Merge branch 'master' of https://repo.pokash.pl/POKASH.PL/ably.do

allegro.py

@@ -0,0 +1,119 @@
+import os
+import re
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer, DataCollatorForLanguageModeling
+from datasets import Dataset
+
+# Konfiguracja
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+MODEL_NAME = "allegro/herbert-base-cased"
+SPECIAL_TOKENS = ["[CITATION_START]", "[CITATION_END]"]
+TEXT_FILE_PATH = "./docs/kodekspracy.txt"  # Zmień na właściwą ścieżkę
+
+def prepare_dataset_from_file(file_path):
+    with open(file_path, 'r', encoding='utf-8') as f:
+        text = f.read()
+
+    articles = re.findall(r'Art\.\s*\d+[a-z]*\..*?(?=\s*Art\.\s*\d+[a-z]*\.|\Z)', text, flags=re.DOTALL)
+    
+    formatted_articles = []
+    for article in articles:
+        article = ' '.join(article.strip().split())
+        
+        art_match = re.match(r'Art\.\s*(\d+[a-z]*)\.?\s*(.*)', article, re.DOTALL)
+        if art_match:
+            art_number = art_match.group(1)
+            art_text = art_match.group(2)
+            
+            paragraphs = re.split(r'(§\s*\d+\.)', art_text)
+            if len(paragraphs) > 1:
+                formatted_paragraphs = []
+                for i in range(1, len(paragraphs), 2):
+                    para_num = paragraphs[i].strip()
+                    para_text = paragraphs[i+1].strip()
+                    formatted_paragraphs.append(f"{para_num} {para_text}")
+                formatted = f"[CITATION_START] Kodeks Pracy, Art. {art_number} [CITATION_END]\n" + "\n".join(formatted_paragraphs)
+            else:
+                formatted = f"[CITATION_START] Kodeks Pracy, Art. {art_number} [CITATION_END] {art_text}"
+            
+            formatted_articles.append({"text": formatted})
+            
+            questions = [
+                f"Zacytuj artykuł {art_number} Kodeksu pracy.",
+                f"Co mówi artykuł {art_number} Kodeksu pracy?",
+                f"Podaj treść artykułu {art_number} Kodeksu pracy."
+            ]
+            for question in questions:
+                formatted_articles.append({"text": f"{question}\n{formatted}"})
+    
+    return formatted_articles
+
+def main():
+    # Inicjalizacja tokenizera
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+    tokenizer.pad_token = tokenizer.eos_token
+    tokenizer.add_special_tokens({"additional_special_tokens": SPECIAL_TOKENS})
+
+    print(f"Pad token: {tokenizer.pad_token}")
+    print(f"Pad token ID: {tokenizer.pad_token_id}")
+
+    # Przygotowanie danych
+    data = prepare_dataset_from_file(TEXT_FILE_PATH)
+    dataset = Dataset.from_dict({"text": [d["text"] for d in data]})
+
+    # Tokenizacja
+    def tokenize_function(examples):
+        tokenized = tokenizer(
+            examples["text"],
+            truncation=True,
+            padding="max_length",
+            max_length=512,
+            return_tensors="pt"
+        )
+        tokenized["labels"] = tokenized["input_ids"].clone()
+        return tokenized
+
+    tokenized_dataset = dataset.map(tokenize_function, batched=True, remove_columns=dataset.column_names)
+
+    # Model i data collator
+    model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
+    model.resize_token_embeddings(len(tokenizer))
+    model.config.pad_token_id = tokenizer.pad_token_id
+    
+    data_collator = DataCollatorForLanguageModeling(
+        tokenizer=tokenizer,
+        mlm=False
+    )
+
+    # Konfiguracja treningu
+    training_args = TrainingArguments(
+        output_dir="./results",
+        num_train_epochs=32,
+        per_device_train_batch_size=2,
+        learning_rate=1e-5,
+        logging_steps=10,
+        weight_decay=0.01,
+        report_to="none",
+        save_strategy="steps",
+        save_steps=500,
+        evaluation_strategy="steps",
+        eval_steps=500,
+        load_best_model_at_end=True,
+    )
+
+    # Trainer
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=tokenized_dataset,
+        eval_dataset=tokenized_dataset,
+        data_collator=data_collator
+    )
+
+    print("Rozpoczęcie treningu...")
+    trainer.train()
+    trainer.save_model("./trained_model/allegro")
+    tokenizer.save_pretrained("./trained_model/allegro")
+
+if __name__ == "__main__":
+    main()

