Update app.py

app.py

@@ -1,15 +1,24 @@
-from flask import Flask, request, render_template_string, jsonify
-from transformers import AutoModelForSequenceClassification, XLMRobertaTokenizer
+from flask import Flask, request, render_template_string, jsonify, send_from_directory
+import requests
+import pandas as pd
+import re
+import time
+from random import randint, choice
+import os
+from transformers import XLMRobertaForSequenceClassification, XLMRobertaTokenizer
+from peft import PeftModel, PeftConfig  # Ensure peft library is installed
 import torch
+from collections import defaultdict
 
 
 # Define the Flask app
 flask_app = Flask(__name__)
 
-# Load the pre-trained model and tokenizer
-MODEL_NAME = "letijo03/xlm-r-shopee"
-tokenizer = XLMRobertaTokenizer.from_pretrained(MODEL_NAME)
-model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
+ Load the base XLM-RoBERTa model with the correct number of labels (3 labels for classification)
+tokenizer = XLMRobertaTokenizer.from_pretrained("letijo03/lora-adapter-32",use_fast=True, trust_remote_code=True)
+base_model = XLMRobertaForSequenceClassification.from_pretrained("xlm-roberta-base", num_labels=3)
+config = PeftConfig.from_pretrained("letijo03/lora-adapter-32")
+model = PeftModel.from_pretrained(base_model, "letijo03/lora-adapter-32")
 model.eval()  # Set the model to evaluation mode
 
 def classify_sentiment(text):