Ten kod działa!

hft.py

@@ -1,31 +1,22 @@
 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 numpy as np
 import PyPDF2
 import docx2txt
 import pytesseract
 from PIL import Image
 from collections import defaultdict
-from transformers import (
-    AutoTokenizer,
-    AutoModelForCausalLM,
-    TrainingArguments,
-    Trainer,
-    DataCollatorForLanguageModeling
-)
-from datasets import Dataset, Features, Value
 from huggingface_hub import login
 
+# Konfiguracja
+os.environ['TORCH_USE_CUDA_DSA'] = '1'
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 login(token="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")
 
-class LegalAITrainer:
-    def __init__(self):
-        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))
@@ -42,255 +33,229 @@ class LegalAITrainer:
     def get_source(self, idx):
         return self.idx_to_source.get(idx, "Unknown")
 
-    class LegalModel(nn.Module):
+def load_file_catalog(catalog_path):
-        def __init__(self, model_name, config):
-            super().__init__()
-            self.base_model = AutoModelForCausalLM.from_pretrained(model_name, config=config)
-            self.source_embedding = nn.Embedding(100000, config.hidden_size, padding_idx=-1)
-            self.confidence_layer = nn.Linear(config.hidden_size, 1)
-            
-            for param in self.base_model.parameters():
-                param.requires_grad = False
-                
-            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):
-            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)
-                inputs_embeds = self.base_model.get_input_embeddings()(input_ids) + source_embeds
-                outputs = self.base_model(
-                    inputs_embeds=inputs_embeds,
-                    attention_mask=attention_mask,
-                    labels=labels
-                )
-            else:
-                outputs = self.base_model(
-                    input_ids=input_ids,
-                    attention_mask=attention_mask,
-                    labels=labels
-                )
-                
-            confidence = torch.sigmoid(self.confidence_layer(outputs.hidden_states[-1].mean(dim=1)))
-            return {
-                "loss": outputs.loss,
-                "logits": outputs.logits,
-                "confidence": confidence,
-                "hidden_states": outputs.hidden_states
-            }
-
-    def load_file_catalog(self, catalog_path):
     try:
-            with open(catalog_path, 'r', encoding='utf-8') as f:
+        with open(catalog_path, 'r', encoding='utf-8') as file:
-                return json.load(f)
+            return json.load(file)
     except Exception as e:
-            print(f"Błąd ładowania katalogu: {str(e)}")
+        print(f"Błąd wczytywania katalogu plików: {str(e)}")
         return {}
 
-    def extract_text(self, file_path):
+def identify_legal_document(filename, file_catalog):
-        ext = os.path.splitext(file_path)[1].lower()
+    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 f:
+            with open(file_path, 'r', encoding='utf-8') as file:
-                    return f.read()
+                return file.read()
         elif ext == '.pdf':
             text = ""
-                with open(file_path, 'rb') as f:
+            try:
-                    reader = PyPDF2.PdfReader(f)
+                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']:
+        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 przetwarzania {file_path}: {str(e)}")
+        print(f"Błąd ekstrakcji tekstu: {str(e)}")
         return ""
 
-    def prepare_data(self, data_dir, catalog_path):
+def prepare_dataset(directory, catalog_path, source_mapper):
-        catalog = self.load_file_catalog(catalog_path)
+    file_catalog = load_file_catalog(catalog_path)
     data = []
-        source_mapper = self.SourceMapper()
     
-        for root, _, files in os.walk(data_dir):
+    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 = self.extract_text(file_path)
+            print(f"\nPrzetwarzanie pliku: {file_path}")
             
-                if not text:
+            try:
+                text = extract_text_from_file(file_path)
+                if not text.strip():
+                    print("Pominięto - brak tekstu")
                     continue
                     
-                doc_type = catalog.get(os.path.splitext(file)[0].lower(), "Opracowanie własne")
+                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+[a-z]*)', text)
+                    articles = re.split(r'(?i)(Art[\.\s]+\d+[\.\s]?)', text)
-                    for i in range(1, len(articles), 2):
+                    articles = [a.strip() for a in articles if a.strip()]
-                        art_num = articles[i].strip()
-                        content = articles[i+1].strip()
                     
-                        if len(content) < 100:
+                    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}, {art_num}"
+                        source = f"{doc_type}, {article_number}"
                         source_mapper.add_source(source)
                         data.append({
-                            "text": f"[LEGAL] {art_num} {content}",
+                            "text": f"{article_number} {article_content}",
-                            "source_idx": source_mapper.get_idx(source),
+                            "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)]
+                    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,
+                            "source_idx": -1
-                            "is_legal": 0
                         })
+                    print(f"Dodano {len(chunks)} chunków")
                     
-        features = Features({
+            except Exception as e:
-            "text": Value("string"),
+                print(f"Błąd podczas przetwarzania pliku: {str(e)}")
-            "source_idx": Value("int32"),
+                continue
-            "is_legal": Value("int32")
-        })
                 
