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import gradio as gr |
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import torch |
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from torch.nn.functional import softmax |
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import shap |
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import requests |
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from transformers import RobertaTokenizer,RobertaForSequenceClassification, pipeline |
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from IPython.core.display import HTML |
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model_dir = 'temp' |
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tokenizer = RobertaTokenizer.from_pretrained(model_dir) |
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model = RobertaForSequenceClassification.from_pretrained(model_dir) |
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pipe = pipeline("text-classification",model=model,tokenizer=tokenizer) |
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def process_text(input_text, input_file): |
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if input_text: |
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text = input_text |
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elif input_file is not None: |
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text = input_file.read().decode('utf-8') |
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inputs = tokenizer(text, return_tensors="pt") |
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with torch.no_grad(): |
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logits = model(**inputs).logits |
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probs = softmax(logits, dim=1) |
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max_prob, predicted_class_id = torch.max(probs, dim=1) |
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prob = str(round(max_prob.item() * 100, 2)) |
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label = model.config.id2label[predicted_class_id.item()] |
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final_label='Human' if model.config.id2label[predicted_class_id.item()]=='LABEL_0' else 'Chat-GPT' |
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processed_result = text |
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def search(text): |
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query = text |
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api_key = 'AIzaSyClvkiiJTZrCJ8BLqUY9I38WYmbve8g-c8' |
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search_engine_id = '53d064810efa44ce7' |
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url = f'https://www.googleapis.com/customsearch/v1?key={api_key}&cx={search_engine_id}&q={query}' |
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try: |
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response = requests.get(url) |
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data = response.json() |
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return data |
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except Exception as e: |
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return {'error': str(e)} |
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def find_plagiarism(text): |
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search_results = search(text) |
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if 'items' not in search_results: |
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return [] |
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similar_articles = [] |
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for item in search_results['items']: |
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title = item.get('title', '') |
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link = item.get('link', '') |
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similar_articles.append([ title,link]) |
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return similar_articles[:5] |
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prediction = pipe([text]) |
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explainer = shap.Explainer(pipe) |
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shap_values = explainer([text]) |
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shap_plot_html = HTML(shap.plots.text(shap_values, display=False)).data |
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similar_articles = find_plagiarism(text) |
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return processed_result, prob, final_label, shap_plot_html,similar_articles |
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text_input = gr.Textbox(label="Enter text") |
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file_input = gr.File(label="Upload a text file") |
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outputs = [gr.Textbox(label="Processed text"), gr.Textbox(label="Probability"), gr.Textbox(label="Label"), gr.HTML(label="SHAP Plot"),gr.Dataframe(label="Similar Articles", headers=["Title", "Link"],row_count=5)] |
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title = "Group 2- ChatGPT text detection module" |
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description = '''Please upload text files and text input responsibly and await the explainable results. The approach in place includes finetuning a Roberta model for text classification.Once the classifications are done the decision is exaplined thorugh the SHAP text plot. |
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The probability is particularly explained by the attention plots through SHAP''' |
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gr.Interface(fn=process_text,title=title,description=description, inputs=[text_input, file_input], outputs=outputs).launch() |
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