Upload book_metadata_retriever.py

book_metadata_retriever.py

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+
+#pip install rank-bm25
+
+import numpy as np
+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+from sklearn.model_selection import train_test_split
+from rank_bm25 import BM25Okapi
+
+
+# Read CSV file
+data = pd.read_csv(r'C:\book_metadata_retriever\books.csv', encoding='latin1')
+
+class TFIDFDoc2Vec:
+    def __init__(self):
+        self.tfidf_vectorizer = TfidfVectorizer()
+        self.doc_vectors = None
+
+    def initialize_vectors(self, documents):
+        tfidf_matrix = self.tfidf_vectorizer.fit_transform(documents)
+        self.doc_vectors = tfidf_matrix.toarray()
+
+    def find_similar_documents(self, query, top_n=10):
+        query_vector = self.tfidf_vectorizer.transform([query]).toarray()
+        similarities = cosine_similarity(query_vector, self.doc_vectors)
+        similar_indices = similarities.argsort(axis=1)[:, ::-1][:, :top_n]
+
+        similar_documents = []
+        for indices in similar_indices:
+            similar_documents.append(indices)
+        return similar_documents
+
+    def rank_bm25(query, bm25_model, documents, top_n=5):
+        scores = bm25_model.get_scores(query)
+        top_indices = np.argsort(scores)[::-1][:top_n]
+        return top_indices
+
+data
+
+# Select the column containing book titles
+documents = data['Book Title'].astype(str)
+
+# Initialize TF-IDF vectors and model
+tfidf_doc2vec_model = TFIDFDoc2Vec()
+tfidf_doc2vec_model.initialize_vectors(documents)
+
+# Initialize BM25 model
+bm25_model = BM25Okapi(documents.str.split())
+
+
+def answer(query):
+    # Find similar documents
+    similar_documents_indices = tfidf_doc2vec_model.find_similar_documents(query)
+
+    # Rank similar documents using BM25
+    imilar_documents_indices_bm25 = TFIDFDoc2Vec.rank_bm25(query, bm25_model, documents)
+
+    # Print list of similar documents
+    print("Similar documents:")
+    for idx, indices in enumerate(similar_documents_indices):
+        print(f"{idx+1}.")
+        for index in indices:
+            print(f"   Book: {data['Book Title'][index]}")
+            print(f"   Author: {data['Author'][index]}")
+            print(f"   Edition: {data['Edition'][index]}")
+            print(f"   File Name: {data['File_name'][index]}")
+
+            print()
+
+# Receive query from the user
+#query = input("Enter your query: ")
+#answer(query)