Create comparison1.py

lib/comparison1.py

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+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
+import numpy as np
+
+# Load the pre-trained sentence transformer model
+model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+
+# Get sentence embeddings for a single paragraph
+def get_single_sentence_embedding(paragraph):
+    """Obtain embeddings for a single paragraph using a sentence transformer."""
+    embedding = model.encode([paragraph], convert_to_tensor=True)
+    return embedding
+
+# Get sentence embeddings for a list of paragraphs
+def get_sentence_embeddings(paragraphs):
+    """Obtain embeddings for a list of paragraphs using a sentence transformer."""
+    embeddings = model.encode(paragraphs, convert_to_tensor=True)
+    return embeddings
+
+# Compute similarity matrices over embeddings
+def compute_similarity(embeddings1, embeddings2):
+    """Compute pairwise cosine similarity between two sets of embeddings."""
+    return cosine_similarity(embeddings1.cpu().numpy(), embeddings2.cpu().numpy())
+
+# Compare a single selected paragraph with a list of stored paragraphs
+def compare_selected_paragraph(paragraph, stored_paragraphs):
+    """Compare the selected paragraph with stored paragraphs."""
+    # Get embedding for the selected paragraph
+    embedding1 = get_single_sentence_embedding(paragraph)
+    
+    # Get embeddings for the stored paragraphs
+    embeddings2 = get_sentence_embeddings(stored_paragraphs)
+    
+    # Compute similarity
+    similarity_matrix = compute_similarity(embedding1, embeddings2)
+    
+    # Find the most similar paragraph
+    most_similar_index = np.argmax(similarity_matrix[0])
+    most_similar_paragraph = stored_paragraphs[most_similar_index]
+    similarity_score = similarity_matrix[0][most_similar_index]
+    
+    return f"Most similar paragraph {most_similar_index + 1}: {most_similar_paragraph}\nSimilarity score: {similarity_score:.2f}"