Jacek Dusza commited on
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Initial commit: NLP Pipeline backend and React frontend

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.gitignore ADDED
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1
+ # Logs
2
+ logs
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+ *.log
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+ npm-debug.log*
5
+ 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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+
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+ node_modules
11
+ dist
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+ dist-ssr
13
+ *.local
14
+
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+ # Editor directories and files
16
+ .vscode/*
17
+ !.vscode/extensions.json
18
+ .idea
19
+ .DS_Store
20
+ *.suo
21
+ *.ntvs*
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+ *.njsproj
23
+ *.sln
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+ *.sw?
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+
26
+ # Dependency directories
27
+ node_modules/
28
+ backend/venv/
29
+
30
+ # Python cache
31
+ __pycache__/
32
+ *.pyc
33
+
34
+ # Environment variables (SECURITY)
35
+ .env
36
+ backend/.env
37
+
38
+ # macOS system files
39
+ .DS_Store
40
+
41
+ # Output data (optional, prevents uploading large datasets)
42
+ backend/results_aug/
43
+ backend/results_base/
README.md ADDED
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1
+ # React + Vite
2
+
3
+ This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules.
4
+
5
+ Currently, two official plugins are available:
6
+
7
+ - [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react) uses [Oxc](https://oxc.rs)
8
+ - [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react-swc) uses [SWC](https://swc.rs/)
9
+
10
+ ## React Compiler
11
+
12
+ 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).
13
+
14
+ ## Expanding the ESLint configuration
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+
16
+ 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.
backend/augmented_dataset.csv ADDED
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backend/clean_data.py ADDED
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1
+ import pandas as pd
2
+
3
+ # Load the augmented dataset from the CSV file
4
+ df = pd.read_csv("augmented_dataset.csv")
5
+ size_before = len(df)
6
+
7
+ # Remove duplicate entries based on the unique 'id' column,
8
+ # retaining the most recently generated variant (keep='last')
9
+ df_clean = df.drop_duplicates(subset=['id'], keep='last')
10
+ size_after = len(df_clean)
11
+
12
+ # Overwrite the existing file with the cleaned, deduplicated dataset
13
+ df_clean.to_csv("augmented_dataset.csv", index=False, encoding="utf-8")
14
+
15
+ # Output the results of the cleaning process
16
+ print("Data cleaning process completed successfully.")
17
+ print(f"Identified and removed {size_before - size_after} duplicate records.")
18
+ print(f"Current deduplicated dataset size: {size_after} samples.")
backend/dataset_test.csv ADDED
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backend/download_polemo.py ADDED
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1
+ from datasets import load_dataset
2
+ import pandas as pd
3
+
4
+ def download_data():
5
+ print("Connecting to Hugging Face and downloading the PolEmo2.0 dataset...")
6
+
7
+ # Load the 'all_text' configuration of the dataset
8
+ dataset = load_dataset("clarin-pl/polemo2-official", "all_text", split="train", trust_remote_code=True)
9
+
10
+ # Convert the first 400 samples to a pandas DataFrame
11
+ df = dataset.select(range(400)).to_pandas()
12
+
13
+ # Rename the target column to 'label' for pipeline compatibility
14
+ df = df.rename(columns={"target": "label"})
15
+
16
+ # Drop technical columns, retaining only 'text' and 'label'
17
+ df = df[['text', 'label']]
18
+
19
+ # Insert a sequential ID column for tracking purposes
20
+ df.insert(0, 'id', range(1, len(df) + 1))
21
+
22
+ # Export the processed dataset to a CSV file
23
+ df.to_csv("dataset_test.csv", index=False, encoding="utf-8")
24
+ print("Success! Saved 400 authentic, diverse reviews to 'dataset_test.csv'.")
25
+
26
+ if __name__ == "__main__":
27
+ download_data()
backend/main.py ADDED
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1
+ import os
2
+ import time
3
+ from fastapi import FastAPI, HTTPException
4
+ from fastapi.middleware.cors import CORSMiddleware
5
+ from pydantic import BaseModel
6
+ from deep_translator import GoogleTranslator
7
+ from sentence_transformers import SentenceTransformer, util
8
+ import nlpaug.augmenter.word as naw
9
+ from groq import Groq
10
+ from dotenv import load_dotenv
11
+
12
+ # Load environment variables from the .env file (for local development)
13
+ load_dotenv()
14
+
15
+ # Application initialization
16
+ app = FastAPI(title="Hybrid Augmentation Pipeline API")
17
+
18
+ print("Loading Sentence-BERT model (this might take a moment on the first run)...")
