Update main.py

main.py

@@ -1,29 +1,57 @@
 import os
-from fastapi import FastAPI
+from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
 from transformers import pipeline
+import langdetect
 
 # Set custom cache directory to avoid permission issues
 os.environ["TRANSFORMERS_CACHE"] = "/app/cache"
 
 app = FastAPI()
 
-# Explicitly specify a model (avoid default selection)
-MODEL_NAME = "distilbert-base-uncased-finetuned-sst-2-english"
-sentiment_pipeline = pipeline("sentiment-analysis", model=MODEL_NAME)
+# Load sentiment analysis models
+multilingual_model = pipeline("sentiment-analysis", model="tabularisai/multilingual-sentiment-analysis")
+english_model = pipeline("sentiment-analysis", model="siebert/sentiment-roberta-large-english")
 
 class SentimentRequest(BaseModel):
     text: str
 
 class SentimentResponse(BaseModel):
-    label: str
-    score: float
+    original_text: str
+    language_detected: str
+    sentiment: str
+    confidence_score: float
+
+def detect_language(text: str) -> str:
+    try:
+        return langdetect.detect(text)
+    except:
+        return "unknown"
 
 @app.get("/")
 def home():
     return {"message": "Sentiment Analysis API is running!"}
 
-@app.post("/predict/", response_model=SentimentResponse)
-def predict(request: SentimentRequest):
-    result = sentiment_pipeline(request.text)
-    return SentimentResponse(label=result[0]['label'], score=result[0]['score'])
+@app.post("/analyze/", response_model=SentimentResponse)
+def analyze_sentiment(request: SentimentRequest):
+    if not request.text:
+        raise HTTPException(status_code=400, detail="No text provided")
+    
+    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)
+    
+    sentiment = result[0]["label"].lower()
+    score = result[0]["score"]
+    
+    return SentimentResponse(
+        original_text=text,
+        language_detected=language,
+        sentiment=sentiment,
+        confidence_score=score
+    )