# import pandas as pd
# from sklearn.model_selection import train_test_split
# from sklearn.linear_model import LogisticRegression
# from sklearn.metrics import accuracy_score, classification_report
# import numpy as np
# import os
# import sys
# src_directory = os.path.abspath(os.path.join(os.path.dirname(__file__), "../..", "src"))
# sys.path.append(src_directory)
# from data import sample_data
# from modules import encoding_model 

# file_path = r"src/data/sms_process_data_main.xlsx"
# df = sample_data.get_data_frame(file_path)

# def get_label(message):
#     from sentence_transformers import SentenceTransformer
#     # model = SentenceTransformer('Alibaba-NLP/gte-base-en-v1.5', trust_remote_code=True)
#     X_train, X_test, y_train, y_test = train_test_split(df['MessageText'], df['label'], test_size=0.2, random_state=42)
#     X_train_embeddings = encoding_model.model.encode(X_train.tolist())
#     models = LogisticRegression(max_iter=100)
#     models.fit(X_train_embeddings, y_train)
#     new_embeddings = encoding_model.model.encode(message)
#     no_of_dimention = len(new_embeddings)
#     array = np.array(new_embeddings).tolist()
#     # new_predictions = models.predict(new_embeddings)
#     dimention = pd.DataFrame(array,columns=["Dimention"])
#     return {"Prediction_Dimention":{no_of_dimention: dimention}}

# def create_embending(message:str):
#     embending_message  = encoding_model.model.encode(message)
#     result = np.array(embending_message).tolist()
#     return result