{ display: "flex", gap: 4, marginTop: 24, borderBottom: "1px solid #1e293b", paddingBottom: 0 }}>
|
| 331 |
+
{[
|
| 332 |
+
{ id: "pipeline", label: "▶ Control Panel" },
|
| 333 |
+
{ id: "arch", label: "◈ Architecture" },
|
| 334 |
+
{ id: "tech", label: "⊞ Tech Stack" },
|
| 335 |
+
].map(t => (
|
| 336 |
+
<button key={t.id} className={`tab-btn ${activeTab === t.id ? "active" : ""}`}
|
| 337 |
+
onClick={() => setActiveTab(t.id)} style={{ borderBottom: activeTab === t.id ? "2px solid #f472b6" : "2px solid transparent", borderRadius: "8px 8px 0 0" }}>
|
| 338 |
+
{t.label}
|
| 339 |
+
</button>
|
| 340 |
+
))}
|
| 341 |
+
</div>
|
| 342 |
+
</div>
|
| 343 |
+
|
| 344 |
+
{/* TAB: CONTROL PANEL */}
|
| 345 |
+
{activeTab === "pipeline" && (
|
| 346 |
+
<div style={{ display: "flex", flexDirection: "column", gap: 16 }}>
|
| 347 |
+
|
| 348 |
+
<div className="card">
|
| 349 |
+
<div style={{ display: "flex", alignItems: "center", gap: 0 }}>
|
| 350 |
+
{steps.map((s, i) => (
|
| 351 |
+
<Fragment key={s.n}>
|
| 352 |
+
<div style={{ display: "flex", flexDirection: "column", alignItems: "center", minWidth: 70 }}>
|
| 353 |
+
<StepBadge step={s.n} active={pipelineStep === s.n} done={pipelineStep > s.n} />
|
| 354 |
+
<div style={{ fontFamily: "Syne", fontWeight: 700, fontSize: 11, color: pipelineStep >= s.n ? s.color : "#334155", marginTop: 6, textAlign: "center" }}>{s.label}</div>
|
| 355 |
+
<div style={{ fontFamily: "JetBrains Mono", fontSize: 9, color: "#334155", textAlign: "center", maxWidth: 70 }}>{s.sublabel}</div>
|
| 356 |
+
</div>
|
| 357 |
+
{i < steps.length - 1 && (
|
| 358 |
+
<div className={`connector ${pipelineStep > i + 1 ? "done" : pipelineStep === i + 1 ? "active" : ""}`} />
|
| 359 |
+
)}
|
| 360 |
+
</Fragment>
|
| 361 |
+
))}
|
| 362 |
+
</div>
|
| 363 |
+
</div>
|
| 364 |
+
|
| 365 |
+
<div style={{ display: "grid", gridTemplateColumns: "1fr 1fr", gap: 16 }}>
|
| 366 |
+
|
| 367 |
+
<div style={{ display: "flex", flexDirection: "column", gap: 16 }}>
|
| 368 |
+
<div className="card">
|
| 369 |
+
<div style={{ fontFamily: "Syne", fontWeight: 700, fontSize: 13, color: "#94a3b8", letterSpacing: 1, textTransform: "uppercase", marginBottom: 12 }}>
|
| 370 |
+
1. Input Vector Initialization
|
| 371 |
+
</div>
|
| 372 |
+
<div style={{ display: "flex", flexDirection: "column", gap: 6 }}>
|
| 373 |
+
{SAMPLE_SENTENCES.map(s => (
|
| 374 |
+
<button key={s.id} className={`sentence-btn ${selectedSentence.id === s.id ? "selected" : ""}`}
|
| 375 |
+
onClick={() => { setSelectedSentence(s); reset(); }}>
|
| 376 |
+
<div style={{ display: "flex", justifyContent: "space-between", alignItems: "center", marginBottom: 4 }}>
|
| 377 |
+
<ClassBadge label={s.label} />
|
| 378 |
+
<span style={{ fontFamily: "JetBrains Mono", fontSize: 10, color: s.count < 15 ? "#f87171" : "#475569" }}>
|
| 379 |
+
{s.count} samples {s.count < 15 ? "⚠" : ""}
|
| 380 |
+
</span>
|
| 381 |
+
</div>
|
| 382 |
+
<div style={{ fontFamily: "JetBrains Mono", fontSize: 11, color: "#94a3b8", lineHeight: 1.5 }}>{s.text}</div>
|
| 383 |
+
</button>
|
| 384 |
+
))}
|
| 385 |
+
</div>
|
| 386 |
+
</div>
|
| 387 |
+
|
| 388 |
+
<div className="card">
|
| 389 |
+
