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| from transformers import pipeline | |
| detector = pipeline("text-classification", model="roberta-base-openai-detector") | |
| def clean_text(text: str) -> list: | |
| """ | |
| Splits the input text into clean sentences/lines. | |
| """ | |
| lines = text.strip().split(".") | |
| return [line.strip() for line in lines if line.strip()] | |
| def detect_ai_text(text: str) -> dict: | |
| """ | |
| Detects AI-generated content in the input text. | |
| Returns overall_ai_score, flagged_sentences, and highlighted_html. | |
| """ | |
| lines = clean_text(text) | |
| flagged_sentences = [] | |
| total_ai_score = 0.0 | |
| highlighted_html = "" | |
| for line in lines: | |
| try: | |
| result = detector(line)[0] | |
| label = result["label"] | |
| score = result["score"] | |
| sentence_html = line | |
| if label == "LABEL_1" or label.lower() == "fake": | |
| flagged_sentences.append({ | |
| "sentence": line, | |
| "ai_likelihood": round(score, 2) | |
| }) | |
| total_ai_score += score | |
| # Highlight based on score | |
| if score > 0.7: | |
| sentence_html = f'<span class="bg-red-500/20 border-b-2 border-red-500 rounded px-1">{line}</span>' | |
| elif score > 0.4: | |
| sentence_html = f'<span class="bg-yellow-500/20 border-b-2 border-yellow-500 rounded px-1">{line}</span>' | |
| else: | |
| total_ai_score += score # optional: to keep normalization balanced | |
| highlighted_html += sentence_html + ". " | |
| except Exception as e: | |
| print(f"Error analyzing line: {line[:30]}... | Error: {e}") | |
| highlighted_html += line + ". " | |
| overall_ai_score = round(total_ai_score / len(lines), 2) if lines else 0.0 | |
| return { | |
| "overall_ai_score": overall_ai_score, | |
| "flagged_sentences": flagged_sentences, | |
| "highlighted_html": highlighted_html.strip() | |
| } |