mod

hft.py

@@ -1,8 +1,6 @@
 import os
 import torch
 import torch.nn as nn
-from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer, DataCollatorForLanguageModeling
-from datasets import Dataset
 import re
 import json
 import PyPDF2
@@ -10,14 +8,26 @@ import docx2txt
 import pytesseract
 from PIL import Image
 from collections import defaultdict
+from transformers import (
+    AutoTokenizer,
+    AutoModelForCausalLM,
+    TrainingArguments,
+    DataCollatorForLanguageModeling
+)
+from datasets import Dataset
 from huggingface_hub import login
 
 # Konfiguracja
-os.environ['TORCH_USE_CUDA_DSA'] = '1'
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
-login(token="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")
+login(token="TWÓJ_TOKEN_HUGGINGFACE")
 
-class SourceMapper:
+class LegalAITrainer:
+    def __init__(self):
+        self.source_mapper = defaultdict(lambda: len(self.source_mapper))
+        self.idx_to_source = {}
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    class SourceMapper:
         def __init__(self):
             self.source_to_idx = defaultdict(lambda: len(self.source_to_idx))
             self.idx_to_source = {}
@@ -33,189 +43,132 @@ class SourceMapper:
         def get_source(self, idx):
             return self.idx_to_source.get(idx, "Unknown")
 
-def load_file_catalog(catalog_path):
+    class LegalModel(nn.Module):
-    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):
-    base_name = os.path.splitext(filename)[0].lower()
-    return file_catalog.get(base_name, "Opracowanie własne")
-
-def extract_text_from_file(file_path):
-    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 = ""
-            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)
-            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
-                
-    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")
-        
-    return data
-
-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(10000, config.hidden_size, padding_idx=-1)
+            self.source_embedding = nn.Embedding(100000, config.hidden_size, padding_idx=-1)
+            self.confidence_layer = nn.Linear(config.hidden_size, 1)
             
+            # Freeze base model
             for param in self.base_model.parameters():
                 param.requires_grad = False
-        for param in self.base_model.get_output_embeddings().parameters():
+                
+            # Trainable components
+            for layer in [self.source_embedding, self.confidence_layer]:
+                for param in layer.parameters():
                     param.requires_grad = True
 
-    def forward(self, input_ids=None, attention_mask=None, labels=None, source_idx=None, **kwargs):
+        def forward(self, input_ids=None, attention_mask=None, labels=None, source_idx=None):
             if source_idx is not None:
-            valid_indices = torch.clamp(source_idx, 0, self.source_embedding.num_embeddings-1)
+                source_idx = torch.clamp(source_idx, 0, self.source_embedding.num_embeddings-1)
-            source_embeds = self.source_embedding(valid_indices).unsqueeze(1)
+                source_embeds = self.source_embedding(source_idx).unsqueeze(1)
                 inputs_embeds = self.base_model.get_input_embeddings()(input_ids) + source_embeds
-            return self.base_model(
+                outputs = self.base_model(
                     inputs_embeds=inputs_embeds,
                     attention_mask=attention_mask,
-                labels=labels,
+                    labels=labels
-                **kwargs
                 )
-        return self.base_model(
+            else:
+                outputs = self.base_model(
                     input_ids=input_ids,
                     attention_mask=attention_mask,
-            labels=labels,
+                    labels=labels
-            **kwargs
                 )
                 
-    def generate(self, *args, **kwargs):
+            confidence = torch.sigmoid(self.confidence_layer(outputs.hidden_states[-1].mean(dim=1)))
-        return self.base_model.generate(*args, **kwargs)
+            return {
-
+                "loss": outputs.loss,
-class CustomDataCollator(DataCollatorForLanguageModeling):
+                "logits": outputs.logits,
-    def torch_call(self, examples):
+                "confidence": confidence,
-        # Przetwórz podstawowe pola
+                "hidden_states": outputs.hidden_states
-        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
+    def load_file_catalog(self, catalog_path):
-        if "source_idx" in examples[0]:
+        try:
-            source_idx = torch.stack([torch.tensor(ex["source_idx"]) for ex in examples])
+            with open(catalog_path, 'r', encoding='utf-8') as f:
-            batch["source_idx"] = source_idx
+                return json.load(f)
+        except Exception as e:
+            print(f"Błąd ładowania katalogu: {str(e)}")
+            return {}
 
