Addsome changes 2

app.py

@@ -5,7 +5,7 @@ import torch
 from sentence_transformers import SentenceTransformer, util, CrossEncoder
 from recommender import BookRecommender
 # Load book dataset
-df = pd.read_csv("/data/books_summary_cleaned.csv")
+df = pd.read_csv("data/books_summary_cleaned.csv")
 
 # Load precomputed SBERT embeddings
 with open("model/sbert_embeddings2.pkl", "rb") as f:

embeddings.py

@@ -3,7 +3,7 @@ from preprocess import preprocess_books
 import pickle
 import numpy as np
 
-def extract_sbert_embeddings(df, save_path="/model/sbert_embeddings2.pkl"):
+def extract_sbert_embeddings(df, save_path="model/sbert_embeddings2.pkl"):
     """Extracts SBERT embeddings from book summaries."""
     model = SentenceTransformer('all-mpnet-base-v2')  # Small, fast, high-performance
 
@@ -15,7 +15,7 @@ def extract_sbert_embeddings(df, save_path="/model/sbert_embeddings2.pkl"):
 
     return embeddings
 
-def load_book_data(filepath="/data/books_summary_cleaned.csv"):
+def load_book_data(filepath="data/books_summary_cleaned.csv"):
     """Loads book dataset and ensures necessary columns exist."""
     df = pd.read_csv(filepath)
     

preprocess.py

@@ -1,5 +1,3 @@
-import pandas as pd
-
 import pandas as pd
 import re
 
@@ -10,9 +8,8 @@ def clean_text(text):
     text = re.sub(r"\s+", " ", text)  # Remove extra spaces
     text = re.sub(r"[^a-zA-Z0-9.,!?;:()'\" ]", "", text)  # Keep only relevant characters
     return text.strip()
-import pandas as pd
 
-def preprocess_books(input_path="/data/books_summary.csv", output_path="/data/books_summary_cleaned.csv"):
+def preprocess_books(input_path="data/books_summary.csv", output_path="data/books_summary_cleaned.csv"):
     """Preprocesses book dataset by handling duplicates, missing values, and text cleaning."""
     
     # Load dataset

recommender.py

@@ -5,7 +5,7 @@ from sentence_transformers import SentenceTransformer, util
 from embeddings import load_book_data
 
 class BookRecommender:
-    def __init__(self, data_path="/data/books_summary.csv", emb_path="/model/sbert_embeddings2.pkl"):
+    def __init__(self, data_path="data/books_summary.csv", emb_path="/model/sbert_embeddings2.pkl"):
         """Loads book dataset and precomputed embeddings."""
         # from data_loader import load_book_data
         self.df = load_book_data(data_path)