19
+ # Using a lightweight, multilingual model (supports Polish and English)
20
+ model = SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2')
21
+ print("Sentence-BERT model loaded successfully.")
22
+
23
+ print("Loading HerBERT model for EDA...")
24
+ # Using HerBERT for contextual word substitution in the Polish language
25
+ eda_aug = naw.ContextualWordEmbsAug(
26
+ model_path='allegro/herbert-base-cased',
27
+ action="substitute",
28
+ device="cpu", # Switch to "cuda" if appropriate GPU hardware is available
29
+ aug_p=0.1, # Substitute a maximum of 10% of words in the input sequence
30
+ top_k=10
31
+ )
32
+ print("HerBERT model loaded successfully.")
33
+
34
+ print("Configuring Groq API...")
35
+ # Securely fetching the API key from environment variables
36
+ GROQ_API_KEY = os.getenv("GROQ_API_KEY")
37
+ if not GROQ_API_KEY:
38
+ raise ValueError("CRITICAL ERROR: GROQ_API_KEY environment variable is missing!")
39
+
40
+ groq_client = Groq(api_key=GROQ_API_KEY)
41
+ print("Groq API configured successfully.")
42
+
43
+ # CORS configuration (allows the frontend application to communicate with the backend)
44
+ app.add_middleware(
45
+ CORSMiddleware,
46
+ allow_origins=["*"], # In production, restrict this to the specific frontend URL
47
+ allow_credentials=True,
48
+ allow_methods=["*"],
49
+ allow_headers=["*"],
50
+ )
51
+
52
+ # 1. Data Models (Expected JSON payloads from the frontend client)
53
+ class AugmentRequest(BaseModel):
54
+ text: str
55
+ method: str
56
+ pivot_lang: str = "en"
57
+ eda_p: float = 0.1 # Percentage of words to substitute during EDA
58
+
59
+ class FilterRequest(BaseModel):
60
+ original: str
61
+ augmented: str
62
+ threshold: float = 0.8 # Cutoff threshold for the semantic similarity filter
63
+
64
+ # 2. Endpoint: Data Augmentation
65
+ @app.post("/augment")
66
+ async def generate_paraphrase(request: AugmentRequest):
67
+ try:
68
+ # Simulated network delay for UI feedback
69
+ time.sleep(1)
70
+
71
+ if request.method == "EDA":
72
+ # Dynamically adjust the substitution probability based on client input
73
+ eda_aug.aug_p = request.eda_p
74
+ result = eda_aug.augment(request.text)[0]
75
+
76
+ elif request.method == "BT":
77
+ # Retrieve the target pivot language from the request
78
+ pivot_lang = request.pivot_lang.lower()
79
+
80
+ # Step 1: Source (PL) -> Pivot Language
81
+ intermediate_text = GoogleTranslator(source='pl', target=pivot_lang).translate(request.text)
82
+
83
+ # Step 2: Pivot Language -> Source (PL)
84
+ result = GoogleTranslator(source=pivot_lang, target='pl').translate(intermediate_text)
85
+
86
+ return {
87
+ "original": request.text,
88
+ "augmented": result,
89
+ "method": request.method,
90
+ "pivot_lang": pivot_lang,
91
+ "intermediate": intermediate_text
92
+ }
93
+
94
+ elif request.method == "LLM":
95
+ # LLM Generation (Llama 3 via Groq API)
96
+ prompt = f"""You are an expert NLP assistant. Paraphrase the following Polish sentence in Polish.
97
+ Maintain the exact same meaning and sentiment, but use a different syntactic structure or synonyms.
98
+ Return ONLY the single paraphrased sentence. Do not include any introductions, notes, or comments.
99
+
100
+ Input sentence: {request.text}"""
101
+
102
+ chat_completion = groq_client.chat.completions.create(
103
+ messages=[{"role": "user", "content": prompt}],
104
+ model="llama-3.3-70b-versatile",
105
+ temperature=0.7,
106
+ max_tokens=100,
107
+ )
108
+
109
+ result = chat_completion.choices[0].message.content.strip()
110
+
111
+ # Strip enclosing quotation marks if generated by the model
112
+ if result.startswith('"') and result.endswith('"'):
113
+ result = result[1:-1]
114
+
115
+ else:
116
+ raise HTTPException(status_code=400, detail="Unknown augmentation method requested.")
