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import onnxruntime as ort
from transformers import AutoTokenizer
import gradio as gr
# Define available models with their ONNX file paths and tokenizer names
models = {
"DistilBERT": {
"onnx_model_path": "distilbert.onnx",
"tokenizer_name": "distilbert-base-multilingual-cased",
},
"BERT": {
"onnx_model_path": "bert.onnx",
"tokenizer_name": "bert-base-multilingual-cased",
},
"MuRIL": {
"onnx_model_path": "muril.onnx",
"tokenizer_name": "google/muril-base-cased",
},
"RoBERTa": {
"onnx_model_path": "roberta.onnx",
"tokenizer_name": "cardiffnlp/twitter-roberta-base-emotion",
},
}
# Load models and tokenizers into memory
model_sessions = {}
tokenizers = {}
for model_name, config in models.items():
print(f"Loading {model_name}...")
model_sessions[model_name] = ort.InferenceSession(config["onnx_model_path"])
tokenizers[model_name] = AutoTokenizer.from_pretrained(config["tokenizer_name"])
print("All models loaded!")
# Prediction function
def predict_with_model(text, model_name):
# Select the appropriate ONNX session and tokenizer
ort_session = model_sessions[model_name]
tokenizer = tokenizers[model_name]
# Tokenize the input text
inputs = tokenizer(text, return_tensors="np", padding=True, truncation=True)
# Run ONNX inference
outputs = ort_session.run(None, {
"input_ids": inputs["input_ids"],
"attention_mask": inputs["attention_mask"],
})
# Post-process the output
logits = outputs[0]
label = "Hate Speech" if logits[0][1] > logits[0][0] else "Not Hate Speech"
return label
# Define Gradio interface
interface = gr.Interface(
fn=predict_with_model,
inputs=[
gr.Textbox(label="Enter text to classify"),
gr.Dropdown(
choices=list(models.keys()),
label="Select a model",
),
],
outputs="text",
title="Multi-Model Hate Speech Detection",
description="Choose a model and enter text to classify whether it's hate speech.",
)
# Launch the app
if __name__ == "__main__":
interface.launch()
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