Update main.py

main.py

@@ -7,7 +7,7 @@ from langdetect import detect, DetectorFactory
 # Ensure consistent language detection results
 DetectorFactory.seed = 0
 
-# Use /tmp for Hugging Face cache to avoid permission errors
+# Set Hugging Face cache directory
 os.environ["HF_HOME"] = "/tmp/huggingface_cache"
 os.environ["TRANSFORMERS_CACHE"] = "/tmp/huggingface_cache"
 
@@ -24,23 +24,22 @@ app = FastAPI()
 
 # Model names
 MULTILINGUAL_MODEL_NAME = "Ehrii/sentiment"
-MULTILINGUAL_TOKENIZER_NAME = "tabularisai/multilingual-sentiment-analysis"  # Correct tokenizer
+MULTILINGUAL_TOKENIZER_NAME = "tabularisai/multilingual-sentiment-analysis"
 ENGLISH_MODEL_NAME = "siebert/sentiment-roberta-large-english"
 
 # Load multilingual sentiment model
 try:
     multilingual_tokenizer = AutoTokenizer.from_pretrained(
-        MULTILINGUAL_TOKENIZER_NAME,  # Use correct tokenizer
-        token=HF_TOKEN,
+        MULTILINGUAL_TOKENIZER_NAME,
+        use_auth_token=HF_TOKEN,
         cache_dir=cache_dir
     )
-    
+
     multilingual_model = pipeline(
         "sentiment-analysis",
         model=MULTILINGUAL_MODEL_NAME,
         tokenizer=multilingual_tokenizer,
-        token=HF_TOKEN,
-        cache_dir=cache_dir
+        use_auth_token=HF_TOKEN
     )
 except Exception as e:
     raise RuntimeError(f"Failed to load multilingual model: {e}")
@@ -50,8 +49,7 @@ try:
     english_model = pipeline(
         "sentiment-analysis",
         model=ENGLISH_MODEL_NAME,
-        token=HF_TOKEN,
-        cache_dir=cache_dir
+        use_auth_token=HF_TOKEN
     )
 except Exception as e:
     raise RuntimeError(f"Failed to load English sentiment model: {e}")