app.py

import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

# Load the trained model and tokenizer
model_path = 'viv/AIKIA'
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSequenceClassification.from_pretrained(model_path)

# Preprocessing function for Greek text
def preprocessing_greek(text):
    # Your preprocessing steps
    text = text.lower()  # Example step
    return text

# Prediction function
def predict(sentence):
    model.eval()
    preprocessed_sentence = preprocessing_greek(sentence)
    inputs = tokenizer(preprocessed_sentence, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
    logits = outputs.logits
    probabilities = torch.nn.functional.softmax(logits, dim=1)
    predicted_label = torch.argmax(probabilities, dim=1).item()
    labels_map = {0: 'NOT', 1: 'OFFENSIVE'}
    return labels_map[predicted_label], probabilities.tolist()

# Gradio Interface
iface = gr.Interface(fn=predict, inputs="text", outputs=["text", "json"])
iface.launch()