q Directions
1. Mix 2 flours and baking powder together
2. Mix water and egg in a separate bowl. Add dry to wet little by little
3. Heat frying pan on medium
4. Pour batter into pan and then put blueberries on top before flipping
5. Top with desired toppings!

Food_and_Drink

import torch
import torch_npu
from torch import nn
from openmind import AutoModel, AutoTokenizer, AutoConfig
import argparse
from openmind import pipeline, is_torch_npu_available
from PIL import Image
import requests

def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--model_name_or_path",
        type=str,
        help="Path to model",
        default=None,
    )
    args = parser.parse_args()
    return args

class CustomModel(nn.Module):
    def __init__(self, config, model_path):
        super(CustomModel, self).__init__()
        self.model = AutoModel.from_pretrained(model_path + "/" + config['base_model'])
        self.dropout = nn.Dropout(config['fc_dropout'])
        self.fc = nn.Linear(self.model.config.hidden_size, len(config['id2label']))

    def forward(self, input_ids, attention_mask):
        features = self.model(input_ids=input_ids, attention_mask=attention_mask).last_hidden_state
        dropped = self.dropout(features)
        outputs = self.fc(dropped)
        return torch.softmax(outputs[:, 0, :], dim=1)

def main():
    args = parse_args()
    model_path = args.model_name_or_path
    
    if is_torch_npu_available():
        device = "npu:0"
    else:
        device = "cpu"

    # Setup configuration and model
    config = AutoConfig.from_pretrained(model_path)
    tokenizer = AutoTokenizer.from_pretrained(model_path)
    model = CustomModel(config.to_dict(), model_path)

    # Prepare and process inputs
    text_samples = ["Sports is a popular domain", "Politics is a popular domain"]
    inputs = tokenizer(text_samples, return_tensors="pt", padding="longest", truncation=True)
    outputs = model(inputs['input_ids'], inputs['attention_mask'])

    # Predict and display results
    predicted_classes = torch.argmax(outputs, dim=1)
    predicted_domains = [config.id2label[class_idx.item()] for class_idx in predicted_classes.cpu().numpy()]
    print(predicted_domains)
if __name__=="__main__":
    main()