remove trainer

app.py

@@ -3,7 +3,7 @@ import numpy as np
 import re
 import time
 
-from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
 from Scraper import Scrap
 
 st.set_page_config(layout="wide")
@@ -15,7 +15,7 @@ label = {0: "valid", 1: "fake"}
 def load_model():
     model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, num_labels=2)
     tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, fast=True)
-    return Trainer(model=model), tokenizer
+    return model, tokenizer
 
 def sigmoid(x):
     return 1 / (1 + np.exp(-x))
@@ -50,9 +50,9 @@ if submit:
             for i in range(text_len // 512):
                 sequences.append(" ".join(text[i * 512: (i + 1) * 512]))
             sequences.append(" ".join(text[text_len - (text_len % 512) : text_len]))
-            sequences = [tokenizer(i, max_length=512, truncation=True, padding="max_length") for i in sequences]
+            sequences = tokenizer(sequences, max_length=512, truncation=True, padding="max_length", return_tensors='pt')
 
-            predictions = model.predict(sequences)[0]
+            predictions = model(**sequences)[0].detach().numpy()
             result = [
                 np.sum([sigmoid(i[0]) for i in predictions]) / len(predictions), 
                 np.sum([sigmoid(i[1]) for i in predictions]) / len(predictions)