import os import torch import torch.nn as nn from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer, GenerationMixin from datasets import Dataset from PIL import Image import re import pytesseract import docx2txt import PyPDF2 import json from collections import defaultdict from huggingface_hub import login os.environ['TORCH_USE_CUDA_DSA'] = '1' os.environ["TOKENIZERS_PARALLELISM"] = "false" login(token="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") class SourceMapper: def __init__(self): self.source_to_idx = defaultdict(lambda: len(self.source_to_idx)) self.idx_to_source = {} def add_source(self, source): if source and source not in self.source_to_idx: idx = self.source_to_idx[source] self.idx_to_source[idx] = source def get_idx(self, source): return self.source_to_idx[source] if source else -1 def get_source(self, idx): 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) def identify_legal_document(filename, file_catalog): return file_catalog.get(filename, "Opracowanie własne") def extract_text_from_file(file_path): _, 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: return "" def prepare_dataset(directory, catalog_path, source_mapper): file_catalog = load_file_catalog(catalog_path) data = [] 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: 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) 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 # Brak źródła }) 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]).cpu() attention_mask = torch.stack([torch.tensor(b["attention_mask"]) for b in batch]).cpu() labels = torch.stack([torch.tensor(b["labels"]) for b in batch]).cpu() source_idx = torch.tensor([b.get("source_idx", -1) for b in batch], dtype=torch.long).cpu() return {"input_ids": input_ids, "attention_mask": attention_mask, "labels": labels, "source_idx": source_idx} class CustomModel(nn.Module, GenerationMixin): def __init__(self, model_name, config): super().__init__() self.base_model = AutoModelForCausalLM.from_pretrained(model_name, config=config) self.source_embedding = nn.Embedding( num_embeddings=1000, embedding_dim=config.hidden_size, padding_idx=-1 ) self.config = config self.device = next(self.base_model.parameters()).device 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) hidden_states = self.base_model.get_input_embeddings()(input_ids) + source_embeds outputs = self.base_model(inputs_embeds=hidden_states, attention_mask=attention_mask, labels=labels, **kwargs) else: outputs = self.base_model(input_ids=input_ids, attention_mask=attention_mask, labels=labels, **kwargs) return outputs def prepare_inputs_for_generation(self, input_ids, **kwargs): return self.base_model.prepare_inputs_for_generation(input_ids, **kwargs) def _reorder_cache(self, past, beam_idx): return self.base_model._reorder_cache(past, beam_idx) class CustomTrainer(Trainer): def compute_loss(self, model, inputs, return_outputs=False, **kwargs): device = next(model.parameters()).device inputs = {k: v.to(device) for k, v in inputs.items()} labels = inputs.pop("labels") source_idx = inputs.pop("source_idx", None) outputs = model(**inputs, labels=labels, source_idx=source_idx) loss = outputs.loss return (loss, outputs) if return_outputs else loss # Inicjalizacja komponentów 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) # Inicjalizacja modelu config = AutoModelForCausalLM.from_pretrained(model_name).config model = CustomModel(model_name, config) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = model.to(device) # Konfiguracja treningu training_args = TrainingArguments( output_dir="./results", num_train_epochs=3, per_device_train_batch_size=2, gradient_accumulation_steps=4, learning_rate=2e-5, fp16=torch.cuda.is_available(), logging_steps=1, logging_dir="./logs", save_strategy="steps", save_steps=1000, logging_strategy="no", report_to="none" ) # Trening trainer = CustomTrainer( model=model, args=training_args, train_dataset=tokenized_dataset, data_collator=custom_collate_fn, ) trainer.train() # Funkcja generująca odpowiedź def generate_answer(question, model, tokenizer, source_mapper, max_length=200): device = next(model.parameters()).device inputs = tokenizer(question, return_tensors="pt", truncation=True, max_length=512).to(device) outputs = model.generate( **inputs, max_length=max_length, num_return_sequences=1, return_dict_in_generate=True, output_scores=True, ) answer = tokenizer.decode(outputs.sequences[0], skip_special_tokens=True) # Pobierz źródło z ostatniego tokena last_token_id = outputs.sequences[0][-1].item() source_idx = model.source_embedding.weight.shape[0] - 1 # Tymczasowe rozwiązanie source = source_mapper.get_source(source_idx) return f"{answer}\n\nŹródło: {source if source else 'Opracowanie własne'}" # Przykład użycia question = "Ile dni urlopu przysługuje pracownikowi?" answer = generate_answer(question, model, tokenizer, source_mapper) print(answer)