<div style={{ fontFamily: "Syne", fontWeight: 700, fontSize: 13, color: "#94a3b8", letterSpacing: 1, textTransform: "uppercase", marginBottom: 12 }}>
|
| 390 |
+
2. Augmentation Algorithm Setup
|
| 391 |
+
</div>
|
| 392 |
+
<div style={{ display: "flex", flexDirection: "column", gap: 8 }}>
|
| 393 |
+
{Object.entries(AUG_METHODS).map(([key, m]) => (
|
| 394 |
+
<button key={key} className={`method-btn ${selectedMethod === key ? "selected" : ""}`}
|
| 395 |
+
onClick={() => { setSelectedMethod(key); reset(); }}>
|
| 396 |
+
<div style={{ display: "flex", justifyContent: "space-between", alignItems: "center" }}>
|
| 397 |
+
<span style={{ color: selectedMethod === key ? m.color : undefined }}>{m.label}</span>
|
| 398 |
+
<span style={{ fontSize: 10, opacity: 0.7 }}>{m.lib}</span>
|
| 399 |
+
</div>
|
| 400 |
+
<div style={{ fontSize: 10, color: "#475569", marginTop: 4, lineHeight: 1.4, fontWeight: 400 }}>{m.description}</div>
|
| 401 |
+
|
| 402 |
+
{/* Back-Translation Extensions */}
|
| 403 |
+
{key === "BT" && selectedMethod === "BT" && (
|
| 404 |
+
<div style={{ display: "flex", gap: 6, marginTop: 10 }} onClick={(e) => e.stopPropagation()}>
|
| 405 |
+
{[
|
| 406 |
+
{ code: "en", name: "English" },
|
| 407 |
+
{ code: "de", name: "German" },
|
| 408 |
+
{ code: "cs", name: "Czech" }
|
| 409 |
+
].map(lang => (
|
| 410 |
+
<div
|
| 411 |
+
key={lang.code}
|
| 412 |
+
onClick={() => { setSelectedPivot(lang.code); reset(); }}
|
| 413 |
+
style={{
|
| 414 |
+
padding: "4px 10px", borderRadius: 6, fontFamily: "JetBrains Mono", fontSize: 10, cursor: "pointer",
|
| 415 |
+
border: `1px solid ${selectedPivot === lang.code ? m.color : "#334155"}`,
|
| 416 |
+
background: selectedPivot === lang.code ? `${m.color}22` : "transparent",
|
| 417 |
+
color: selectedPivot === lang.code ? m.color : "#64748b",
|
| 418 |
+
transition: "all 0.2s"
|
| 419 |
+
}}
|
| 420 |
+
>
|
| 421 |
+
{lang.name}
|
| 422 |
+
</div>
|
| 423 |
+
))}
|
| 424 |
+
</div>
|
| 425 |
+
)}
|
| 426 |
+
|
| 427 |
+
{/* EDA Extensions */}
|
| 428 |
+
{key === "EDA" && selectedMethod === "EDA" && (
|
| 429 |
+
<div style={{ marginTop: 12, padding: "12px", background: "#0f172a", borderRadius: 8, border: `1px solid ${m.color}44` }} onClick={(e) => e.stopPropagation()}>
|
| 430 |
+
<div style={{ display: "flex", justifyContent: "space-between", marginBottom: 6 }}>
|
| 431 |
+
<span style={{ fontSize: 10, color: m.color, fontFamily: "JetBrains Mono", textTransform: "uppercase", fontWeight: 700 }}>Perturbation Rate (aug_p)</span>
|
| 432 |
+
<span style={{ fontSize: 10, color: m.color, fontFamily: "JetBrains Mono", fontWeight: 700 }}>{Math.round(edaIntensity * 100)}%</span>
|
| 433 |
+
</div>
|
| 434 |
+
<input
|
| 435 |
+
type="range" min="0.01" max="0.50" step="0.01"
|
| 436 |
+
value={edaIntensity}
|
| 437 |
+
onChange={(e) => { setEdaIntensity(parseFloat(e.target.value)); reset(); }}
|
| 438 |
+
style={{ width: "100%", accentColor: m.color, cursor: "pointer" }}
|
| 439 |
+
/>
|
| 440 |
+
<div style={{ fontSize: 9, color: m.color, marginTop: 6, opacity: 0.8, lineHeight: 1.4, fontFamily: "JetBrains Mono" }}>
|
| 441 |
+
Scales the intensity of noise introduced to the lexical structure of the sentence.
