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import os
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from transformers import pipeline, AutoTokenizer
from langdetect import detect, DetectorFactory
# Ensure consistent language detection results
DetectorFactory.seed = 0
# Set Hugging Face cache directory to a writable location
os.environ["HF_HOME"] = "/tmp/huggingface"
os.environ["TRANSFORMERS_CACHE"] = "/tmp/huggingface"
# 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.")
app = FastAPI()
# Model names
MULTILINGUAL_MODEL_NAME = "Ehrii/sentiment"
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'
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'
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,
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}")
class SentimentRequest(BaseModel):
text: str
class SentimentResponse(BaseModel):
original_text: str
language_detected: str
sentiment: str
confidence_score: float
def detect_language(text):
"""Detect the language of the given text."""
try:
return detect(text)
except Exception:
return "unknown"
@app.get("/")
def home():
return {"message": "Sentiment Analysis API is running!"}
@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,
sentiment=result[0]["label"].lower(),
confidence_score=result[0]["score"],
) |