Update api.py

api.py

@@ -4,7 +4,7 @@ from PIL import Image
 import io
 import tensorflow as tf
 import os
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
+# from transformers import AutoTokenizer, AutoModelForSequenceClassification
 # # os.environ['HF_TOKEN']=''
 # from huggingface_hub import login
 
@@ -13,14 +13,14 @@ from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
 
 # Read token from environment
-hf_token = os.getenv("HF_TOKEN")
-print("HF_TOKEN:", hf_token)
+# hf_token = os.getenv("HF_TOKEN")
+# print("HF_TOKEN:", hf_token)
 
 # Load tokenizer directly with the token (no login)
-tokenizer = AutoTokenizer.from_pretrained(
-    "chillies/distilbert-course-review-classification",
-    token=hf_token  # Pass it directly
-)
+# tokenizer = AutoTokenizer.from_pretrained(
+#     "chillies/distilbert-course-review-classification",
+#     token=hf_token  # Pass it directly
+# )
 
 
 
@@ -30,7 +30,7 @@ tokenizer = AutoTokenizer.from_pretrained(
 # tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
 
 
-model = AutoModelForSequenceClassification.from_pretrained("chillies/distilbert-course-review-classification")
+# model = AutoModelForSequenceClassification.from_pretrained("chillies/distilbert-course-review-classification")
 
 
 # from transformers import DistilBertTokenizerFast
@@ -42,6 +42,18 @@ model = AutoModelForSequenceClassification.from_pretrained("chillies/distilbert-
 # model = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")
 
 
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+MODEL_DIR = "/my_model"
+TOKENIZER_DIR = "/my_tokenizer"
+# Load the model and tokenizer
+try:
+  model = AutoModelForSequenceClassification.from_pretrained(MODEL_DIR)
+  tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_DIR)
+  print("Model and tokenizer loaded successfully.")
+except Exception as e:
+  print(f"Error loading model or tokenizer: {e}")
+
+
 
 def inference(review):
   inputs = tokenizer(review, return_tensors="pt", padding=True, truncation=True)