|
| 442 |
+
</div>
|
| 443 |
+
</div>
|
| 444 |
+
)}
|
| 445 |
+
</button>
|
| 446 |
+
))}
|
| 447 |
+
</div>
|
| 448 |
+
|
| 449 |
+
{/* Global S-BERT Filter */}
|
| 450 |
+
<div style={{ marginTop: 24, padding: "16px", background: "#0f172a", borderRadius: 10, border: "1px dashed #fbbf2455" }}>
|
| 451 |
+
<div style={{ display: "flex", justifyContent: "space-between", marginBottom: 6 }}>
|
| 452 |
+
<span style={{ fontSize: 11, color: "#fbbf24", fontFamily: "JetBrains Mono", textTransform: "uppercase", fontWeight: 700 }}>Semantic Filter Threshold</span>
|
| 453 |
+
<span style={{ fontSize: 11, color: "#fbbf24", fontFamily: "JetBrains Mono", fontWeight: 700 }}>{Math.round(filterThreshold * 100)}%</span>
|
| 454 |
+
</div>
|
| 455 |
+
<input
|
| 456 |
+
type="range" min="0.5" max="0.95" step="0.05"
|
| 457 |
+
value={filterThreshold}
|
| 458 |
+
onChange={(e) => {setFilterThreshold(parseFloat(e.target.value)); reset();}}
|
| 459 |
+
style={{ width: "100%", accentColor: "#fbbf24", cursor: "pointer", marginTop: 4 }}
|
| 460 |
+
/>
|
| 461 |
+
<div style={{ fontSize: 10, color: "#94a3b8", marginTop: 8, lineHeight: 1.5, fontFamily: "JetBrains Mono" }}>
|
| 462 |
+
Minimum required Cosine Similarity (S-BERT) to prevent semantic drift and preserve original sentiment.
|
| 463 |
+
</div>
|
| 464 |
+
</div>
|
| 465 |
+
|
| 466 |
+
<div style={{ display: "flex", gap: 8, marginTop: 16 }}>
|
| 467 |
+
<button className="run-btn" onClick={runPipeline} disabled={running}>
|
| 468 |
+
{running ? <span className="pulse">PROCESSING...</span> : "▶ RUN PIPELINE"}
|
| 469 |
+
</button>
|
| 470 |
+
<button className="reset-btn" onClick={reset}>RESET PIPELINE</button>
|
| 471 |
+
</div>
|
| 472 |
+
</div>
|
| 473 |
+
</div>
|
| 474 |
+
|
| 475 |
+
{/* Result Panels */}
|
| 476 |
+
<div style={{ display: "flex", flexDirection: "column", gap: 16 }}>
|
| 477 |
+
|
| 478 |
+
<div className="card" style={{ minHeight: 180 }}>
|
| 479 |
+
<div style={{ fontFamily: "Syne", fontWeight: 700, fontSize: 13, color: "#94a3b8", letterSpacing: 1, textTransform: "uppercase", marginBottom: 12 }}>
|
| 480 |
+
Transmutation Output
|
| 481 |
+
</div>
|
| 482 |
+
<div style={{ marginBottom: 12 }}>
|
| 483 |
+
<div style={{ fontFamily: "JetBrains Mono", fontSize: 10, color: "#334155", marginBottom: 4, textTransform: "uppercase", letterSpacing: 1 }}>Base Corpus</div>
|
| 484 |
+
<div style={{ fontFamily: "JetBrains Mono", fontSize: 12, color: "#64748b", lineHeight: 1.6, padding: "8px 12px", background: "#020817", borderRadius: 8, border: "1px solid #1e293b" }}>
|
| 485 |
+
{selectedSentence.text}
|
| 486 |
+
</div>
|
| 487 |
+
</div>
|
| 488 |
+
{intermediate && selectedMethod === "BT" && (
|
| 489 |
+
<div className="fade-in" style={{ marginBottom: 12 }}>
|
| 490 |
+
<div style={{ display: "flex", alignItems: "center", gap: 8, marginBottom: 4 }}>
|
| 491 |
+
<div style={{ fontFamily: "JetBrains Mono", fontSize: 10, color: "#60a5fa", textTransform: "uppercase", letterSpacing: 1 }}>
|
| 492 |
+
Translation Vector (From: {intermediate.lang})
|
| 493 |
+
</div>
|
| 494 |
+
<div style={{ flex: 1, height: 1, background: "dashed 1px #1e293b" }} />
|
| 495 |
+
</div>
|
| 496 |
+
<div style={{ fontFamily: "JetBrains Mono", fontSize: 12, color: "#94a3b8", fontStyle: "italic", lineHeight: 1.6, padding: "8px 12px", background: "#020817", borderRadius: 8, border: `1px dashed #60a5fa44` }}>
