Update modules/chatbot.py

modules/chatbot.py

@@ -1,5 +1,25 @@
 from transformers import GPT2LMHeadModel, GPT2Tokenizer
 import torch
+from torch.optim import Adam
+from torch.utils.data import DataLoader, Dataset
+import json
+import tqdm
+
+class MultilingualChatData(Dataset):
+    def __init__(self, file_path, tokenizer, max_length=512):
+        with open(file_path, 'r', encoding='utf-8') as f:
+            self.data = json.load(f)
+        self.tokenizer = tokenizer
+        self.max_length = max_length
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, idx):
+        item = self.data[idx]
+        input_text = f"<startofstring> {item['input']} <bot>: {item['output']} <endofstring>"
+        encoding = self.tokenizer(input_text, truncation=True, padding='max_length', max_length=self.max_length, return_tensors="pt")
+        return encoding['input_ids'].squeeze(), encoding['attention_mask'].squeeze()
 
 class MultilingualChatbot:
     def __init__(self):
@@ -15,19 +35,58 @@ class MultilingualChatbot:
         }
         for tokenizer in self.tokenizers.values():
             tokenizer.pad_token = tokenizer.eos_token
+            tokenizer.add_special_tokens({
+                "bos_token": "<startofstring>",
+                "eos_token": "<endofstring>"
+            })
+            tokenizer.add_tokens(["<bot>:"])
+        
+        for model in self.models.values():
+            model.resize_token_embeddings(len(self.tokenizers['en']))  # Assuming all tokenizers have the same vocabulary size
+        
+        self.device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
+        for model in self.models.values():
+            model.to(self.device)
+
+    def train(self, lang, data_file, epochs=5, batch_size=32, learning_rate=1e-4):
+        model = self.models[lang]
+        tokenizer = self.tokenizers[lang]
+        
+        chat_data = MultilingualChatData(data_file, tokenizer)
+        data_loader = DataLoader(chat_data, batch_size=batch_size, shuffle=True)
+        
+        optimizer = Adam(model.parameters(), lr=learning_rate)
+        
+        model.train()
+        for epoch in range(epochs):
+            total_loss = 0
+            for batch in tqdm.tqdm(data_loader, desc=f"Epoch {epoch+1}/{epochs}"):
+                input_ids, attention_mask = [b.to(self.device) for b in batch]
+                
+                optimizer.zero_grad()
+                outputs = model(input_ids, attention_mask=attention_mask, labels=input_ids)
+                loss = outputs.loss
+                loss.backward()
+                optimizer.step()
+                
+                total_loss += loss.item()
+            
+            print(f"Epoch {epoch+1}/{epochs}, Loss: {total_loss/len(data_loader):.4f}")
+        
+        torch.save(model.state_dict(), f"model_state_{lang}.pt")
 
     def generate_response(self, prompt, src_lang):
-        # Default to English if the language is not supported
         model = self.models.get(src_lang, self.models['en'])
         tokenizer = self.tokenizers.get(src_lang, self.tokenizers['en'])
         
-        input_ids = tokenizer.encode(prompt + tokenizer.eos_token, return_tensors='pt')
+        input_text = f"<startofstring> {prompt} <bot>: "
+        input_ids = tokenizer.encode(input_text, return_tensors='pt').to(self.device)
         
-        # Move input to the same device as the model
-        input_ids = input_ids.to(model.device)
+        attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=self.device)
         
-        chat_history_ids = model.generate(
+        output = model.generate(
             input_ids,
+            attention_mask=attention_mask,
             max_length=1000,
             pad_token_id=tokenizer.eos_token_id,
             no_repeat_ngram_size=3,
@@ -39,7 +98,9 @@ class MultilingualChatbot:
             length_penalty=1.0,
             repetition_penalty=1.2
         )
-        return tokenizer.decode(chat_history_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
+        
+        decoded_output = tokenizer.decode(output[0], skip_special_tokens=True)
+        return decoded_output.split("<bot>:")[-1].strip()
 
 def initialize_chatbot():
     return MultilingualChatbot()
@@ -47,8 +108,14 @@ def initialize_chatbot():
 def get_chatbot_response(chatbot, prompt, src_lang):
     return chatbot.generate_response(prompt, src_lang)
 
-def initialize_chatbot():
-    return MultilingualChatbot()
-
-def get_chatbot_response(chatbot, prompt, src_lang):
-    return chatbot.generate_response(prompt, src_lang)
+# Ejemplo de uso
+if __name__ == "__main__":
+    chatbot = initialize_chatbot()
+    
+    # Entrenar el modelo en español (asumiendo que tienes un archivo de datos en español)
+    chatbot.train('es', './spanish_chat_data.json', epochs=3)
+    
+    # Generar respuestas
+    print(get_chatbot_response(chatbot, "Hola, ¿cómo estás?", 'es'))
+    print(get_chatbot_response(chatbot, "Hello, how are you?", 'en'))
+    print(get_chatbot_response(chatbot, "Bonjour, comment allez-vous?", 'fr'))