run_model.py

import sys
import torch
from transformers import BertTokenizer, BertForSequenceClassification

# Directory where the model and tokenizer files are located
model_dir = "C:/Users/Biruh/amharic_hate_speech_detection/amharic-hate-speech-backend"  # Update path if necessary

# Load tokenizer and model
tokenizer = BertTokenizer.from_pretrained(model_dir)
model = BertForSequenceClassification.from_pretrained(model_dir)

# Get the text input from the command-line arguments
if len(sys.argv) > 1:
    text = sys.argv[1]
else:
    text = "ምንም ጽሁፍ አልተሰጠም"  # Default text in case no input is provided

# Tokenize the input text
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)

# Make prediction without gradients
with torch.no_grad():
    outputs = model(**inputs)

# Extract the prediction (the logits) and convert it to a class (1 = Hate Speech, 0 = Non-Hate Speech)
logits = outputs.logits
prediction = torch.argmax(logits, dim=-1).item()

# Output the prediction result
print("Hate Speech" if prediction == 1 else "Non-Hate Speech")