catalog.json

@@ -0,0 +1,5 @@
+{
+    "kodekspracy": "Kodeks Pracy",
+    "urlopproporcjonalny": "Rozporządzenie BHP",
+    "ustawaopanstwowejinspekcjipracy": "Ustawa o Państwowej inspekcji pracy"
+}

docs/kodekspracy.txt

@@ -2,7 +2,7 @@ USTAWA
 
 z dnia 26 czerwca 1974 r.
 
-Kodeks pracy1)
+Kodeks pracy
 
 (Dz. U. z 2023 r. poz. 1465 oraz z 2024 r. poz. 878, 1222, 1871 i 1965)
 
@@ -11,8 +11,6 @@ obowiązuje od dnia 1 stycznia 1975 r.
 
 historia od dnia 16 lutego 1998 r.
 
-Preambuła (uchylona)
-
 DZIAŁ PIERWSZY
 Przepisy ogólne
 

file_catalog.json

@@ -1,3 +0,0 @@
-{
-    "kodekspracy.md": "Kodeks pracy"
-}

gemma-faiss.py

@@ -0,0 +1,93 @@
+import os
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+import faiss
+import numpy as np
+import ollama
+import gradio as gr
+import os
+import argparse
+from sentence_transformers import SentenceTransformer
+
+# === KONFIGURACJA ===
+model_name = "hse.ably.do:latest"  # Nazwa modelu Ollama
+faiss_index_path = "faiss_index.idx"  # Plik indeksu FAISS
+kodeks_file = "/home/ably.do/docs/kodekspracy.txt"  # Plik z treścią kodeksu pracy
+embedding_model = SentenceTransformer("all-MiniLM-L6-v2")  # Model do embedowania tekstu
+
+# === KROK 1: WCZYTYWANIE KODEKSU PRACY ===
+def load_kodeks(filepath):
+    with open(filepath, "r", encoding="utf-8") as file:
+        content = file.read()
+        articles = content.split("\n\n")  # Dzielimy na sekcje
+    return [article.strip() for article in articles if article.strip().startswith("Art.")]
+
+# === KROK 2: TWORZENIE INDEKSU FAISS ===
+def create_faiss_index(sections):
+    embeddings = embedding_model.encode(sections, convert_to_numpy=True)  # Tworzenie wektorów
+    index = faiss.IndexFlatL2(embeddings.shape[1])  # Indeks FAISS
+    index.add(embeddings)  # Dodanie wektorów do FAISS
+    faiss.write_index(index, faiss_index_path)  # Zapis indeksu
+    return index, sections
+
+# === KROK 3: WYSZUKIWANIE NAJBLIŻSZEGO FRAGMENTU ===
+def search_faiss(query, index, sections, top_k=3):
+    query_vector = embedding_model.encode([query], convert_to_numpy=True)
+    _, idx = index.search(query_vector, top_k)  # Szukamy więcej wyników
+
+    results = [sections[i] for i in idx[0] if i < len(sections)]
+    return "\n\n".join(results)  # Połącz kilka najlepszych fragmentów
+
+# === KROK 4: GENEROWANIE ODPOWIEDZI Z OLLAMA ===
+def generate_response(user_query):
+    if not os.path.exists(faiss_index_path):
+        return "Błąd: Indeks FAISS nie istnieje. Uruchom aplikację z opcją --rebuild-index."
+
+    try:
+        index = faiss.read_index(faiss_index_path)
+    except Exception as e:
+        return f"Błąd ładowania FAISS: {str(e)}"
+
+    sections = load_kodeks(kodeks_file)
+    best_match = search_faiss(user_query, index, sections)
+
+    # 👀 DEBUG: Sprawdź, co zwraca FAISS
+    print(f"🔍 Najlepsze dopasowanie FAISS dla '{user_query}':\n{best_match}")
+
+    prompt = f"""
+    Odpowiedz na pytanie na podstawie następującego tekstu:
+
+    {best_match}
+
+    Pytanie: {user_query}
+    Podaj dokładny tekst artykułu, jeśli go znajdziesz w treści powyżej.
+    """
+
+    response = ollama.chat(model=model_name, messages=[{"role": "user", "content": prompt}])
+
+    print(f"📝 Odpowiedź modelu:\n{response}")  # 👀 DEBUG: Sprawdź odpowiedź Ollama
+
+    return response.get("message", response.get("content", "Błąd: Nie udało się wygenerować odpowiedzi."))
+
+# === KROK 5: INTERFEJS WEBOWY ===
+iface = gr.Interface(
+    fn=generate_response,
+    inputs=gr.Textbox(label="Zadaj pytanie o kodeks pracy"),
+    outputs=gr.Textbox(label="Odpowiedź"),
+    title="Asystent Kodeksu Pracy",
+    description="Wpisz pytanie, a system zwróci odpowiedni fragment kodeksu pracy."
+)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--rebuild-index", action="store_true", help="Odbudowanie indeksu FAISS")
+    args = parser.parse_args()
+
+    if args.rebuild_index or not os.path.exists(faiss_index_path):
+        print("Tworzenie nowego indeksu FAISS...")
+        sections = load_kodeks(kodeks_file)
+        create_faiss_index(sections)
+    else:
+        print("Indeks FAISS już istnieje.")
+    
+    iface.launch(share=True)

