app.py

from fastapi import FastAPI
from pydantic import BaseModel
from typing import List, Dict, Any
from setfit import SetFitModel

# Define the input model
class TextInput(BaseModel):
    text: List[str]

# Initialize the FastAPI app
app = FastAPI()

# Load the model once, when the app starts
model = SetFitModel.from_pretrained("assets")

@app.post("/predict")
async def predict(input: TextInput):
    # Get the text input from the request
    text_input = input.text
    
    # Initialize a list to store the predictions
    response: List[Dict[str, Any]] = []

    # Predict using the loaded model for each message
    for message in text_input:
        pred = model.predict([message])[0]  # Predict expects a list, so wrap the message in a list and take the first result
        response.append({"Message": message, "label": pred})
    
    return response




@app.get("/")
def read_root():
    return {"message": "Welcome"}