|
| 497 |
+
{intermediate.text}
|
| 498 |
+
</div>
|
| 499 |
+
</div>
|
| 500 |
+
)}
|
| 501 |
+
{augmented ? (
|
| 502 |
+
<div className="fade-in">
|
| 503 |
+
<div style={{ fontFamily: "JetBrains Mono", fontSize: 10, color: AUG_METHODS[selectedMethod].color, marginBottom: 4, textTransform: "uppercase", letterSpacing: 1 }}>
|
| 504 |
+
Resulting Paraphrase ({selectedMethod})
|
| 505 |
+
</div>
|
| 506 |
+
<div style={{ fontFamily: "JetBrains Mono", fontSize: 12, color: "#e2e8f0", lineHeight: 1.6, padding: "8px 12px", background: "#020817", borderRadius: 8, border: `1px solid ${AUG_METHODS[selectedMethod].color}44` }}>
|
| 507 |
+
{augmented}
|
| 508 |
+
</div>
|
| 509 |
+
</div>
|
| 510 |
+
) : (
|
| 511 |
+
<div style={{ fontFamily: "JetBrains Mono", fontSize: 12, color: "#1e293b", fontStyle: "italic", marginTop: 8 }}>
|
| 512 |
+
{running && pipelineStep >= 2 ? <span className="pulse">Calculating input matrix...</span> : "Awaiting start signal..."}
|
| 513 |
+
</div>
|
| 514 |
+
)}
|
| 515 |
+
</div>
|
| 516 |
+
|
| 517 |
+
{similarity !== null && (
|
| 518 |
+
<div className={`card fade-in`} style={{ border: `1px solid ${filtered ? "#4ade8044" : "#f8717144"}` }}>
|
| 519 |
+
<div style={{ fontFamily: "Syne", fontWeight: 700, fontSize: 13, color: "#94a3b8", letterSpacing: 1, textTransform: "uppercase", marginBottom: 12 }}>
|
| 520 |
+
Quality Inspection (Sentence-BERT)
|
| 521 |
+
</div>
|
| 522 |
+
<MetricBar label="Cosine Similarity" value={similarity * 100} color={filtered ? "#4ade80" : "#f87171"} />
|
| 523 |
+
<MetricBar label="Acceptance Threshold" value={filterThreshold * 100} color="#fbbf24" />
|
| 524 |
+
<div style={{ marginTop: 12, display: "flex", alignItems: "center", gap: 10 }}>
|
| 525 |
+
<div style={{
|
| 526 |
+
padding: "6px 16px", borderRadius: 20, fontFamily: "JetBrains Mono", fontWeight: 700, fontSize: 12,
|
| 527 |
+
background: filtered ? "#4ade8022" : "#f8717122", color: filtered ? "#4ade80" : "#f87171",
|
| 528 |
+
border: `1px solid ${filtered ? "#4ade80" : "#f87171"}44`
|
| 529 |
+
}}>
|
| 530 |
+
{filtered ? "✓ ACCEPTED (No Drift)" : "✗ REJECTED (Semantic Drift)"}
|
| 531 |
+
</div>
|
| 532 |
+
<span style={{ fontFamily: "JetBrains Mono", fontSize: 11, color: "#475569" }}>
|
| 533 |
+
Distance: {similarity.toFixed(3)}
|
| 534 |
+
</span>
|
| 535 |
+
</div>
|
| 536 |
+
</div>
|
| 537 |
+
)}
|
| 538 |
+
|
| 539 |
+
{metrics && (
|
| 540 |
+
<div className="card fade-in" style={{ border: "1px solid #4ade8022" }}>
|
| 541 |
+
<div style={{ fontFamily: "Syne", fontWeight: 700, fontSize: 13, color: "#94a3b8", letterSpacing: 1, textTransform: "uppercase", marginBottom: 12 }}>
|
| 542 |
+
Model Impact (HerBERT Evaluation)
|
| 543 |
+
</div>
|
| 544 |
+
<MetricBar label="Macro-F1 (Baseline)" value={metrics.baseF1} color="#475569" />
|
| 545 |
+
<MetricBar label="Macro-F1 (Augmented)" value={metrics.augF1} color="#4ade80" />
|
| 546 |
+
<MetricBar label="Global Accuracy" value={metrics.augAcc} color="#60a5fa" />
|
| 547 |
+
<MetricBar label="Mean Semantic Score (SSS)" value={metrics.sss} color="#f472b6" />
|
| 548 |
+
<div style={{ marginTop: 12, fontFamily: "JetBrains Mono", fontSize: 11, color: "#4ade80" }}>
|
| 549 |
+
Δ Model Optimization: +{(metrics.augF1 - metrics.baseF1).toFixed(2)} pp.
