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import torch
import gc
from transformers import activations
# Monkeypatch PytorchGELUTanh for AutoAWQ compatibility
if not hasattr(activations, 'PytorchGELUTanh'):
activations.PytorchGELUTanh = activations.NewGELUActivation
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
AutoModelForVision2Seq,
AutoProcessor
)
from diffusers import DiffusionPipeline
from diffusers.utils import export_to_video
from PIL import Image
import requests
import io
from qwen_vl_utils import process_vision_info
import os
class BrainBus:
def __init__(self):
print("Initializing Brain Bus Orchestrator...")
self.device = "cuda" if torch.cuda.is_available() else "cpu"
# Configuration for loading 4-bit models (Orchestrator)
self.bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.float32, # Using float32 for T4 stability
)
# Load the Orchestrator (Math Model) immediately
self.orchestrator_path = "merged_models/math"
self.tokenizer = None
self.orchestrator = None
self._load_orchestrator()
def _load_orchestrator(self):
print(f"Loading Orchestrator from {self.orchestrator_path}...")
try:
self.tokenizer = AutoTokenizer.from_pretrained(self.orchestrator_path)
self.orchestrator = AutoModelForCausalLM.from_pretrained(
self.orchestrator_path,
quantization_config=self.bnb_config,
device_map="auto",
trust_remote_code=True
)
except Exception as e:
print(f"Failed to load orchestrator: {e}")
def _clean_memory(self):
torch.cuda.empty_cache()
gc.collect()
def determine_intent(self, user_input):
# Construct a classification prompt
prompt = (
"Classify the following user query into one of these categories: "
"[CODE, MATH, GENERAL, VISION, VIDEO, 3D]. "
"Return ONLY the category name.\n\n"
f"Query: {user_input}\nCategory:"
)
try:
inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device)
outputs = self.orchestrator.generate(**inputs, max_new_tokens=10)
response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
# Extract the label from the response (simple parsing)
# Remove input prompt from response if model echoes it
if prompt in response:
response = response.replace(prompt, "")
response = response.strip().upper()
# Fallback if generation is verbose
for category in ['CODE', 'MATH', 'GENERAL', 'VISION', 'VIDEO', '3D']:
if category in response:
return category
return "GENERAL" # Default fallback
except Exception as e:
print(f"Error determining intent: {e}")
return "GENERAL"
def run_code_expert(self, query):
print("Loading Code Expert...")
model = None
try:
model = AutoModelForCausalLM.from_pretrained(
"merged_models/code",
quantization_config=self.bnb_config,
device_map="auto",
trust_remote_code=True
)
inputs = self.tokenizer(query, return_tensors="pt").to(self.device)
outputs = model.generate(**inputs, max_new_tokens=256)
result = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
if query in result:
result = result.replace(query, "").strip()
return result
except Exception as e:
return f"Code Expert Error: {e}"
finally:
if model is not None:
del model
self._clean_memory()
def run_general_expert(self, query):
print("Loading General Expert...")
model = None
try:
model = AutoModelForCausalLM.from_pretrained(
"merged_models/normal",
quantization_config=self.bnb_config,
device_map="auto",
trust_remote_code=True
)
inputs = self.tokenizer(query, return_tensors="pt").to(self.device)
outputs = model.generate(**inputs, max_new_tokens=256)
result = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
if query in result:
result = result.replace(query, "").strip()
return result
except Exception as e:
return f"General Expert Error: {e}"
finally:
if model is not None:
del model
self._clean_memory()
def run_math_expert(self, query):
print("Using Orchestrator (Math Expert)...")
# Since the orchestrator IS the math model, use it directly
try:
inputs = self.tokenizer(query, return_tensors="pt").to(self.device)
outputs = self.orchestrator.generate(**inputs, max_new_tokens=256)
result = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
if query in result:
result = result.replace(query, "").strip()
return result
except Exception as e:
return f"Math Expert Error: {e}"
def run_vision_expert(self, query, image_path=None):
print("Loading Vision Expert...")
model = None
try:
# Use specific AWQ model ID
model_id = "Qwen/Qwen2.5-VL-3B-Instruct-AWQ"
# Use AutoModelForVision2Seq to handle Qwen2.5VL architecture
model = AutoModelForVision2Seq.from_pretrained(
model_id,
torch_dtype=torch.float16,
device_map="auto"
)
processor = AutoProcessor.from_pretrained(model_id)
# Setup input
messages = []
content = []
if image_path:
try:
image = Image.open(image_path)
content.append({"type": "image", "image": image})
except:
return "Error loading image."
content.append({"type": "text", "text": query})
messages.append({"role": "user", "content": content})
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
).to(self.device)
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
result = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)[0]
return result
except Exception as e:
return f"Vision Expert Error: {e}"
finally:
if model is not None:
del model
self._clean_memory()
def run_video_expert(self, query):
print("Loading Video Expert...")
pipe = None
try:
# Use fallback model from testing
model_id = "damo-vilab/text-to-video-ms-1.7b"
pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, variant="fp16")
pipe.enable_model_cpu_offload()
# video_frames is list of numpy arrays or PIL images
result = pipe(query, num_inference_steps=20)
video_frames = result.frames[0]
output_path = "generated_video.mp4"
export_to_video(video_frames, output_path, fps=8)
return f"Video generated at {output_path}"
except Exception as e:
return f"Video Expert Error: {e}"
finally:
if pipe is not None:
del pipe
self._clean_memory()
def run_3d_expert(self, query):
print("Loading 3D Expert...")
pipe = None
try:
model_id = "openai/shap-e"
pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe.to("cuda")
_ = pipe(query, num_inference_steps=20)
return "3D Object generated (check output directory)"
except Exception as e:
return f"3D Expert Error: {e}"
finally:
if pipe is not None:
del pipe
self._clean_memory()
def process_query(self, text, image_path=None):
# 1. Determine Intent
print(f"\n[Input]: {text}")
intent = self.determine_intent(text)
print(f"[Intent Detected]: {intent}")
# 2. Route to Expert
response = ""
if intent == "CODE":
response = self.run_code_expert(text)
elif intent == "MATH":
response = self.run_math_expert(text)
elif intent == "VISION":
response = self.run_vision_expert(text, image_path)
elif intent == "VIDEO":
response = self.run_video_expert(text)
elif intent == "3D":
response = self.run_3d_expert(text)
else: # GENERAL
response = self.run_general_expert(text)
return response
if __name__ == "__main__":
# Initialize the bus but don't run a loop yet
bus = BrainBus()
print("Brain Bus ready. Run 'process_query' to interact.")
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