-        return Dataset.from_dict({
+    print(f"\nPodsumowanie przygotowania danych:")
-            "text": [d["text"] for d in data],
+    print(f"Łączna liczba przykładów: {len(data)}")
-            "source_idx": np.array([d["source_idx"] for d in data], dtype=np.int32),
+    if data:
-            "is_legal": np.array([d["is_legal"] for d in data], dtype=np.int32)
+        print("Przykładowy wpis:")
-        }, features=features), source_mapper
+        print(json.dumps(data[0], indent=2, ensure_ascii=False))
+    else:
+        print("BRAK DANYCH - sprawdź diagnostykę powyżej")
         
-    def train(self, model_name="crumb/nano-mistral", data_dir="data", catalog_path="catalog.json"):
+    return data
-        dataset, source_mapper = self.prepare_data(data_dir, catalog_path)
+
+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)
+        
+        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:
+            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
+        )
+    
+    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():
+    source_mapper = SourceMapper()
+    model_name = "crumb/nano-mistral"
     tokenizer = AutoTokenizer.from_pretrained(model_name)
     tokenizer.pad_token = tokenizer.eos_token
 
-        def tokenize_fn(examples):
+    # Przygotowanie danych
+    catalog_path = "file_catalog.json"
+    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"],
-                padding="max_length",
             truncation=True,
+            padding="max_length",
             max_length=512,
             return_tensors="pt"
         )
         return {
-                "input_ids": tokenized["input_ids"].squeeze().tolist(),
+            "input_ids": tokenized["input_ids"].squeeze(),
-                "attention_mask": tokenized["attention_mask"].squeeze().tolist(),
+            "attention_mask": tokenized["attention_mask"].squeeze(),
-                "labels": tokenized["input_ids"].squeeze().clone().tolist(),
+            "labels": tokenized["input_ids"].squeeze().clone(),
-                "source_idx": examples["source_idx"]
+            "source_idx": examples["source_idx"]  # Dodano bez konwersji do tensora
         }
 
-        tokenized_dataset = dataset.map(tokenize_fn, batched=True, batch_size=16)
+    tokenized_dataset = dataset.map(tokenize_function, batched=True, batch_size=16)
 
-        class CustomDataCollator(DataCollatorForLanguageModeling):
+    model = CustomModel(model_name, AutoModelForCausalLM.from_pretrained(model_name).config)
-            def torch_call(self, examples):
+    model.source_mapper = source_mapper
-                batch = super().torch_call(examples)
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-                if "source_idx" in examples[0]:
+    model.to(device)
-                    batch["source_idx"] = torch.tensor(
-                        [ex["source_idx"] for ex in examples], 
-                        dtype=torch.int32
-                    )
-                return batch
-
-        config = AutoModelForCausalLM.from_pretrained(model_name).config
-        model = self.LegalModel(model_name, config).to(self.device)
 
     training_args = TrainingArguments(
-            output_dir="./legal_ai_model",
+        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=50,
+        logging_steps=10,
         save_strategy="steps",
-            save_steps=500,
+        save_steps=1000,
         report_to="none",
         remove_unused_columns=False
     )
 
-        class LegalTrainer(Trainer):
+    trainer = Trainer(
-            def compute_loss(self, model, inputs, return_outputs=False):
-                outputs = model(**inputs)
-                loss = outputs["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
-
-        trainer = LegalTrainer(
         model=model,
         args=training_args,
         train_dataset=tokenized_dataset,
         data_collator=CustomDataCollator(tokenizer=tokenizer, mlm=False)
     )
 
-        print("Rozpoczęcie treningu...")
+    print("\nRozpoczęcie treningu...")
     trainer.train()
 
-        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!")
-
-    def generate_response(self, prompt, confidence_threshold=0.65):
-        model = self.LegalModel.from_pretrained(
-            "./trained_legal_ai",
-            config=AutoModelForCausalLM.from_pretrained("crumb/nano-mistral").config
-        ).to(self.device)
-        
-        tokenizer = AutoTokenizer.from_pretrained("./trained_legal_ai")
-        
-        with open("./trained_legal_ai/source_mapper.json", "r") as f:
-            source_mapper = json.load(f)
-
-        inputs = tokenizer(
-            f"[PROMPT] {prompt} [RESPONSE]",
-            return_tensors="pt",
-            max_length=512,
-            truncation=True
-        ).to(self.device)
-
-        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
-            )
-
-        full_text = tokenizer.decode(outputs.sequences[0], skip_special_tokens=True)
-        confidence = torch.sigmoid(outputs.scores[-1][:, tokenizer.eos_token_id]).item()
-        
-        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__":
-    legal_ai = LegalAITrainer()
+    main()
-    
-    legal_ai.train(
-        model_name="crumb/nano-mistral",
-        data_dir="./legal_docs",
-        catalog_path="./catalog.json"
-    )
-    
-    test_prompt = "Jakie są kary za nieprzestrzeganie przepisów RODO?"
-    print(legal_ai.generate_response(test_prompt))