gemma.py

@@ -0,0 +1,119 @@
+import os
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+import torch
+import faiss
+import numpy as np
+from sentence_transformers import SentenceTransformer
+from datasets import Dataset
+from peft import LoraConfig, get_peft_model
+from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer, DataCollatorForLanguageModeling
+
+# 1️⃣ Inicjalizacja modelu do embeddingów
+embed_model = SentenceTransformer("all-MiniLM-L6-v2")
+
+# 2️⃣ Dodanie dokumentów i embeddingów
+def read_documents_from_file(file_path):
+    with open(file_path, 'r', encoding='utf-8') as file:
+        content = file.read()
+        articles = content.split('\n\n')
+        documents = []
+        for article in articles:
+            if article.strip().startswith('Art.'):
+                documents.append(article.strip())
+    return documents
+#documents = [
+#    "Jak założyć firmę w Polsce?", 
+#    "Jak rozliczyć podatek VAT?", 
+#    "Procedura składania reklamacji w e-sklepie.",
+#    "Jakie dokumenty są potrzebne do rejestracji działalności?"
+#]
+file_path = './docs/kodekspracy.txt'  # Zmień na właściwą ścieżkę
+documents = read_documents_from_file(file_path)
+embeddings = embed_model.encode(documents)
+
+# 3️⃣ Inicjalizacja FAISS i dodanie wektorów
+dim = embeddings.shape[1]
+index = faiss.IndexFlatL2(dim)
+index.add(np.array(embeddings, dtype=np.float32))
+
+# 4️⃣ Przygotowanie danych treningowych
+def create_training_data():
+    data = {
+        "text": documents,
+        "embedding": embeddings.tolist()
+    }
+    return Dataset.from_dict(data)
+
+dataset = create_training_data()
+
+# Podział danych na treningowe i ewaluacyjne
+split_dataset = dataset.train_test_split(test_size=0.25)
+train_dataset = split_dataset["train"]
+eval_dataset = split_dataset["test"]
+
+# 5️⃣ Ładowanie modelu Gemma 2B
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model_name = "google/gemma-2-2b"
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16).to(device)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# 6️⃣ Konfiguracja LoRA
+lora_config = LoraConfig(
+    r=8, lora_alpha=32, lora_dropout=0.1, bias="none", task_type="CAUSAL_LM"
+)
+model = get_peft_model(model, lora_config)
+
+# 7️⃣ Tokenizacja danych
+max_length = 384
+
+def tokenize_function(examples):
+    return tokenizer(
+        examples["text"],
+        padding="max_length",
+        truncation=True,
+        max_length=max_length
+    )
+
+tokenized_train = train_dataset.map(tokenize_function, batched=True)
+tokenized_eval = eval_dataset.map(tokenize_function, batched=True)
+
+# 8️⃣ Parametry treningu
+training_args = TrainingArguments(
+    output_dir="./results",
+    eval_strategy="steps",          # Ewaluacja co określoną liczbę kroków
+    eval_steps=500,                 # Ewaluacja co 500 kroków
+    save_strategy="steps",          # Zapis modelu co określoną liczbę kroków
+    save_steps=500,                 # Zapis modelu co 500 kroków
+    learning_rate=1e-5,
+    per_device_train_batch_size=2,
+    per_device_eval_batch_size=2,
+    num_train_epochs=16,
+    weight_decay=0.01,
+    load_best_model_at_end=True,    # Wczytaj najlepszy model na końcu
+    metric_for_best_model="loss",   # Kryterium wyboru najlepszego modelu
+    greater_is_better=False,        # Niższy loss = lepszy model
+)
+
+# 9️⃣ Data Collator
+data_collator = DataCollatorForLanguageModeling(
+    tokenizer=tokenizer,
+    mlm=False
+)
+
+# 🔟 Trening modelu
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_train,
+    eval_dataset=tokenized_eval,    # Dodany zestaw ewaluacyjny
+    data_collator=data_collator,
+)
+
+trainer.train()
+
+# 1️⃣1️⃣ Zapis modelu
+model.save_pretrained("./trained_model/gemma")
+tokenizer.save_pretrained("./trained_model/gemma")
+
+print("✅ Model został wytrenowany i zapisany!")

