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Browse files- README.md +1 -1
- app.py +53 -0
- fine-tuned_all-distilroberta-v1_quantized.onnx +3 -0
- requirements.txt +4 -0
README.md
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sdk: gradio
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sdk_version:
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app_file: app.py
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pinned: false
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license: mit
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colorFrom: purple
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sdk: gradio
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sdk_version: 3.50.0
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app_file: app.py
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pinned: false
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license: mit
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app.py
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import gradio as gr
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import onnxruntime as ort
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from transformers import AutoTokenizer
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import numpy as np
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# Load the ONNX model
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onnx_model_path = "fine-tuned_all-distilroberta-v1_quantized.onnx"
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ort_session = ort.InferenceSession(onnx_model_path)
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# Load the tokenizer
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tokenizer = AutoTokenizer.from_pretrained(
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"sentence-transformers/all-MiniLM-L6-v2")
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def predict_similarity(question, candidate_answer, ai_answer):
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# Combine question and answers
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candidate_combined = f"Question: {question} Answer: {candidate_answer}"
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ai_combined = f"Question: {question} Answer: {ai_answer}"
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# Tokenize inputs
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inputs = tokenizer([candidate_combined, ai_combined],
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padding=True, truncation=True, return_tensors="np")
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# Run inference
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ort_inputs = {
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"input_ids": inputs["input_ids"],
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"attention_mask": inputs["attention_mask"]
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}
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ort_outputs = ort_session.run(None, ort_inputs)
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# Calculate cosine similarity
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embeddings = ort_outputs[0]
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similarity = np.dot(embeddings[0], embeddings[1]) / \
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(np.linalg.norm(embeddings[0]) * np.linalg.norm(embeddings[1]))
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return float(similarity)
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# Create Gradio interface
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iface = gr.Interface(
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fn=predict_similarity,
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inputs=[
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gr.Textbox(label="Coding Question"),
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gr.Textbox(label="Candidate's Answer"),
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gr.Textbox(label="AI-generated Answer")
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],
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outputs=gr.Number(label="Similarity Score"),
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title="AI Code Detector",
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description="Detect similarity between human-written and AI-generated coding answers."
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)
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# Launch the app
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iface.launch()
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fine-tuned_all-distilroberta-v1_quantized.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:b3df341367812546bc086c5d3fa809bbde3a35dc89cc34cd5bff32dd0bd711f3
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size 82520985
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requirements.txt
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gradio==3.50.0
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onnxruntime==1.19.2
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torch==2.4.1
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transformers==4.44.2
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