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import os
import logging
from fastapi import FastAPI
from pydantic import BaseModel
from datetime import datetime
from model import sentiment_model
from prometheus_fastapi_instrumentator import Instrumentator
app = FastAPI()
Instrumentator().instrument(app).expose(app)
if not os.path.exists("logs"):
os.mkdir("logs")
try:
with open("logs/api.log", "w") as f:
f.write("")
except:
Exception("Error creating log file")
exit(1)
# configure logging
logging.basicConfig(
filename="logs/api.log",
level=logging.INFO,
format="%(asctime)s - %(levelname)s - %(message)s"
)
class TextRequest(BaseModel):
text: str
class BatchTextRequest(BaseModel):
texts: list[str]
@app.get("/")
def home():
return { "message": "Sentiment Analyzer API" }
@app.post("/predict/")
def predict_sentiment(request: TextRequest):
start_time = datetime.now()
sentiment = sentiment_model.predict([request.text])[0]
end_time = datetime.now()
response_time = (end_time - start_time).total_seconds()
logging.info(f"Sentiment: {sentiment}, Response Time: {response_time}")
return { "message": sentiment, "response_time": response_time }
@app.post("/predict-batch/")
def predict_sentiments(request: BatchTextRequest):
start_time = datetime.now()
sentiments = sentiment_model.predict(request.texts).tolist()
results = list()
count: int = 0
for sentiment in sentiments:
s = { "text": request.texts[count], "sentiment": sentiment }
results.append(s)
count += 1
end_time = datetime.now()
response_time = (end_time - start_time).total_seconds()
logging.info(f"Response Time: {response_time}")
return {"results": results, "response_time": response_time}
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