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import gradio as gr
import onnxruntime as ort
from transformers import AutoTokenizer
import numpy as np
# Load the ONNX model
onnx_model_path = "fine-tuned_all-distilroberta-v1_quantized.onnx"
ort_session = ort.InferenceSession(onnx_model_path)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(
"sentence-transformers/all-distilroberta-v1")
def predict_similarity(question, candidate_answer, ai_answer):
# Combine question and answers
candidate_combined = f"Question: {question} Answer: {candidate_answer}"
ai_combined = f"Question: {question} Answer: {ai_answer}"
# Tokenize inputs
inputs = tokenizer([candidate_combined, ai_combined],
padding=True, truncation=True, return_tensors="np")
# Run inference
ort_inputs = {
"input_ids": inputs["input_ids"],
"attention_mask": inputs["attention_mask"]
}
ort_outputs = ort_session.run(None, ort_inputs)
# Get embeddings (shape: (seq_length, 768))
embeddings = ort_outputs[0]
# Apply mean pooling to reduce (seq_length, 768) to (768,)
candidate_embedding = np.mean(embeddings[0], axis=0) # Shape (768,)
ai_embedding = np.mean(embeddings[1], axis=0) # Shape (768,)
# Calculate cosine similarity
similarity = np.dot(candidate_embedding, ai_embedding) / \
(np.linalg.norm(candidate_embedding) * np.linalg.norm(ai_embedding))
return float(similarity)
# Create Gradio interface
iface = gr.Interface(
fn=predict_similarity,
inputs=[
gr.Textbox(label="Coding Question"),
gr.Textbox(label="Candidate's Answer"),
gr.Textbox(label="AI-generated Answer")
],
outputs=gr.Number(label="Similarity Score"),
title="AI Code Detector",
description="Detect similarity between human-written and AI-generated coding answers."
)
# Launch the app
iface.launch()
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