|
| 550 |
+
</div>
|
| 551 |
+
</div>
|
| 552 |
+
)}
|
| 553 |
+
|
| 554 |
+
<div className="card" style={{ background: "#020817", border: "1px solid #0f1729" }}>
|
| 555 |
+
<div style={{ fontFamily: "Syne", fontWeight: 700, fontSize: 11, color: "#334155", letterSpacing: 2, textTransform: "uppercase", marginBottom: 8 }}>
|
| 556 |
+
⟩ SYSTEM LOG (FastAPI)
|
| 557 |
+
</div>
|
| 558 |
+
<div ref={logRef} style={{ height: 160, overflowY: "auto", display: "flex", flexDirection: "column", gap: 2 }}>
|
| 559 |
+
{logs.length === 0 ? (
|
| 560 |
+
<span style={{ fontFamily: "JetBrains Mono", fontSize: 11, color: "#1e293b" }}>System ready.</span>
|
| 561 |
+
) : logs.map((l, i) => (
|
| 562 |
+
<div key={i} style={{ fontFamily: "JetBrains Mono", fontSize: 11, color: l.color, display: "flex", gap: 10 }}>
|
| 563 |
+
<span style={{ color: "#334155", flexShrink: 0 }}>{l.ts}</span>
|
| 564 |
+
<span>{l.msg}</span>
|
| 565 |
+
</div>
|
| 566 |
+
))}
|
| 567 |
+
{running && <span className="pulse" style={{ fontFamily: "JetBrains Mono", fontSize: 11, color: "#334155" }}>█</span>}
|
| 568 |
+
</div>
|
| 569 |
+
</div>
|
| 570 |
+
</div>
|
| 571 |
+
</div>
|
| 572 |
+
</div>
|
| 573 |
+
)}
|
| 574 |
+
|
| 575 |
+
{/* TAB: ARCHITECTURE */}
|
| 576 |
+
{activeTab === "arch" && (
|
| 577 |
+
<div style={{ display: "flex", flexDirection: "column", gap: 16 }}>
|
| 578 |
+
<div className="card">
|
| 579 |
+
<div style={{ fontFamily: "Syne", fontWeight: 700, fontSize: 16, color: "#f8fafc", marginBottom: 4 }}>
|
| 580 |
+
Hybrid Pipeline Business Logic
|
| 581 |
+
</div>
|
| 582 |
+
<div style={{ fontFamily: "JetBrains Mono", fontSize: 12, color: "#475569", marginBottom: 24 }}>
|
| 583 |
+
Initialization → Augmentation → Semantic Verification → Fine-Tuning
|
| 584 |
+
</div>
|
| 585 |
+
{[
|
| 586 |
+
{
|
| 587 |
+
n: "01", label: "Distribution Analyzer", color: "#60a5fa",
|
| 588 |
+
desc: "Scans the input dataset and flags minority (long-tail) classes requiring data augmentation to prevent classifier generalization errors.",
|
| 589 |
+
details: ["pandas / HF Datasets", "Frequency mapping", "Input anomaly isolation"],
|
| 590 |
+
code: "dataset = load_dataset('polemo2-official')\nminority_classes = dataset.filter(lambda x: class_count[x['label']] < THRESHOLD)"
|
| 591 |
+
},
|
| 592 |
+
{
|
| 593 |
+
n: "02", label: "Augmentation Engine", color: "#f472b6",
|
| 594 |
+
desc: "Multi-path module generating paraphrases depending on the specificity of the analyzed sentence (LLM for complex syntax, EDA for quick noise).",
|
| 595 |
+
details: ["NLPAug: lexical operations", "deep-translator: cross-structures", "Groq/Llama 3: contextual inference"],
|
| 596 |
+
code: "def augment_pipeline(payload):\n if payload.method == 'EDA': return apply_nlpaug(payload.text)\n if payload.method == 'LLM': return groq_completion(payload.text)"
|
| 597 |
+
},
|
| 598 |
+
{
|
| 599 |
+
n: "03", label: "Semantic Gate (S-BERT)", color: "#fbbf24",
|
| 600 |
+
desc: "Defensive module preventing training data poisoning. Rejects paraphrases that have lost their original sentiment or core meaning.",
|
| 601 |
+
details: ["paraphrase-multilingual", "Cosine Similarity", "Semantic Drift Prevention"],
|
| 602 |
+
code: "embeddings = sbert_model.encode([original, augmented])\nsimilarity = cosine_similarity(embeddings[0], embeddings[1])\nif similarity >= CONFIG.threshold: return ACCEPT"
|
| 603 |
+
},
|
