Update app.py

app.py

@@ -5,19 +5,19 @@ import os
 import json
 import random
 import gradio as gr
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import DataLoader, TensorDataset, IterableDataset
 from sklearn.ensemble import IsolationForest, RandomForestClassifier
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import OneHotEncoder
 from sklearn.neural_network import MLPClassifier
 from deap import base, creator, tools, algorithms
 from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
-import torch
-import torch.nn as nn
-import torch.optim as optim
-from torch.utils.data import DataLoader, TensorDataset, IterableDataset
+import gc
 import multiprocessing as mp
 from joblib import Parallel, delayed
-import gc
 
 warnings.filterwarnings('ignore', category=FutureWarning, module='huggingface_hub.file_download')
 
@@ -233,8 +233,10 @@ def generate_text(prompt, max_length=100):
     generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
     return generated_text
 
-sentiment_pipeline = pipeline("sentiment-analysis", model='distilbert-base-uncased-finetuned-sst-2-english', device_map="auto")
-
+model_name = "distilbert-base-uncased-finetuned-sst-2-english"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+sentiment_pipeline = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
 def get_sentiment(text):
     result = sentiment_pipeline(text)[0]
     return f"Sentiment: {result['label']}, Score: {result['score']:.4f}"
@@ -288,3 +290,4 @@ iface = gr.Interface(
 )
 
 iface.launch()
+