-        return batch
+    def extract_text(self, file_path):
+        ext = os.path.splitext(file_path)[1].lower()
+        try:
+            if ext in ['.txt', '.md']:
+                with open(file_path, 'r', encoding='utf-8') as f:
+                    return f.read()
+            elif ext == '.pdf':
+                text = ""
+                with open(file_path, 'rb') as f:
+                    reader = PyPDF2.PdfReader(f)
+                    for page in reader.pages:
+                        text += page.extract_text() or ""
+                return text
+            elif ext in ['.doc', '.docx']:
+                return docx2txt.process(file_path)
+            elif ext in ['.jpg', '.jpeg', '.png']:
+                return pytesseract.image_to_string(Image.open(file_path))
+            else:
+                return ""
+        except Exception as e:
+            print(f"Błąd przetwarzania {file_path}: {str(e)}")
+            return ""
 
-def main():
+    def prepare_data(self, data_dir, catalog_path):
-    source_mapper = SourceMapper()
+        catalog = self.load_file_catalog(catalog_path)
-    model_name = "crumb/nano-mistral"
+        data = []
+        source_mapper = self.SourceMapper()
+
+        for root, _, files in os.walk(data_dir):
+            for file in files:
+                file_path = os.path.join(root, file)
+                text = self.extract_text(file_path)
+                
+                if not text:
+                    continue
+                
+                doc_type = catalog.get(os.path.splitext(file)[0].lower(), "Opracowanie własne")
+                
+                if doc_type != "Opracowanie własne":
+                    articles = re.split(r'(?i)(Art\.\s*\d+[a-z]*)', text)
+                    for i in range(1, len(articles), 2):
+                        art_num = articles[i].strip()
+                        content = articles[i+1].strip()
+                        
+                        if len(content) < 100:
+                            continue
+                            
+                        source = f"{doc_type}, {art_num}"
+                        source_mapper.add_source(source)
+                        data.append({
+                            "text": f"[LEGAL] {art_num} {content}",
+                            "source_idx": source_mapper.get_idx(source),
+                            "is_legal": 1
+                        })
+                else:
+                    chunks = [f"[GENERAL] {text[i:i+512]}" for i in range(0, len(text), 512)]
+                    for chunk in chunks:
+                        data.append({
+                            "text": chunk,
+                            "source_idx": -1,
+                            "is_legal": 0
+                        })
+        
+        return Dataset.from_dict({k: [d[k] for d in data] for k in data[0]}), source_mapper
+
+    def train(self, model_name="crumb/nano-mistral", data_dir="data", catalog_path="catalog.json"):
+        # Przygotowanie danych
+        dataset, source_mapper = self.prepare_data(data_dir, catalog_path)
         tokenizer = AutoTokenizer.from_pretrained(model_name)
         tokenizer.pad_token = tokenizer.eos_token
 
-    # Przygotowanie danych
+        # Tokenizacja
-    catalog_path = "file_catalog.json"
+        def tokenize_fn(examples):
-    data = prepare_dataset("files", catalog_path, source_mapper)
-    
-    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",
+                truncation=True,
                 max_length=512,
                 return_tensors="pt"
             )
@@ -223,39 +176,118 @@ def main():
                 "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
+                "source_idx": examples["source_idx"]
             }
 
-    tokenized_dataset = dataset.map(tokenize_function, batched=True, batch_size=16)
+        tokenized_dataset = dataset.map(tokenize_fn, batched=True, batch_size=16)
 
-    model = CustomModel(model_name, AutoModelForCausalLM.from_pretrained(model_name).config)
+        # Inicjalizacja modelu
-    model.source_mapper = source_mapper
+        config = AutoModelForCausalLM.from_pretrained(model_name).config
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        model = self.LegalModel(model_name, config).to(self.device)
-    model.to(device)
 
