app.py

from fastapi import FastAPI

app = FastAPI()

@app.get("/")
def greet_json():
  return {"Hello": "World!"}


from transformers import BertTokenizer, BertForSequenceClassification
import torch

from bertopic import BERTopic

model = BERTopic.load("sleiyer/restricted_item_detector")
# Load the trained model and tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

# Function to predict the class of a single input text
def predict(text):
  # Preprocess the input text
  inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True)

  # Make predictions
  with torch.no_grad():
    outputs = model(**inputs)

  # Get the predicted class
  logits = outputs.logits
  predicted_class = torch.argmax(logits, dim=1).item()

  label_map = {0: 'Allowed Item', 1: 'Restricted Item'}

  # Map the predicted class to a human-readable label
  predicted_label = label_map[predicted_class]

  # Displaying the user input
  return f'The item "{text}" is classified as: "{predicted_label}"'

  return predicted_class

@app.post("/predict")
def predict(input):
  return predict(input)