gpt.py

@@ -0,0 +1,118 @@
+import os
+import re
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer, DataCollatorForLanguageModeling
+from datasets import Dataset
+
+# Konfiguracja
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+MODEL_NAME = "gpt2-medium"
+SPECIAL_TOKENS = ["[CITATION_START]", "[CITATION_END]"]
+TEXT_FILE_PATH = "./docs/kodekspracy.txt"  # Zmień na właściwą ścieżkę
+
+def prepare_dataset_from_file(file_path):
+    with open(file_path, 'r', encoding='utf-8') as f:
+        text = f.read()
+
+    # Wydziel artykuły za pomocą wyrażenia regularnego
+    articles = re.findall(r'Art\.\s*\d+[a-z]*\..*?(?=\s*Art\.\s*\d+[a-z]*\.|\Z)', text, flags=re.DOTALL)
+    
+    formatted_articles = []
+    for article in articles:
+        # Usuń zbędne białe znaki
+        article = ' '.join(article.strip().split())
+        
+        # Wydziel numer artykułu i treść
+        art_match = re.match(r'Art\.\s*(\d+[a-z]*)\.?\s*(.*)', article, re.DOTALL)
+        if art_match:
+            art_number = art_match.group(1)
+            art_text = art_match.group(2)
+            
+            # Podziel na paragrafy, jeśli istnieją
+            paragraphs = re.split(r'(§\s*\d+\.)', art_text)
+            if len(paragraphs) > 1:
+                formatted_paragraphs = []
+                for i in range(1, len(paragraphs), 2):
+                    para_num = paragraphs[i].strip()
+                    para_text = paragraphs[i+1].strip()
+                    formatted_paragraphs.append(f"{para_num} {para_text}")
+                formatted = f"[CITATION_START] Kodeks Pracy, Art. {art_number} [CITATION_END]\n" + "\n".join(formatted_paragraphs)
+            else:
+                formatted = f"[CITATION_START] Kodeks Pracy, Art. {art_number} [CITATION_END] {art_text}"
+            
+            formatted_articles.append({"text": formatted})
+            
+        # Dodaj przykłady pytań i odpowiedzi
+        questions = [
+            f"Zacytuj artykuł {art_number} Kodeksu pracy.",
+            f"Co mówi artykuł {art_number} Kodeksu pracy?",
+            f"Podaj treść artykułu {art_number} Kodeksu pracy."
+        ]
+        for question in questions:
+            formatted_articles.append({"text": f"{question}\n{formatted}"})
+    
+    return formatted_articles
+
+
+def main():
+    # Inicjalizacja tokenizera
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+    tokenizer.add_special_tokens({"additional_special_tokens": SPECIAL_TOKENS})
+    tokenizer.pad_token = tokenizer.eos_token
+
+    # Przygotowanie danych
+    data = prepare_dataset_from_file(TEXT_FILE_PATH)
+    dataset = Dataset.from_dict({"text": [d["text"] for d in data]})
+
+    # Tokenizacja
+    def tokenize_function(examples):
+        tokenized = tokenizer(
+            examples["text"],
+            truncation=True,
+            padding="max_length",
+            max_length=1024,  # Zwiększono dla dłuższych artykułów
+            return_tensors="pt"
+        )
+        tokenized["labels"] = tokenized["input_ids"].clone()
+        return tokenized
+
+    tokenized_dataset = dataset.map(tokenize_function, batched=True)
+
+    # Model i data collator
+    model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
+    model.resize_token_embeddings(len(tokenizer), mean_resizing=False)
+    
+    data_collator = DataCollatorForLanguageModeling(
+        tokenizer=tokenizer,
+        mlm=False
+    )
+
+    # Konfiguracja treningu
+    training_args = TrainingArguments(
+        output_dir="./results",
+        num_train_epochs=32,  # Zwiększono liczbę epok
+        per_device_train_batch_size=2,
+        learning_rate=1e-5, #precyzja uczenia
+        logging_steps=10,
+        weight_decay=0.01,
+        report_to="none",
+        save_strategy="no",
+        load_best_model_at_end=True,  # Ładowanie najlepszego modelu na końcu
+    )
+
+
+    # Trainer
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=tokenized_dataset,
+        data_collator=data_collator
+    )
+
+    print("Rozpoczęcie treningu...")
+    trainer.train()
+    trainer.save_model("./trained_model/gpt")
+    tokenizer.save_pretrained("./trained_model/gpt")
+
+if __name__ == "__main__":
+    main()

