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import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import gradio as gr
class BookRecommender:
def __init__(self):
self.df = None
self.similarity_matrix = None
def load_data(self, file_obj):
try:
if file_obj.name.endswith('.csv'):
df = pd.read_csv(file_obj)
elif file_obj.name.endswith(('.xls', '.xlsx')):
df = pd.read_excel(file_obj)
else:
raise ValueError("Unsupported file format. Please provide a CSV or Excel file.")
return df
except Exception as e:
return str(e)
def preprocess_data(self, df):
df['summary'] = df['summary'].fillna('')
df['title'] = df['title'].fillna('')
df = df.drop_duplicates(subset=['title', 'summary'])
return df
def create_tfidf_matrix(self, df):
tfidf = TfidfVectorizer(stop_words='english')
tfidf_matrix = tfidf.fit_transform(df['summary'])
return tfidf_matrix
def calculate_similarity(self, tfidf_matrix):
return cosine_similarity(tfidf_matrix)
def recommend_books(self, book_title):
if self.df is None or self.similarity_matrix is None:
return ["Please upload and process a file first."]
try:
book_index = self.df[self.df['title'] == book_title].index[0]
except IndexError:
return ["Book title not found."]
similar_books_indices = self.similarity_matrix[book_index].argsort()[::-1][1:6]
return self.df['title'].iloc[similar_books_indices].tolist()
def create_interface(self):
def process_file(file_obj):
if file_obj is None:
return "Please upload a file first.", None
self.df = self.load_data(file_obj)
self.df = self.preprocess_data(self.df)
tfidf_matrix = self.create_tfidf_matrix(self.df)
self.similarity_matrix = self.calculate_similarity(tfidf_matrix)
return "File uploaded and processed successfully!", gr.update(interactive=True)
def recommend_interface(book_title):
recommendations = self.recommend_books(book_title)
return recommendations
with gr.Blocks() as iface:
file_input = gr.File(label="Upload CSV or Excel file")
process_button = gr.Button("Process File")
status_text = gr.Textbox(label="Status", interactive=False)
text_input = gr.Textbox(lines=1, placeholder="Enter book title", interactive=False)
output_list = gr.Textbox(label="Recommended Books", interactive=False)
process_button.click(process_file, inputs=file_input, outputs=[status_text, text_input])
text_input.submit(recommend_interface, inputs=text_input, outputs=output_list)
return iface
recommender = BookRecommender()
interface = recommender.create_interface()
interface.launch()
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