Update main.py

main.py

@@ -10,11 +10,14 @@ DetectorFactory.seed = 0
 # Set Hugging Face cache directory to a writable location
 os.environ["HF_HOME"] = "/tmp/huggingface"
 os.environ["TRANSFORMERS_CACHE"] = "/tmp/huggingface"
-os.makedirs(os.environ["HF_HOME"], exist_ok=True)
+
+# Create cache directory with proper permissions
+cache_dir = os.environ["HF_HOME"]
+os.makedirs(cache_dir, exist_ok=True)
+os.chmod(cache_dir, 0o755)  # Set read/write/execute permissions for owner
 
 # Retrieve Hugging Face token from environment variable
 HF_TOKEN = os.getenv("HF_TOKEN")
-
 if not HF_TOKEN:
     raise RuntimeError("Hugging Face token is missing! Please set the HF_TOKEN environment variable.")
 
@@ -26,19 +29,29 @@ ENGLISH_MODEL_NAME = "siebert/sentiment-roberta-large-english"
 
 # Load multilingual sentiment model
 try:
-    multilingual_tokenizer = AutoTokenizer.from_pretrained(MULTILINGUAL_MODEL_NAME, use_auth_token=HF_TOKEN)
+    multilingual_tokenizer = AutoTokenizer.from_pretrained(
+        MULTILINGUAL_MODEL_NAME,
+        token=HF_TOKEN,  # Use 'token' instead of deprecated 'use_auth_token'
+        cache_dir=cache_dir
+    )
     multilingual_model = pipeline(
         "sentiment-analysis",
         model=MULTILINGUAL_MODEL_NAME,
         tokenizer=multilingual_tokenizer,
-        use_auth_token=HF_TOKEN
+        token=HF_TOKEN,  # Use 'token' instead of deprecated 'use_auth_token'
+        cache_dir=cache_dir
     )
 except Exception as e:
     raise RuntimeError(f"Failed to load multilingual model: {e}")
 
 # Load English sentiment model
 try:
-    english_model = pipeline("sentiment-analysis", model=ENGLISH_MODEL_NAME, use_auth_token=HF_TOKEN)
+    english_model = pipeline(
+        "sentiment-analysis",
+        model=ENGLISH_MODEL_NAME,
+        token=HF_TOKEN,  # Use 'token' instead of deprecated 'use_auth_token'
+        cache_dir=cache_dir
+    )
 except Exception as e:
     raise RuntimeError(f"Failed to load English sentiment model: {e}")
 
@@ -65,16 +78,14 @@ def home():
 @app.post("/analyze/", response_model=SentimentResponse)
 def analyze_sentiment(request: SentimentRequest):
     text = request.text.strip()
-
     if not text:
         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)
-
+    
     return SentimentResponse(
         original_text=text,
         language_detected=language,