Recognize Industrial Components with GLM-4.5V and Hugging Face Transformers

                      
                     
import os
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Configuration
MODEL_NAME = os.getenv('MODEL_NAME', 'GLM-4.5V')  # Use environment variable for model name
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'

# Load model and tokenizer
try:
    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
    model = AutoModelForCausalLM.from_pretrained(MODEL_NAME).to(DEVICE)
except Exception as e:
    raise RuntimeError(f'Error loading model: {str(e)}')

# Function to recognize components
def recognize_component(input_text: str) -> str:
    try:
        inputs = tokenizer(input_text, return_tensors='pt').to(DEVICE)
        output = model.generate(**inputs)
        recognized_text = tokenizer.decode(output[0], skip_special_tokens=True)
        return recognized_text
    except Exception as e:
        return f'Error recognizing component: {str(e)}'

# Main execution
if __name__ == '__main__':
    test_input = 'Identify the motor and sensor specifications.'
    result = recognize_component(test_input)
    print(f'Recognized Component: {result}')