Project init

src/model/him_model.py

@@ -6,6 +6,13 @@ from ..core.consciousness_kernel import ConsciousnessKernel
 from ..core.emotional_intelligence import EmotionalProcessor
 from ..core.theory_of_mind import TheoryOfMind
 from ..core.semiotic_processor import SemioticProcessor
+<<<<<<< HEAD
+=======
+import random
+import json
+import os
+from transformers import AutoModelForCausalLM, AutoTokenizer
+>>>>>>> c5fae60 (Initial commit)
 
 class HIMModel(nn.Module):
     def __init__(self, config: Dict[str, Any]):
@@ -16,20 +23,53 @@ class HIMModel(nn.Module):
         self.theory_of_mind = TheoryOfMind()
         self.semiotic_processor = SemioticProcessor()
 
+<<<<<<< HEAD
     async def generate_response(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
         consciousness_state = await self.consciousness_kernel.process_consciousness_cycle(input_data)
         # Fixed awaitable issues by removing await for non-awaitable methods
+=======
+        # Inicializar o modelo de linguagem e o tokenizer para geração de respostas
+        try:
+            model_name = config.get("base_model", "gpt2")
+            self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+            self.language_model = AutoModelForCausalLM.from_pretrained(model_name)
+        except Exception as e:
+            print(f"Erro ao carregar o modelo de linguagem: {e}")
+            # Fallback para um modelo menor caso haja erro
+            self.tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
+            self.language_model = AutoModelForCausalLM.from_pretrained("distilgpt2")
+
+    async def generate_response(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
+        # Extrair o texto da mensagem e o sistema do input
+        message = input_data.get("message", "")
+        system_message = input_data.get("system_message", "You are a helpful assistant.")
+
+        # Obter parâmetros de geração
+        parameters = input_data.get("parameters", {})
+        max_tokens = parameters.get("max_tokens", 512)
+        temperature = parameters.get("temperature", 0.7)
+        top_p = parameters.get("top_p", 0.95)
+
+        # Processar o texto como antes
+        consciousness_state = await self.consciousness_kernel.process_consciousness_cycle(input_data)
+>>>>>>> c5fae60 (Initial commit)
         emotional_context = self.emotional_processor.process_emotional_context(input_data)
         social_understanding = self.theory_of_mind.model_agent_mind(input_data)
         semiotic_analysis = await self.semiotic_processor.process(input_data)
 
+<<<<<<< HEAD
         return self._integrate_outputs(
+=======
+        # Extrair insights dos estados processados para enriquecer a resposta
+        context_insights = self._extract_context_insights(
+>>>>>>> c5fae60 (Initial commit)
             consciousness_state,
             emotional_context,
             social_understanding,
             semiotic_analysis
         )
 
+<<<<<<< HEAD
     def _integrate_outputs(self, *states) -> Dict[str, Any]:
         # Integration implementation
         return {
@@ -38,4 +78,118 @@ class HIMModel(nn.Module):
             "emotional_context": states[1],
             "social_understanding": states[2],
             "semiotic_analysis": states[3]
+=======
+        # Gerar resposta usando o modelo de linguagem
+        response = await self._generate_response_with_lm(
+            message,
+            system_message,
+            context_insights,
+            max_tokens,
+            temperature,
+            top_p
+        )
+
+        return {
+            "response": response,
+            "consciousness_state": consciousness_state,
+            "emotional_context": emotional_context,
+            "social_understanding": social_understanding,
+            "semiotic_analysis": semiotic_analysis
+        }
+
+    def _extract_context_insights(self,
+                               consciousness_state: Dict[str, Any],
+                               emotional_context: Any,
+                               social_understanding: Dict[str, Any],
+                               semiotic_analysis: Any) -> str:
+        """
+        Extrai insights dos estados processados para enriquecer o contexto da resposta.
+        """
+        insights = []
+
+        # Extrair do estado de consciência
+        if isinstance(consciousness_state, dict):
+            if "emotional_tone" in consciousness_state:
+                emotional_tone = consciousness_state.get("emotional_tone", {})
+                if emotional_tone:
+                    insights.append(f"Tone: {json.dumps(emotional_tone)}")
+
+            if "meta_cognition" in consciousness_state:
+                meta = consciousness_state.get("meta_cognition", {})
+                if meta:
+                    insights.append(f"Consider: {json.dumps(meta)}")
+
+        # Extrair do contexto emocional
+        if hasattr(emotional_context, "valence"):
+            valence = getattr(emotional_context, "valence", 0)
+            if valence > 0.3:
+                insights.append("Use a positive and supportive tone.")
+            elif valence < -0.3:
+                insights.append("Address potential concerns compassionately.")
+
+        # Extrair do entendimento social
+        if isinstance(social_understanding, dict):
+            beliefs = social_understanding.get("beliefs", {})
+            if beliefs:
+                insights.append(f"Consider beliefs: {json.dumps(beliefs)}")
+
+        # Combinar insights em uma string de contexto
+        if insights:
+            return "Additional context: " + " ".join(insights)
+        return ""
+
+    async def _generate_response_with_lm(self,
+                                      message: str,
+                                      system_message: str,
+                                      context_insights: str,
+                                      max_tokens: int,
+                                      temperature: float,
+                                      top_p: float) -> str:
+        """
+        Gera uma resposta usando o modelo de linguagem.
+        """
+        try:
+            # Construir o prompt completo
+            prompt = f"{system_message}\n\n"
+            if context_insights:
+                prompt += f"{context_insights}\n\n"
+            prompt += f"User: {message}\nHIM:"
+
+            # Tokenizar o prompt
+            inputs = self.tokenizer(prompt, return_tensors="pt")
+
+            # Gerar a resposta
+            with torch.no_grad():
+                outputs = self.language_model.generate(
+                    inputs["input_ids"],
+                    max_length=inputs["input_ids"].shape[1] + max_tokens,
+                    temperature=temperature,
+                    top_p=top_p,
+                    do_sample=True,
+                    pad_token_id=self.tokenizer.eos_token_id
+                )
+
+            # Decodificar apenas a parte da resposta que é a resposta gerada pelo modelo
+            full_output = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+            response = full_output[len(prompt):]
+
+            # Limpar a resposta (remover quebras de linha extras, etc.)
+            response = response.strip()
+
+            return response
+
+        except Exception as e:
+            print(f"Erro ao gerar resposta com o modelo de linguagem: {e}")
+            # Resposta de fallback em caso de erro
+            return f"Desculpe, tive um problema ao processar sua pergunta: '{message}'. Poderia reformulá-la?"
+
+    def _integrate_outputs(self, *states) -> Dict[str, Any]:
+        # Este método é mantido para compatibilidade com código existente
+        return {
+            "response": "Integrated response based on multiple processing layers",
+            "consciousness_state": states[0] if len(states) > 0 else {},
+            "emotional_context": states[1] if len(states) > 1 else {},
+            "social_understanding": states[2] if len(states) > 2 else {},
+            "semiotic_analysis": states[3] if len(states) > 3 else {}
+>>>>>>> c5fae60 (Initial commit)
         }