hft.py

@@ -1,14 +1,14 @@
 import os
 import torch
 import torch.nn as nn
-from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer
+from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer, DataCollatorForLanguageModeling
 from datasets import Dataset
-from PIL import Image
 import re
-import pytesseract
-import docx2txt
-import PyPDF2
 import json
+import PyPDF2
+import docx2txt
+import pytesseract
+from PIL import Image
 from collections import defaultdict
 from huggingface_hub import login
 
@@ -34,127 +34,205 @@ class SourceMapper:
         return self.idx_to_source.get(idx, "Unknown")
 
 def load_file_catalog(catalog_path):
-    with open(catalog_path, 'r', encoding='utf-8') as file:
-        return json.load(file)
+    try:
+        with open(catalog_path, 'r', encoding='utf-8') as file:
+            return json.load(file)
+    except Exception as e:
+        print(f"Błąd wczytywania katalogu plików: {str(e)}")
+        return {}
 
 def identify_legal_document(filename, file_catalog):
-    return file_catalog.get(filename, "Opracowanie własne")
+    base_name = os.path.splitext(filename)[0].lower()
+    return file_catalog.get(base_name, "Opracowanie własne")
 
 def extract_text_from_file(file_path):
-    _, ext = os.path.splitext(file_path)
-    ext = ext.lower()
+    try:
+        _, ext = os.path.splitext(file_path)
+        ext = ext.lower()
         
-    if ext in ['.txt', '.md']:
-        with open(file_path, 'r', encoding='utf-8') as file:
-            return file.read()
-    elif ext == '.pdf':
-        text = ""
-        with open(file_path, 'rb') as file:
-            reader = PyPDF2.PdfReader(file)
-            for page in reader.pages:
-                text += page.extract_text()
-        return text
-    elif ext in ['.doc', '.docx']:
-        return docx2txt.process(file_path)
-    elif ext in ['.jpg', '.jpeg', '.png', '.bmp', '.tiff']:
-        return pytesseract.image_to_string(Image.open(file_path))
-    else:
+        if ext in ['.txt', '.md']:
+            with open(file_path, 'r', encoding='utf-8') as file:
+                return file.read()
+        elif ext == '.pdf':
+            text = ""
+            try:
+                with open(file_path, 'rb') as file:
+                    reader = PyPDF2.PdfReader(file)
+                    for page in reader.pages:
+                        text += page.extract_text() or ""
+            except Exception as e:
+                print(f"Błąd PDF: {str(e)}")
+            return text
+        elif ext in ['.doc', '.docx']:
+            return docx2txt.process(file_path)
+        elif ext in ['.jpg', '.jpeg', '.png', '.bmp', '.tiff']:
+            return pytesseract.image_to_string(Image.open(file_path))
+        else:
+            print(f"Nieobsługiwany format pliku: {ext}")
+            return ""
+    except Exception as e:
+        print(f"Błąd ekstrakcji tekstu: {str(e)}")
         return ""
 
