File size: 2,033 Bytes
5e95de5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | def generate_explanation(originality_score: float, similarity_results, query_features) -> str:
explanation = []
explanation.append(f"Originality Score: {originality_score:.2f}%")
if originality_score < 40.0:
explanation.append("Classification: LOW ORIGINALITY (High Duplicate Risk)\n")
elif originality_score < 75.0:
explanation.append("Classification: MEDIUM ORIGINALITY (Moderate Similarity)\n")
else:
explanation.append("Classification: HIGH ORIGINALITY (Novel Idea)\n")
explanation.append("Top Similar Projects in Database:")
# Loop over matched results
for idx, row in similarity_results.iterrows():
title = row.get("project_title", "Unknown Title")
score = row.get("hybrid_score", 0.0) * 100
explanation.append(f"- \"{title}\" (Similarity Score: {score:.1f}%)")
explanation.append("\nConcept Matching Analysis:")
# Highlight matched features of the best candidate
if len(similarity_results) > 0:
best_row = similarity_results.iloc[0]
matched_feats = best_row.get("matched_features", [])
unique_feats = best_row.get("unique_query_features", [])
explanation.append("Matched Concepts (Shared with database):")
if matched_feats:
for item in matched_feats:
if isinstance(item, dict) and 'feature_a' in item and 'feature_b' in item:
explanation.append(f" * \"{item['feature_a']}\" matched with \"{item['feature_b']}\"")
else:
explanation.append(f" * \"{item}\"")
else:
explanation.append(" * No shared concepts found.")
explanation.append("\nUnique Concepts (Exclusive to this project):")
if unique_feats:
for item in unique_feats:
explanation.append(f" * \"{item}\"")
else:
explanation.append(" * No unique concepts identified.")
return "\n".join(explanation)
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