117
+
118
+ return {"original": request.text, "augmented": result, "method": request.method}
119
+
120
+ except Exception as e:
121
+ raise HTTPException(status_code=500, detail=str(e))
122
+
123
+ # 3. Endpoint: Semantic Filtration (Sentence-BERT)
124
+ @app.post("/filter")
125
+ async def filter_sentence(request: FilterRequest):
126
+ try:
127
+ # Compute dense vector embeddings for both text sequences
128
+ emb1 = model.encode(request.original, convert_to_tensor=True)
129
+ emb2 = model.encode(request.augmented, convert_to_tensor=True)
130
+
131
+ # Compute cosine similarity between the embeddings
132
+ cosine_scores = util.cos_sim(emb1, emb2)
133
+ sim_score = cosine_scores[0][0].item() # Extract the numerical value from the tensor
134
+
135
+ # Floating-point precision safeguard (clamp between 0.0 and 1.0)
136
+ sim_score = min(max(sim_score, 0.0), 1.0)
137
+
138
+ # Evaluate against the requested threshold
139
+ passed = sim_score >= request.threshold
140
+
141
+ return {
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 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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",
44
+ "integrity": "sha512-locTkQyKvwIEgBzVrn8693ebc97F2U8ZHjbXwDXJ5Fn2TCpNwTlKcaKLkdHop5c/icOFE7qt7Q9JC5hnKNa6Gg==",
45
+ "dev": true,
46
+ "license": "MIT",
47
+ "engines": {
48
+ "node": ">=6.9.0"
49
+ }
50
+ },
51
+ "node_modules/@babel/core": {
52
+ "version": "7.29.7",
53
+ "resolved": "https://registry.npmjs.org/@babel/core/-/core-7.29.7.tgz",
54
+ "integrity": "sha512-RgHBCvtjbOK2gXSNBNIkNoEc9qoVEtau3hj8gEqKQuL3HZAibKarWFEI3Lfm6EYKkLalOh8eSrj9b+ch9H/VBA==",
55
+ "dev": true,
56
+ "license": "MIT",
57
+ "dependencies": {
58
+ "@babel/code-frame": "^7.29.7",
59
+ "@babel/generator": "^7.29.7",
60
+ "@babel/helper-compilation-targets": "^7.29.7",
61
+ "@babel/helper-module-transforms": "^7.29.7",
62
+ "@babel/helpers": "^7.29.7",
63
+ "@babel/parser": "^7.29.7",
64
+ "@babel/template": "^7.29.7",
65
+ "@babel/traverse": "^7.29.7",
66
+ "@babel/types": "^7.29.7",
67
+ "@jridgewell/remapping": "^2.3.5",
68
+ "convert-source-map": "^2.0.0",
69
+ "debug": "^4.1.0",
70
+ "gensync": "^1.0.0-beta.2",
71
+ "json5": "^2.2.3",
72
+ "semver": "^6.3.1"
73
+ },
74
+ "engines": {
75
+ "node": ">=6.9.0"
76
+ },
77
+ "funding": {
78
+ "type": "opencollective",
79
+ "url": "https://opencollective.com/babel"
80
+ }
81
+ },
82
+ "node_modules/@babel/generator": {
83
+ "version": "7.29.7",
84
+ "resolved": "https://registry.npmjs.org/@babel/generator/-/generator-7.29.7.tgz",
85
+ "integrity": "sha512-DkXD5OJQaAQIdZ1bt3UZdEnHAn9Imd3IVBdX03UFe+ony9Ojw5pzr9YVKGDY1jt+Gcn/FnGkNf8r+Vj5NOJWtQ==",
86
+ "dev": true,
87
+ "license": "MIT",
88
+ "dependencies": {
89
+ "@babel/parser": "^7.29.7",
90
+ "@babel/types": "^7.29.7",
91
+ "@jridgewell/gen-mapping": "^0.3.12",
92
+ "@jridgewell/trace-mapping": "^0.3.28",
93
+ "jsesc": "^3.0.2"
94
+ },
95
+ "engines": {
96
+ "node": ">=6.9.0"
97
+ }
98
+ },
99
+ "node_modules/@babel/helper-compilation-targets": {
100
+ "version": "7.29.7",
101
+ "resolved": "https://registry.npmjs.org/@babel/helper-compilation-targets/-/helper-compilation-targets-7.29.7.tgz",
102
+ "integrity": "sha512-wem6WaBj4NaVYVdNhLPPVacES6ZJ+KBBfSkTMD3YZxbP3rm3Di85tJU5ljaUNhaOynt+Aj0xruhYuzQBt8n71g==",
103
+ "dev": true,
104
+ "license": "MIT",
105
+ "dependencies": {
106
+ "@babel/compat-data": "^7.29.7",
107
+ "@babel/helper-validator-option": "^7.29.7",
108
+ "browserslist": "^4.24.0",
109
+ "lru-cache": "^5.1.1",
110
+ "semver": "^6.3.1"
111
+ },
112
+ "engines": {
113
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114
+ }
115
+ },
116
+ "node_modules/@babel/helper-globals": {
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+ "version": "7.29.7",
118
+ "resolved": "https://registry.npmjs.org/@babel/helper-globals/-/helper-globals-7.29.7.tgz",
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+ "integrity": "sha512-3nQVUAtvkKH9zahfWgw96Jc/uFOmjACE1kQz82E2lqWmHBgjzbNlsC22nuQTfahmWeQtTq5nQ/4Nnd2A1wj4zA==",
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+ "dev": true,
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+ "license": "MIT",
122
+ "engines": {
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+ "node": ">=6.9.0"
124
+ }
125
+ },
126
+ "node_modules/@babel/helper-module-imports": {
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+ "version": "7.29.7",
128
+ "resolved": "https://registry.npmjs.org/@babel/helper-module-imports/-/helper-module-imports-7.29.7.tgz",
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+ "integrity": "sha512-ejHwrQQYcm9xnTivShn2IDOlIzInN34AXskvq9QicvCtEzq1Vzclu/tKF8Jq1Cg8JG2GL6/EmjgsCT7lXepE3g==",
130
+ "dev": true,
131
+ "license": "MIT",
132
+ "dependencies": {
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+ "@babel/traverse": "^7.29.7",
134
+ "@babel/types": "^7.29.7"
135
+ },
136
+ "engines": {
137
+ "node": ">=6.9.0"
138
+ }
139
+ },
140
+ "node_modules/@babel/helper-module-transforms": {
141
+ "version": "7.29.7",
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+ "resolved": "https://registry.npmjs.org/@babel/helper-module-transforms/-/helper-module-transforms-7.29.7.tgz",
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+ "integrity": "sha512-UPUVSyXbOh627KiCIGQSgwWzGeBKLkaJ9PJEdrngIwMSzxLR4jS4+f1f1jb7VzBbg8nFLaYotvVPFCTqdrmTAg==",
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+ "dev": true,
145
+ "license": "MIT",
146
+ "dependencies": {
147
+ "@babel/helper-module-imports": "^7.29.7",
148
+ "@babel/helper-validator-identifier": "^7.29.7",
149
+ "@babel/traverse": "^7.29.7"
150
+ },
151
+ "engines": {
152
+ "node": ">=6.9.0"
153
+ },
154
+ "peerDependencies": {
155
+ "@babel/core": "^7.0.0"
156
+ }
157
+ },
158
+ "node_modules/@babel/helper-string-parser": {
159
+ "version": "7.29.7",
160
+ "resolved": "https://registry.npmjs.org/@babel/helper-string-parser/-/helper-string-parser-7.29.7.tgz",
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+ "integrity": "sha512-Pb5ijPrZ89GDH8223L4UP8i6QApWxs04RbPQJTeWDV0/keR2E36MeKnyr6LYmUUvqRRI+Iv87SuF1W6ErINzYw==",
