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#https://colab.research.google.com/drive/1MXxS7wCsldrRngOF6Su9BqM7KSKQ0Hvv?authuser=0#scrollTo=Ew_gPI3mN-zF
    
    
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load the tokenizer and model
# Using 'gemma-2b-it' as a common 'smaller gemma' model for demonstration
# You can change this to 'google/gemma-7b-it' for a larger version
model_id = "google/gemma-4-E4B-it"

vishalsingha

def make_prediction(X, clf_path, class_encoding_path, std_path, col_seq_path, good_features_path ):   
    X = pd.DataFrame(X.values.reshape(1, -1), columns = X.index, dtype = float)
    kingdom_classes = ['Archea', 'Bacteria', 'Eukaryots']
    
    
    #load class encoding
    with open(class_encoding_path, 'rb') as file:
        le = pickle.load(file)
    file.close()
    

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