+        # Konfiguracja treningu
         training_args = TrainingArguments(
-        output_dir="./results",
+            output_dir="./legal_ai_model",
-        num_train_epochs=3,
+            num_train_epochs=5,
             per_device_train_batch_size=2,
             gradient_accumulation_steps=4,
             learning_rate=2e-5,
             fp16=torch.cuda.is_available(),
-        logging_steps=10,
+            logging_steps=50,
             save_strategy="steps",
-        save_steps=1000,
+            save_steps=500,
             report_to="none",
             remove_unused_columns=False
         )
 
-    trainer = Trainer(
+        # Customowy Trainer
+        class LegalTrainer(Trainer):
+            def compute_loss(self, model, inputs, return_outputs=False):
+                outputs = model(**inputs)
+                loss = outputs.loss
+                
+                # Confidence loss
+                target_conf = (inputs["source_idx"] != -1).float()
+                conf_loss = nn.BCELoss()(outputs.confidence.squeeze(), target_conf)
+                
+                total_loss = loss + 0.7*conf_loss
+                return (total_loss, outputs) if return_outputs else total_loss
+
+        # Trening
+        trainer = LegalTrainer(
             model=model,
             args=training_args,
             train_dataset=tokenized_dataset,
-        data_collator=CustomDataCollator(tokenizer=tokenizer, mlm=False)
+            data_collator=DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
         )
 
-    print("\nRozpoczęcie treningu...")
+        print("Rozpoczęcie treningu...")
         trainer.train()
         
+        # Zapisz model
+        model.save_pretrained("./trained_legal_ai")
+        tokenizer.save_pretrained("./trained_legal_ai")
+        with open("./trained_legal_ai/source_mapper.json", "w") as f:
+            json.dump(source_mapper.idx_to_source, f)
+
+        print("Trening zakończony i model zapisany!")
+
+    def generate_response(self, prompt, confidence_threshold=0.65):
+        # Ładowanie modelu
+        model = self.LegalModel.from_pretrained("./trained_legal_ai", 
+                                              config=AutoModelForCausalLM.from_pretrained("crumb/nano-mistral").config)
+        tokenizer = AutoTokenizer.from_pretrained("./trained_legal_ai")
+        model.to(self.device)
+        
+        # Ładowanie mapowania źródeł
+        with open("./trained_legal_ai/source_mapper.json", "r") as f:
+            source_mapper = json.load(f)
+
+        # Przygotowanie wejścia
+        inputs = tokenizer(
+            f"[PROMPT] {prompt} [RESPONSE]",
+            return_tensors="pt",
+            max_length=512,
+            truncation=True
+        ).to(self.device)
+
+        # Generacja
+        with torch.no_grad():
+            outputs = model.generate(
+                input_ids=inputs.input_ids,
+                attention_mask=inputs.attention_mask,
+                max_length=512,
+                do_sample=True,
+                temperature=0.7,
+                top_k=50,
+                pad_token_id=tokenizer.eos_token_id,
+                output_scores=True,
+                return_dict_in_generate=True
+            )
+
+        # Analiza wyników
+        full_text = tokenizer.decode(outputs.sequences[0], skip_special_tokens=True)
+        confidence = torch.sigmoid(outputs.scores[-1][:, tokenizer.eos_token_id]).item()
+        
+        # Ekstrakcja i weryfikacja źródeł
+        citations = list(set(re.findall(r"Art\.\s*\d+[a-z]*", full_text)))
+        verified = [c for c in citations if any(c in s for s in source_mapper.values())]
+
+        if confidence < confidence_threshold or not verified:
+            return "Nie mogę udzielić jednoznacznej odpowiedzi na podstawie dostępnych danych."
+        else:
+            return f"{full_text}\n\nPotwierdzone źródła: {', '.join(verified)}"
+
 if __name__ == "__main__":
-    main()
+    legal_ai = LegalAITrainer()
+    
+    # Etap 1: Trening
+    legal_ai.train(
+        model_name="crumb/nano-mistral",
+        data_dir="./legal_docs",
+        catalog_path="./catalog.json"
+    )
+    
+    # Etap 2: Testowanie
+    test_prompt = "Ile dni urlopu przysługuje po 5 latach pracy w pełnym wymiarze?"
+    print(legal_ai.generate_response(test_prompt))