Update main.py

main.py

@@ -1,7 +1,7 @@
 import os
 from fastapi import FastAPI
 from pydantic import BaseModel
-from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
+from transformers import pipeline, AutoTokenizer
 from langdetect import detect, DetectorFactory
 
 # Ensure consistent language detection results
@@ -14,19 +14,20 @@ os.makedirs(os.environ["HF_HOME"], exist_ok=True)
 
 app = FastAPI()
 
-# Load the original tokenizer from the base model
-original_tokenizer = AutoTokenizer.from_pretrained("tabularisai/multilingual-sentiment-analysis")
-mode_name = "johndoee/sentiment"  # or "./sentiment"
+# Model names
+multilingual_model_name = "johndoee/sentiment"
+english_model_name = "siebert/sentiment-roberta-large-english"
 
-# Load the fine-tuned model (johndoee/sentiment) and pass the tokenizer explicitly
+# Load tokenizer and model for multilingual sentiment analysis
+multilingual_tokenizer = AutoTokenizer.from_pretrained(multilingual_model_name)
 multilingual_model = pipeline(
     "sentiment-analysis",
-    model= AutoModelForSequenceClassification.from_pretrained(model_name),
-    tokenizer=original_tokenizer
+    model=multilingual_model_name,
+    tokenizer=multilingual_tokenizer
 )
 
-# Load the English sentiment model
-english_model = pipeline("sentiment-analysis", model="siebert/sentiment-roberta-large-english")
+# Load English sentiment model
+english_model = pipeline("sentiment-analysis", model=english_model_name)
 
 class SentimentRequest(BaseModel):
     text: str
@@ -52,11 +53,9 @@ def analyze_sentiment(request: SentimentRequest):
     text = request.text
     language = detect_language(text)
 
-    # Choose the appropriate model based on language
-    if language == "en":
-        result = english_model(text)
-    else:
-        result = multilingual_model(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,