app.py

from flask import Flask, request, jsonify
from transformers import DistilBertTokenizerFast, TFDistilBertForSequenceClassification
import tensorflow as tf
import numpy as np
import torch

app = Flask(__name__)
device = '/GPU:0' if torch.cuda.is_available() else 'CPU'
model_name = "distilbert-base-uncased"
tokenizer = DistilBertTokenizerFast.from_pretrained(model_name)
model = TFDistilBertForSequenceClassification.from_pretrained(model_name, from_pt=True).signatures['serving_default'].to(device)
session = tf.compat.v1.keras.backend.get_session()

@app.route("/predict", methods=["POST"])
def predict():
    data = request.get_json()
    input_text = data["input"]
    input_ids = torch.tensor(tokenizer.encode(input_text)).unsqueeze(0).to(device)
    
    with torch.no_grad():
        outputs = model(inputs={'input_ids': input_ids}).logits
        probabilities = tf.nn.softmax(outputs).numpy()
        prediction = np.argmax(probabilities)
        
    return jsonify({"response": prediction})

if __name__ == "__main__":
    app.run(debug=True)