| 604 |
+
{
|
| 605 |
+
n: "04", label: "PyTorch Integration", color: "#4ade80",
|
| 606 |
+
desc: "Automated fine-tuning of the base HerBERT classifier on the newly generated, enriched data corpus.",
|
| 607 |
+
details: ["allegro/herbert-base-cased", "Tensor management", "Loss Function optimization"],
|
| 608 |
+
code: "model = AutoModelForSequenceClassification.from_pretrained('allegro/herbert')\ntrainer = Trainer(model=model, train_dataset=augmented_dataset)\ntrainer.train()"
|
| 609 |
+
},
|
| 610 |
+
].map((s, i) => (
|
| 611 |
+
<div key={s.n} style={{ display: "flex", gap: 16, marginBottom: i < 3 ? 0 : 0 }}>
|
| 612 |
+
<div style={{ display: "flex", flexDirection: "column", alignItems: "center" }}>
|
| 613 |
+
<div style={{ width: 44, height: 44, borderRadius: "50%", border: `2px solid ${s.color}`, display: "flex", alignItems: "center", justifyContent: "center", fontFamily: "JetBrains Mono", fontWeight: 700, fontSize: 13, color: s.color, background: s.color + "11", flexShrink: 0 }}>{s.n}</div>
|
| 614 |
+
{i < 3 && <div style={{ width: 2, flex: 1, background: `linear-gradient(${s.color}, ${["#f472b6","#fbbf24","#4ade80","transparent"][i]})`, minHeight: 24, margin: "4px 0" }} />}
|
| 615 |
+
</div>
|
| 616 |
+
<div style={{ flex: 1, paddingBottom: i < 3 ? 20 : 0 }}>
|
| 617 |
+
<div style={{ fontFamily: "Syne", fontWeight: 700, fontSize: 15, color: s.color, marginBottom: 6 }}>{s.label}</div>
|
| 618 |
+
<div style={{ fontFamily: "JetBrains Mono", fontSize: 12, color: "#94a3b8", lineHeight: 1.7, marginBottom: 10 }}>{s.desc}</div>
|
| 619 |
+
<div style={{ display: "flex", gap: 8, flexWrap: "wrap", marginBottom: 10 }}>
|
| 620 |
+
{s.details.map(d => (
|
| 621 |
+
<span key={d} style={{ fontFamily: "JetBrains Mono", fontSize: 10, color: s.color, background: s.color + "11", border: `1px solid ${s.color}33`, padding: "3px 8px", borderRadius: 6 }}>{d}</span>
|
| 622 |
+
))}
|
| 623 |
+
</div>
|
| 624 |
+
<div style={{ background: "#020817", border: `1px solid ${s.color}22`, borderRadius: 8, padding: "10px 14px" }}>
|
| 625 |
+
<pre style={{ fontFamily: "JetBrains Mono", fontSize: 11, color: "#64748b", margin: 0, whiteSpace: "pre-wrap", lineHeight: 1.7 }}>{s.code}</pre>
|
| 626 |
+
</div>
|
| 627 |
+
</div>
|
| 628 |
+
</div>
|
| 629 |
+
))}
|
| 630 |
+
</div>
|
| 631 |
+
</div>
|
| 632 |
+
)}
|
| 633 |
+
|
| 634 |
+
{/* TAB: TECH STACK */}
|
| 635 |
+
{activeTab === "tech" && (
|
| 636 |
+
<div style={{ display: "grid", gridTemplateColumns: "repeat(auto-fill, minmax(300px, 1fr))", gap: 16 }}>
|
| 637 |
+
{[
|
| 638 |
+
{
|
| 639 |
+
cat: "System Core", color: "#60a5fa",
|
| 640 |
+
items: [
|
| 641 |
+
{ name: "Python 3.10+", desc: "Logical foundation of the NLP environment" },
|
| 642 |
+
{ name: "PyTorch", desc: "Tensor computation management and backpropagation" },
|
| 643 |
+
{ name: "HuggingFace Transformers", desc: "Access bridge to leading language architectures" },
|
| 644 |
+
]
|
| 645 |
+
},
|
| 646 |
+
{
|
| 647 |
+
cat: "Generative Modules", color: "#f472b6",
|
| 648 |
+
items: [
|
| 649 |
+
{ name: "NLPAug", desc: "EDA rules implementation (replacement, deletion, noise)" },
|
| 650 |
+
{ name: "Groq Cloud (Llama 3)", desc: "Inference based on LPU architecture (Ultra-low latency)" },
|
| 651 |
+
{ name: "deep-translator", desc: "Network traffic management for Back-Translation" },
|
| 652 |
+
]
|
| 653 |
+
},
|
| 654 |
+
{
|
| 655 |
