Update main.py

main.py

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+# Instalação das dependências necessárias
+!pip install transformers torch gradio datasets
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextDataset, DataCollatorForLanguageModeling, Trainer, TrainingArguments
+import gradio as gr
+import json
+import os
+
+class GameAIAssistant:
+    def __init__(self, model_name="deepseek-ai/DeepSeek-R1"):
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+        self.model = AutoModelForCausalLM.from_pretrained(model_name)
+        self.memory = []
+        self.max_memory = 10
+        self.game_knowledge_file = "game_knowledge.txt"
+        
+        # Carregar conhecimento existente do jogo
+        self.load_game_knowledge()
+
+    def load_game_knowledge(self):
+        if os.path.exists(self.game_knowledge_file):
+            with open(self.game_knowledge_file, 'r', encoding='utf-8') as f:
+                self.game_knowledge = f.read()
+        else:
+            self.game_knowledge = ""
+
+    def save_game_knowledge(self, new_knowledge):
+        with open(self.game_knowledge_file, 'a', encoding='utf-8') as f:
+            f.write(new_knowledge + "\n")
+        self.game_knowledge += new_knowledge + "\n"
+
+    def generate_response(self, user_input):
+        # Combinar memória, conhecimento do jogo e entrada do usuário
+        context = f"""Conhecimento do Jogo:
+{self.game_knowledge}
+
+Histórico de Conversas:
+{' '.join([f'{m["role"]}: {m["content"]}' for m in self.memory[-5:]])}
+
+Usuário: {user_input}
+Assistente:"""
+
+        # Gerar resposta
+        inputs = self.tokenizer(context, return_tensors="pt", max_length=1024, truncation=True)
+        outputs = self.model.generate(
+            inputs["input_ids"],
+            max_length=2048,
+            temperature=0.7,
+            top_p=0.9,
+            pad_token_id=self.tokenizer.eos_token_id
+        )
+        
+        response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+        response = response.split("Assistente:")[-1].strip()
+
+        # Atualizar memória
+        self.memory.append({"role": "user", "content": user_input})
+        self.memory.append({"role": "assistant", "content": response})
+        
+        # Manter apenas as últimas N mensagens
+        if len(self.memory) > self.max_memory:
+            self.memory = self.memory[-self.max_memory:]
+            
+        return response
+
+    def train_on_new_data(self, training_text):
+        # Salvar dados de treinamento
+        with open("train_data.txt", "w", encoding="utf-8") as f:
+            f.write(training_text)
+            
+        # Criar dataset
+        dataset = TextDataset(
+            tokenizer=self.tokenizer,
+            file_path="train_data.txt",
+            block_size=128
+        )
+        
+        data_collator = DataCollatorForLanguageModeling(
+            tokenizer=self.tokenizer,
+            mlm=False
+        )
+        
+        # Configurar treinamento
+        training_args = TrainingArguments(
+            output_dir="./game_ai_model",
+            overwrite_output_dir=True,
+            num_train_epochs=3,
+            per_device_train_batch_size=4,
+            save_steps=10_000,
+            save_total_limit=2,
+        )
+        
+        # Iniciar treinamento
+        trainer = Trainer(
+            model=self.model,
+            args=training_args,
+            data_collator=data_collator,
+            train_dataset=dataset,
+        )
+        
+        trainer.train()
+        
+        # Salvar como conhecimento do jogo
+        self.save_game_knowledge(training_text)
+        
+        return "Treinamento concluído e conhecimento salvo!"
+
+# Inicializar o assistente
+assistant = GameAIAssistant()
+
+# Criar interface Gradio
+with gr.Blocks() as interface:
+    gr.Markdown("# Assistente de IA para Desenvolvimento de Jogos")
+    
+    with gr.Row():
+        with gr.Column():
+            chatbot = gr.Textbox(label="Chat")
+            msg = gr.Textbox(label="Sua mensagem")
+            send = gr.Button("Enviar")
+            
+        with gr.Column():
+            training_data = gr.Textbox(label="Dados de Treinamento", lines=10)
+            train = gr.Button("Treinar IA")
+    
+    # Funções de callback
+    def chat(message):
+        response = assistant.generate_response(message)
+        return response
+    
+    def train_model(text):
+        return assistant.train_on_new_data(text)
+    
+    # Conectar componentes
+    send.click(chat, inputs=msg, outputs=chatbot)
+    train.click(train_model, inputs=training_data, outputs=chatbot)
+
+# Iniciar a interface
+interface.launch(share=True)