 def prepare_dataset(directory, catalog_path, source_mapper):
     file_catalog = load_file_catalog(catalog_path)
     data = []
     
+    print(f"\n{'='*50}\nDIAGNOSTYKA DANYCH\n{'='*50}")
+    
     for root, _, files in os.walk(directory):
         for file in files:
             file_path = os.path.join(root, file)
-            text = extract_text_from_file(file_path)
-            if not text:
+            print(f"\nPrzetwarzanie pliku: {file_path}")
+            
+            try:
+                text = extract_text_from_file(file_path)
+                if not text.strip():
+                    print("Pominięto - brak tekstu")
+                    continue
+                    
+                print(f"Długość tekstu: {len(text)} znaków")
+                
+                doc_type = identify_legal_document(file, file_catalog)
+                print(f"Rozpoznany typ dokumentu: {doc_type}")
+                
+                if doc_type != "Opracowanie własne":
+                    articles = re.split(r'(?i)(Art[\.\s]+\d+[\.\s]?)', text)
+                    articles = [a.strip() for a in articles if a.strip()]
+                    
+                    print(f"Znaleziono {len(articles)} fragmentów")
+                    
+                    for i in range(0, len(articles)-1, 2):
+                        article_number = articles[i]
+                        article_content = articles[i+1]
+                        
+                        if len(article_content) < 50:
+                            continue
+                            
+                        source = f"{doc_type}, {article_number}"
+                        source_mapper.add_source(source)
+                        data.append({
+                            "text": f"{article_number} {article_content}",
+                            "source_idx": source_mapper.get_idx(source)
+                        })
+                else:
+                    clean_text = re.sub(r'\s+', ' ', text).strip()
+                    chunks = [clean_text[i:i+512] for i in range(0, len(clean_text), 512)]
+                    chunks = [c for c in chunks if c.strip()]
+                    
+                    for chunk in chunks:
+                        data.append({
+                            "text": chunk,
+                            "source_idx": -1
+                        })
+                    print(f"Dodano {len(chunks)} chunków")
+                    
+            except Exception as e:
+                print(f"Błąd podczas przetwarzania pliku: {str(e)}")
                 continue
                 
-            doc_type = identify_legal_document(file, file_catalog)
-            if doc_type != "Opracowanie własne":
-                articles = re.split(r'(Art\.\s+\d+[\.\s])', text)
-                for i in range(1, len(articles), 2):
-                    article_number = articles[i].strip()
-                    article_content = articles[i+1].strip() if i+1 < len(articles) else ""
-                    source = f"{doc_type}, {article_number}"
-                    source_mapper.add_source(source)
+    print(f"\nPodsumowanie przygotowania danych:")
+    print(f"Łączna liczba przykładów: {len(data)}")
+    if data:
+        print("Przykładowy wpis:")
+        print(json.dumps(data[0], indent=2, ensure_ascii=False))
+    else:
+        print("BRAK DANYCH - sprawdź diagnostykę powyżej")
         
-                    data.append({
-                        "text": f"{article_number} {article_content}",
-                        "source_idx": source_mapper.get_idx(source)
-                    })
-            else:
-                chunks = [text[i:i+512] for i in range(0, len(text), 512)]
-                for chunk in chunks:
-                    data.append({
-                        "text": chunk,
-                        "source_idx": -1
-                    })
     return data
 