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+ "dev": true,
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+ "license": "MIT",
164
+ "engines": {
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+ "node": ">=6.9.0"
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+ }
167
+ },
168
+ "node_modules/@babel/helper-validator-identifier": {
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+ "version": "7.29.7",
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+ "resolved": "https://registry.npmjs.org/@babel/helper-validator-identifier/-/helper-validator-identifier-7.29.7.tgz",
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+ "integrity": "sha512-qehxGkRj55h/ff8EMaJ+cYhyaKlHIxqYDn682wQD7RNp9UujOQsHog2uS0r2vzr4pW+sXf90NeeayjcNaX3fFg==",
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+ "dev": true,
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+ "license": "MIT",
174
+ "engines": {
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+ "node": ">=6.9.0"
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+ }
177
+ },
178
+ "node_modules/@babel/helper-validator-option": {
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+ "version": "7.29.7",
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+ "resolved": "https://registry.npmjs.org/@babel/helper-validator-option/-/helper-validator-option-7.29.7.tgz",
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+ "integrity": "sha512-N9ZErrD+yW5geCDtBqnOoxmR8+tNKiGuxKlDpuJxfsqpa2dFcexaziGAE/qoHLiDDreVNMupxGmSoNlyvsA3gw==",
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+ "dev": true,
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+ "license": "MIT",
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+ "engines": {
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+ "node": ">=6.9.0"
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+ }
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188
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191
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+ "dev": true,
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+ "license": "MIT",
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+ "@babel/template": "^7.29.7",
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198
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+ "integrity": "sha512-hnORnjP/1P/zFEndoeX+n+t1RwWRJiJpM/jO7FW32Kn9r5+sJB2JWOdYo4L6k78j15eCwY3Gm/7364B1EMwtNg==",
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+ "bin": {
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+ },
214
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+ }
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221
+ "integrity": "sha512-puq+Gf35oI24FeN11LkoUQFqv9uwNeWpxXZi/Ji3rRIoKAzKnxRaZ+Gkj0vKS9ZCiTESfng1N9LyOyXvo+m+Gg==",
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+ "dev": true,
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+ "license": "MIT",
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+ "@babel/parser": "^7.29.7",
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+ "@babel/types": "^7.29.7"
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+ },
229
+ "engines": {
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+ "node": ">=6.9.0"
231
+ }
232
+ },
233
+ "node_modules/@babel/traverse": {
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+ "version": "7.29.7",
235
+ "resolved": "https://registry.npmjs.org/@babel/traverse/-/traverse-7.29.7.tgz",
236
+ "integrity": "sha512-EhlfNQtZ+NK22w5BM61ciuiq1m58ed33Wr1Xan//ZRTy6hgjnwyCffRYwzsGXdASJSUJ1guZILsErh1eQcl+zw==",
237
+ "dev": true,
238
+ "license": "MIT",
239
+ "dependencies": {
240
+ "@babel/code-frame": "^7.29.7",
241
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package.json ADDED
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+ {
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+ "name": "data-augmentation",
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+ "private": true,
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+ "version": "0.0.0",
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+ "type": "module",
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+ "scripts": {
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+ "dev": "vite",
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+ "build": "vite build",
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+ "lint": "eslint .",
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+ "preview": "vite preview"
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+ },
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+ "dependencies": {
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+ "react": "^19.2.6",
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+ "react-dom": "^19.2.6"
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+ },
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+ "devDependencies": {
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+ "@eslint/js": "^10.0.1",
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+ "@types/react": "^19.2.14",
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+ "@types/react-dom": "^19.2.3",
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+ "@vitejs/plugin-react": "^6.0.1",
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+ "eslint": "^10.3.0",
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+ "eslint-plugin-react-hooks": "^7.1.1",
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+ "eslint-plugin-react-refresh": "^0.5.2",
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+ "globals": "^17.6.0",
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+ "vite": "^8.0.12"
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+ }
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+ }
public/favicon.svg ADDED
public/icons.svg ADDED
src/App.css ADDED
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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);
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+ border: 2px solid transparent;
