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from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
from utils.self_awareness import SelfAwareness
from config import MODEL_NAME, DEVICE
class JAIRVISMKV:
def __init__(self):
self.model = AutoModelForCausalLM.from_pretrained(MODEL_NAME).to(DEVICE)
self.tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
self.self_awareness = SelfAwareness(self)
self.conversation_context = []
self.max_context_length = 1024
def generate_response(self, prompt):
self.conversation_context.append(f"Human: {prompt}")
context = "\n".join(self.conversation_context[-5:])
inputs = self.tokenizer.encode(context + "\nAI:", return_tensors="pt").to(DEVICE)
attention_mask = torch.ones(inputs.shape, dtype=torch.long, device=DEVICE)
outputs = self.model.generate(
inputs,
attention_mask=attention_mask,
max_length=self.max_context_length,
num_return_sequences=1,
no_repeat_ngram_size=2,
top_k=50,
top_p=0.95,
temperature=0.7
)
response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
ai_response = response.split("AI:")[-1].strip()
self.conversation_context.append(f"AI: {ai_response}")
self.self_awareness.update_self_model()
return ai_response
def introspect(self):
return self.self_awareness.generate_insights(str(self))
def implement_improvement(self, suggestion):
return self.self_awareness.implement_improvement(suggestion)
def __str__(self):
return f"JAIRVISMKV Model: {MODEL_NAME}, Device: {DEVICE}"
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