-def tokenize_function(examples):
-    tokenized = tokenizer(
-        examples["text"],
-        truncation=True,
-        padding="max_length",
-        max_length=512,
-        return_tensors="pt"
-    )
-    tokenized["labels"] = tokenized["input_ids"].clone()
-    tokenized["source_idx"] = examples["source_idx"]
-    return tokenized
-
-def custom_collate_fn(batch):
-    input_ids = torch.stack([torch.tensor(b["input_ids"]) for b in batch])
-    attention_mask = torch.stack([torch.tensor(b["attention_mask"]) for b in batch])
-    labels = torch.stack([torch.tensor(b["labels"]) for b in batch])
-    source_idx = torch.tensor([b.get("source_idx", -1) for b in batch], dtype=torch.long)
-    return {
-        "input_ids": input_ids,
-        "attention_mask": attention_mask,
-        "labels": labels,
-        "source_idx": source_idx
-    }
-
 class CustomModel(nn.Module):
     def __init__(self, model_name, config):
         super().__init__()
         self.base_model = AutoModelForCausalLM.from_pretrained(model_name, config=config)
-        self.source_embedding = nn.Embedding(1000, config.hidden_size, padding_idx=-1)
+        self.source_embedding = nn.Embedding(10000, config.hidden_size, padding_idx=-1)
+        
+        for param in self.base_model.parameters():
+            param.requires_grad = False
+        for param in self.base_model.get_output_embeddings().parameters():
+            param.requires_grad = True
             
     def forward(self, input_ids=None, attention_mask=None, labels=None, source_idx=None, **kwargs):
         if source_idx is not None:
-            source_idx = torch.clamp(source_idx, 0, self.source_embedding.num_embeddings-1)
-            source_embeds = self.source_embedding(source_idx).unsqueeze(1).expand(-1, input_ids.size(1), -1)
+            valid_indices = torch.clamp(source_idx, 0, self.source_embedding.num_embeddings-1)
+            source_embeds = self.source_embedding(valid_indices).unsqueeze(1)
             inputs_embeds = self.base_model.get_input_embeddings()(input_ids) + source_embeds
-            return self.base_model(inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=labels, **kwargs)
-        return self.base_model(input_ids=input_ids, attention_mask=attention_mask, labels=labels, **kwargs)
+            return self.base_model(
+                inputs_embeds=inputs_embeds,
+                attention_mask=attention_mask,
+                labels=labels,
+                **kwargs
+            )
+        return self.base_model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            labels=labels,
+            **kwargs
+        )
     
     def generate(self, *args, **kwargs):
         return self.base_model.generate(*args, **kwargs)
 
+class CustomDataCollator(DataCollatorForLanguageModeling):
+    def torch_call(self, examples):
+        # Przetwórz podstawowe pola
+        input_ids = torch.stack([torch.tensor(ex["input_ids"]) for ex in examples])
+        attention_mask = torch.stack([torch.tensor(ex["attention_mask"]) for ex in examples])
+        labels = torch.stack([torch.tensor(ex["labels"]) for ex in examples])
+        
+        batch = {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "labels": labels
+        }
+        
+        # Dodaj source_idx jeśli istnieje
+        if "source_idx" in examples[0]:
+            source_idx = torch.stack([torch.tensor(ex["source_idx"]) for ex in examples])
+            batch["source_idx"] = source_idx
+            
+        return batch
+
 def main():
-    # Inicjalizacja
     source_mapper = SourceMapper()
     model_name = "crumb/nano-mistral"
     tokenizer = AutoTokenizer.from_pretrained(model_name)
     tokenizer.pad_token = tokenizer.eos_token
 
     # Przygotowanie danych
-    catalog_path = "file_catalog.json"
-    data = prepare_dataset("files", catalog_path, source_mapper)
-    dataset = Dataset.from_list(data)
-    tokenized_dataset = dataset.map(tokenize_function, batched=True, batch_size=8)
+    catalog_path = "catalog.json"
+    data = prepare_dataset("docs", catalog_path, source_mapper)
     
