Update main.py

main.py

@@ -1,5 +1,5 @@
 import os
-from fastapi import FastAPI
+from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
 from transformers import pipeline, AutoTokenizer
 from langdetect import detect, DetectorFactory
@@ -12,22 +12,35 @@ os.environ["HF_HOME"] = "/tmp/huggingface"
 os.environ["TRANSFORMERS_CACHE"] = "/tmp/huggingface"
 os.makedirs(os.environ["HF_HOME"], exist_ok=True)
 
+# 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.")
+
 app = FastAPI()
 
 # Model names
-multilingual_model_name = "johndoee/sentiment"
-english_model_name = "siebert/sentiment-roberta-large-english"
+MULTILINGUAL_MODEL_NAME = "johndoee/sentiment"
+ENGLISH_MODEL_NAME = "siebert/sentiment-roberta-large-english"
 
-# Load tokenizer and model for multilingual sentiment analysis
-multilingual_tokenizer = AutoTokenizer.from_pretrained(multilingual_model_name)
-multilingual_model = pipeline(
-    "sentiment-analysis",
-    model=multilingual_model_name,
-    tokenizer=multilingual_tokenizer
-)
+# Load multilingual sentiment model
+try:
+    multilingual_tokenizer = AutoTokenizer.from_pretrained(MULTILINGUAL_MODEL_NAME, use_auth_token=HF_TOKEN)
+    multilingual_model = pipeline(
+        "sentiment-analysis",
+        model=MULTILINGUAL_MODEL_NAME,
+        tokenizer=multilingual_tokenizer,
+        use_auth_token=HF_TOKEN
+    )
+except Exception as e:
+    raise RuntimeError(f"Failed to load multilingual model: {e}")
 
 # Load English sentiment model
-english_model = pipeline("sentiment-analysis", model=english_model_name)
+try:
+    english_model = pipeline("sentiment-analysis", model=ENGLISH_MODEL_NAME, use_auth_token=HF_TOKEN)
+except Exception as e:
+    raise RuntimeError(f"Failed to load English sentiment model: {e}")
 
 class SentimentRequest(BaseModel):
     text: str
@@ -39,6 +52,7 @@ class SentimentResponse(BaseModel):
     confidence_score: float
 
 def detect_language(text):
+    """Detect the language of the given text."""
     try:
         return detect(text)
     except Exception:
@@ -50,7 +64,11 @@ def home():
 
 @app.post("/analyze/", response_model=SentimentResponse)
 def analyze_sentiment(request: SentimentRequest):
-    text = request.text
+    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
@@ -62,4 +80,4 @@ def analyze_sentiment(request: SentimentRequest):
         language_detected=language,
         sentiment=result[0]["label"].lower(),
         confidence_score=result[0]["score"],
-    )
+    )