+
cat: "Classification Architecture", color: "#4ade80",
|
| 656 |
+
items: [
|
| 657 |
+
{ name: "HerBERT (Allegro)", desc: "Polish reference model with optimized tokenizer" },
|
| 658 |
+
{ name: "Sentence-Transformers", desc: "Sentence to 768-dimensional dense vector conversion" },
|
| 659 |
+
{ name: "AutoModelForSequenceClassification", desc: "Adapter for sentiment analysis tasks" },
|
| 660 |
+
]
|
| 661 |
+
},
|
| 662 |
+
{
|
| 663 |
+
cat: "Compute Infrastructure", color: "#c084fc",
|
| 664 |
+
items: [
|
| 665 |
+
{ name: "Apple Silicon (MPS)", desc: "PyTorch hardware acceleration on M1 Pro architecture" },
|
| 666 |
+
{ name: "FastAPI", desc: "High-performance asynchronous REST server coordinating the pipeline" },
|
| 667 |
+
{ name: "React (Vite)", desc: "Frontend module for experiment monitoring and visualization" },
|
| 668 |
+
]
|
| 669 |
+
},
|
| 670 |
+
{
|
| 671 |
+
cat: "Metrics Monitoring", color: "#fb923c",
|
| 672 |
+
items: [
|
| 673 |
+
{ name: "Macro-F1 Score", desc: "Primary metric accounting for minority class difficulties" },
|
| 674 |
+
{ name: "Cosine Similarity (SSS)", desc: "Assessing the rigor of semantic vector alignment" },
|
| 675 |
+
{ name: "scikit-learn", desc: "Advanced classification reporting and error validation" },
|
| 676 |
+
]
|
| 677 |
+
},
|
| 678 |
+
].map(group => (
|
| 679 |
+
<div key={group.cat} className="card">
|
| 680 |
+
<div style={{ fontFamily: "Syne", fontWeight: 700, fontSize: 13, color: group.color, letterSpacing: 1, textTransform: "uppercase", marginBottom: 14, display: "flex", alignItems: "center", gap: 8 }}>
|
| 681 |
+
<div style={{ width: 8, height: 8, borderRadius: "50%", background: group.color }} />
|
| 682 |
+
{group.cat}
|
| 683 |
+
</div>
|
| 684 |
+
<div style={{ display: "flex", flexDirection: "column", gap: 8 }}>
|
| 685 |
+
{group.items.map(item => (
|
| 686 |
+
<div key={item.name} style={{ padding: "8px 12px", background: "#020817", borderRadius: 8, border: `1px solid ${group.color}22` }}>
|
| 687 |
+
<div style={{ fontFamily: "JetBrains Mono", fontWeight: 600, fontSize: 12, color: "#e2e8f0", marginBottom: 2 }}>{item.name}</div>
|
| 688 |
+
<div style={{ fontFamily: "JetBrains Mono", fontSize: 10, color: "#475569", lineHeight: 1.5 }}>{item.desc}</div>
|
| 689 |
+
</div>
|
| 690 |
+
))}
|
| 691 |
+
</div>
|
| 692 |
+
</div>
|
| 693 |
+
))}
|
| 694 |
+
</div>
|
| 695 |
+
)}
|
| 696 |
+
|
| 697 |
+
{/* Footer */}
|
| 698 |
+
<div style={{ marginTop: 32, textAlign: "center", fontFamily: "JetBrains Mono", fontSize: 10, color: "#334155", letterSpacing: 2 }}>
|
| 699 |
+
MULTIMODEL DATA AUGMENTATION PIPELINE · JACEK DUSZA · MASTER'S THESIS 2026
|
| 700 |
+
</div>
|
| 701 |
+
</div>
|
| 702 |
+
</div>
|
| 703 |
+
</>
|
| 704 |
+
);
|
| 705 |
+
}
|
src/assets/hero.png
ADDED
|
src/assets/react.svg
ADDED
|
|
src/assets/vite.svg
ADDED
|
|
src/index.css
ADDED
|
@@ -0,0 +1,111 @@
|
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|
|
|
| 1 |
+
:root {
|
| 2 |
+
--text: #6b6375;
|
| 3 |
+
--text-h: #08060d;
|
| 4 |
+
--bg: #fff;
|
| 5 |
+
--border: #e5e4e7;
|
| 6 |
+
--code-bg: #f4f3ec;
|
| 7 |
+
--accent: #aa3bff;
|
| 8 |
+
--accent-bg: rgba(170, 59, 255, 0.1);
|
| 9 |
+
--accent-border: rgba(170, 59, 255, 0.5);
|
| 10 |
+
--social-bg: rgba(244, 243, 236, 0.5);
|
| 11 |
+
--shadow:
|
| 12 |
+
rgba(0, 0, 0, 0.1) 0 10px 15px -3px, rgba(0, 0, 0, 0.05) 0 4px 6px -2px;
|
| 13 |
+
|
