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from fastapi import FastAPI, Query
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
app = FastAPI()
# Load the pre-trained model and tokenizer
model_name = "imalexianne/Movie_Review_Roberta"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# tokenizer = AutoTokenizer.from_pretrained("username/model_name")
# Create a sentiment analysis pipeline
sentiment = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
# Create a dictionary to map sentiment labels to positive and negative strings
sentiment_label_mapping = {
"LABEL_1": "positive",
"LABEL_0": "negative",
}
# Define a request body model
class SentimentRequest(BaseModel):
text: str
# Define a response model
class SentimentResponse(BaseModel):
sentiment: str # 1 for positive, 0 for negative
score: float
@app.get("/")
def read_root():
explanation = {
'message': "Welcome to the Movie Review Sentiment Prediction App",
'description': "This API allows you to predict Movie Review Sentiment based on a given text",
'usage': "Submit a POST request to /predict with text to make predictions.",
}
return explanation
# Create an endpoint for sentiment analysis with query parameter
@app.get("/sentiment/")
async def analyze_sentiment(text: str = Query(..., description="Input text for sentiment analysis")):
result = sentiment(text)
sentiment_label = result[0]["label"]
sentiment_score = result[0]["score"]
sentiment_value = sentiment_label_mapping.get(sentiment_label, -1) # Default to -1 for unknown labels
return SentimentResponse(sentiment=sentiment_value, score=sentiment_score)
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000)
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