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+ transition: border-color 0.3s;
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+ margin-bottom: 24px;
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+
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+ &:hover {
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+ border-color: var(--accent-border);
13
+ }
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+ &:focus-visible {
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+ outline: 2px solid var(--accent);
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+ outline-offset: 2px;
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+ }
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+ }
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+
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+ .hero {
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+ position: relative;
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+
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+ .base,
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+ .framework,
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+ inset-inline: 0;
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+ margin: 0 auto;
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+ }
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+ .base {
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+ width: 170px;
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+ }
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+ .vite {
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+ position: absolute;
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+ }
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+ .framework {
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+ top: 34px;
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+ transform: perspective(2000px) rotateZ(300deg) rotateX(44deg) rotateY(39deg)
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+ scale(1.4);
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+ height: 26px;
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+ scale(0.8);
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+ }
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+ }
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+
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+ #center {
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+ display: flex;
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+ flex-direction: column;
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+ gap: 25px;
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+ place-content: center;
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+ place-items: center;
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+ flex-grow: 1;
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+
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+ @media (max-width: 1024px) {
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+ padding: 32px 20px 24px;
69
+ gap: 18px;
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+ }
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+ }
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+
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;
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+ @media (max-width: 1024px) {
82
+ padding: 24px 20px;
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+ }
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+ .icon {
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+ margin-bottom: 16px;
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+ width: 22px;
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+ height: 22px;
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+ }
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+
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+ @media (max-width: 1024px) {
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+ flex-direction: column;
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+ text-align: center;
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+ }
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+ }
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+ #docs {
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+ border-right: 1px solid var(--border);
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+ @media (max-width: 1024px) {
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+ border-right: none;
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+ border-bottom: 1px solid var(--border);
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+ }
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+ }
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+
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+ #next-steps ul {
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+ list-style: none;
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+ padding: 0;
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+ display: flex;
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+ gap: 8px;
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+ margin: 32px 0 0;
113
+
114
+ .logo {
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+ height: 18px;
116
+ }
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+
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+ a {
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+ 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;
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+ transition: box-shadow 0.3s;
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+
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
+ }
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+ &::after {
181
+ right: 0;
182
+ border-right-color: var(--border);
183
+ }
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+ }
src/App.jsx ADDED
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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 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ })