Update main.py

main.py

@@ -25,20 +25,22 @@ app = FastAPI()
 
 # Model names
 MULTILINGUAL_MODEL_NAME = "Ehrii/sentiment"
+MULTILINGUAL_TOKENIZER_NAME = "tabularisai/multilingual-sentiment-analysis"  # Correct tokenizer
 ENGLISH_MODEL_NAME = "siebert/sentiment-roberta-large-english"
 
 # Load multilingual sentiment model
 try:
     multilingual_tokenizer = AutoTokenizer.from_pretrained(
-        MULTILINGUAL_MODEL_NAME,
-        token=HF_TOKEN,  # Use 'token' instead of deprecated 'use_auth_token'
+        MULTILINGUAL_TOKENIZER_NAME,  # Use correct tokenizer
+        token=HF_TOKEN,
         cache_dir=cache_dir
     )
+    
     multilingual_model = pipeline(
         "sentiment-analysis",
         model=MULTILINGUAL_MODEL_NAME,
         tokenizer=multilingual_tokenizer,
-        token=HF_TOKEN,  # Use 'token' instead of deprecated 'use_auth_token'
+        token=HF_TOKEN,
         cache_dir=cache_dir
     )
 except Exception as e:
@@ -49,7 +51,7 @@ try:
     english_model = pipeline(
         "sentiment-analysis",
         model=ENGLISH_MODEL_NAME,
-        token=HF_TOKEN,  # Use 'token' instead of deprecated 'use_auth_token'
+        token=HF_TOKEN,
         cache_dir=cache_dir
     )
 except Exception as e:
@@ -82,7 +84,6 @@ def analyze_sentiment(request: SentimentRequest):
         raise HTTPException(status_code=400, detail="Text input cannot be empty.")
     
     language = detect_language(text)
-    # Choose the appropriate model based on detected language
     model = english_model if language == "en" else multilingual_model
     result = model(text)
     
@@ -91,4 +92,4 @@ def analyze_sentiment(request: SentimentRequest):
         language_detected=language,
         sentiment=result[0]["label"].lower(),
         confidence_score=result[0]["score"],
-    )
+    )