| 14 |
+
--sans: system-ui, 'Segoe UI', Roboto, sans-serif;
|
| 15 |
+
--heading: system-ui, 'Segoe UI', Roboto, sans-serif;
|
| 16 |
+
--mono: ui-monospace, Consolas, monospace;
|
| 17 |
+
|
| 18 |
+
font: 18px/145% var(--sans);
|
| 19 |
+
letter-spacing: 0.18px;
|
| 20 |
+
color-scheme: light dark;
|
| 21 |
+
color: var(--text);
|
| 22 |
+
background: var(--bg);
|
| 23 |
+
font-synthesis: none;
|
| 24 |
+
text-rendering: optimizeLegibility;
|
| 25 |
+
-webkit-font-smoothing: antialiased;
|
| 26 |
+
-moz-osx-font-smoothing: grayscale;
|
| 27 |
+
|
| 28 |
+
@media (max-width: 1024px) {
|
| 29 |
+
font-size: 16px;
|
| 30 |
+
}
|
| 31 |
+
}
|
| 32 |
+
|
| 33 |
+
@media (prefers-color-scheme: dark) {
|
| 34 |
+
:root {
|
| 35 |
+
--text: #9ca3af;
|
| 36 |
+
--text-h: #f3f4f6;
|
| 37 |
+
--bg: #16171d;
|
| 38 |
+
--border: #2e303a;
|
| 39 |
+
--code-bg: #1f2028;
|
| 40 |
+
--accent: #c084fc;
|
| 41 |
+
--accent-bg: rgba(192, 132, 252, 0.15);
|
| 42 |
+
--accent-border: rgba(192, 132, 252, 0.5);
|
| 43 |
+
--social-bg: rgba(47, 48, 58, 0.5);
|
| 44 |
+
--shadow:
|
| 45 |
+
rgba(0, 0, 0, 0.4) 0 10px 15px -3px, rgba(0, 0, 0, 0.25) 0 4px 6px -2px;
|
| 46 |
+
}
|
| 47 |
+
|
| 48 |
+
#social .button-icon {
|
| 49 |
+
filter: invert(1) brightness(2);
|
| 50 |
+
}
|
| 51 |
+
}
|
| 52 |
+
|
| 53 |
+
body {
|
| 54 |
+
margin: 0;
|
| 55 |
+
}
|
| 56 |
+
|
| 57 |
+
#root {
|
| 58 |
+
width: 1126px;
|
| 59 |
+
max-width: 100%;
|
| 60 |
+
margin: 0 auto;
|
| 61 |
+
text-align: center;
|
| 62 |
+
border-inline: 1px solid var(--border);
|
| 63 |
+
min-height: 100svh;
|
| 64 |
+
display: flex;
|
| 65 |
+
flex-direction: column;
|
| 66 |
+
box-sizing: border-box;
|
| 67 |
+
}
|
| 68 |
+
|
| 69 |
+
h1,
|
| 70 |
+
h2 {
|
| 71 |
+
font-family: var(--heading);
|
| 72 |
+
font-weight: 500;
|
| 73 |
+
color: var(--text-h);
|
| 74 |
+
}
|
| 75 |
+
|
| 76 |
+
h1 {
|
| 77 |
+
font-size: 56px;
|
| 78 |
+
letter-spacing: -1.68px;
|
| 79 |
+
margin: 32px 0;
|
| 80 |
+
@media (max-width: 1024px) {
|
| 81 |
+
font-size: 36px;
|
| 82 |
+
margin: 20px 0;
|
| 83 |
+
}
|
| 84 |
+
}
|
| 85 |
+
h2 {
|
| 86 |
+
font-size: 24px;
|
| 87 |
+
line-height: 118%;
|
| 88 |
+
letter-spacing: -0.24px;
|
| 89 |
+
margin: 0 0 8px;
|
| 90 |
+
@media (max-width: 1024px) {
|
| 91 |
+
font-size: 20px;
|
| 92 |
+
}
|
| 93 |
+
}
|
| 94 |
+
p {
|
| 95 |
+
margin: 0;
|
| 96 |
+
}
|
| 97 |
+
|
| 98 |
+
code,
|
| 99 |
+
.counter {
|
| 100 |
+
font-family: var(--mono);
|
| 101 |
+
display: inline-flex;
|
| 102 |
+
border-radius: 4px;
|
| 103 |
+
color: var(--text-h);
|
| 104 |
+
}
|
| 105 |
+
|
| 106 |
+
code {
|
| 107 |
+
font-size: 15px;
|
| 108 |
+
line-height: 135%;
|
| 109 |
+
padding: 4px 8px;
|
| 110 |
+
background: var(--code-bg);
|
| 111 |
+
}
|
src/main.jsx
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import { StrictMode } from 'react'
|
| 2 |
+
import { createRoot } from 'react-dom/client'
|
| 3 |
+
import './index.css'
|
| 4 |
+
import App from './App.jsx'
|
| 5 |
+
|
| 6 |
+
createRoot(document.getElementById('root')).render(
|
| 7 |
+
<StrictMode>
|
| 8 |
+
<App />
|
| 9 |
+
</StrictMode>,
|
| 10 |
+
)
|
vite.config.js
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import { defineConfig } from 'vite'
|
| 2 |
+
import react from '@vitejs/plugin-react'
|
| 3 |
+
|
| 4 |
+
// https://vite.dev/config/
|
| 5 |
+
export default defineConfig({
|
| 6 |
+
plugins: [react()],
|
| 7 |
+
})
|