-    # Model
-    config = AutoModelForCausalLM.from_pretrained(model_name).config
-    model = CustomModel(model_name, config)
+    if not data:
+        print("\nBrak danych do treningu!")
+        return
+
+    #dataset = Dataset.from_list(data)
+    dataset = Dataset.from_dict({k: [d[k] for d in data] for k in data[0]})
+
+
+    def tokenize_function(examples):
+        tokenized = tokenizer(
+            examples["text"],
+            truncation=True,
+            padding="max_length",
+            max_length=512,
+            return_tensors="pt"
+        )
+        return {
+            "input_ids": tokenized["input_ids"].squeeze(),
+            "attention_mask": tokenized["attention_mask"].squeeze(),
+            "labels": tokenized["input_ids"].squeeze().clone(),
+            "source_idx": examples["source_idx"]  # Dodano bez konwersji do tensora
+        }
+
+    tokenized_dataset = dataset.map(tokenize_function, batched=True, batch_size=16)
+
+    model = CustomModel(model_name, AutoModelForCausalLM.from_pretrained(model_name).config)
+    model.source_mapper = source_mapper
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     model.to(device)
 
-    # Trening
     training_args = TrainingArguments(
         output_dir="./results",
         num_train_epochs=3,
@@ -162,66 +240,22 @@ def main():
         gradient_accumulation_steps=4,
         learning_rate=2e-5,
         fp16=torch.cuda.is_available(),
-        logging_steps=1,
+        logging_steps=10,
         save_strategy="steps",
         save_steps=1000,
-        report_to="none"
+        report_to="none",
+        remove_unused_columns=False
     )
 
     trainer = Trainer(
         model=model,
         args=training_args,
         train_dataset=tokenized_dataset,
-        data_collator=custom_collate_fn,
+        data_collator=CustomDataCollator(tokenizer=tokenizer, mlm=False)
     )
-    print("Rozpoczęcie treningu...")
+
+    print("\nRozpoczęcie treningu...")
     trainer.train()
 
-    # Testowanie
-    def generate_answer(question):
-        inputs = tokenizer(question, return_tensors="pt").to(device)
-        
-        outputs = model.generate(
-            **inputs,
-            max_new_tokens=200,
-            temperature=0.7,
-            top_p=0.9,
-            do_sample=True,
-            repetition_penalty=1.2,
-            no_repeat_ngram_size=2,
-            pad_token_id=tokenizer.eos_token_id
-        )
-        
-        answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
-        answer = answer.replace(question, "").strip()
-        
-        sources = set()
-        for match in re.finditer(r'Art\.\s+\d+', answer):
-            article_ref = match.group(0).strip()
-            for idx, source in source_mapper.idx_to_source.items():
-                if article_ref in source:
-                    sources.add(source)
-        
-        return {
-            "question": question,
-            "answer": answer,
-            "sources": list(sources) if sources else ["Opracowanie własne"]
-        }
-
-    # Przykładowe testy
-    test_questions = [
-        "Jakie są zasady udzielania urlopu wypoczynkowego?",
-        "Co mówi art. 154 kodeksu pracy?",
-        "Jakie są obowiązki pracodawcy w zakresie BHP?"
-    ]
-    
-    print("\n" + "="*50 + "\nWYNIKI TESTOW\n" + "="*50)
-    for question in test_questions:
-        result = generate_answer(question)
-        print(f"\nPYTANIE: {result['question']}")
-        print(f"ODPOWIEDŹ: {result['answer'][:500]}")
-        print(f"ŹRÓDŁA: {', '.join(result['sources'])}")
-        print("-"*80)
-
 if __name__ == "__main__":
     main()

requirements.txt

@@ -0,0 +1,8 @@
+torch>=2.0.1
+transformers>=4.30.2
+datasets>=2.13.1
+Pillow>=9.4.0
+pytesseract>=0.3.10
+python-docx>=0.8.11
+PyPDF2>=3.0.1
+huggingface-hub>=0.16.4

test.py

@@ -0,0 +1,22 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_path = "./trained_model/gpt"
+model = AutoModelForCausalLM.from_pretrained(model_path)
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+tokenizer.pad_token = tokenizer.eos_token
+model.config.pad_token_id = tokenizer.eos_token_id
+
+def generate_response(prompt, max_length=1000):
+    inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True)
+    outputs = model.generate(
+        inputs.input_ids,
+        attention_mask=inputs.attention_mask,
+        pad_token_id=tokenizer.pad_token_id,
+        max_length=100
+    )
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+prompt = "Zacytuj paragraf pierwszy artykułu 154 Kodeksu pracy."
+response = generate_response(prompt)
+print(response)