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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() | |