Create app.py

app.py

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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, filepath):
+        try:
+            if filepath.endswith('.csv'):
+                df = pd.read_csv(filepath)
+            elif filepath.endswith(('.xls', '.xlsx')):
+                df = pd.read_excel(filepath)
+            else:
+                raise ValueError("Unsupported file format. Please provide a CSV or Excel file.")
+            return df
+        except FileNotFoundError:
+            raise FileNotFoundError(f"File not found at {filepath}")
+        except ValueError as e:
+            raise ValueError(f"Error loading data: {e}")
+        except Exception as e:
+            raise Exception(f"Error loading data: {e}")
+
+    def preprocess_data(self, df, summary_column='summary', title_column='title'):
+        if df[summary_column].isnull().any():
+            df[summary_column] = df[summary_column].fillna('')
+            print("Handled missing values in summary column.")
+
+        if df[title_column].isnull().any():
+            df[title_column] = df[title_column].fillna('')
+            print("Handled missing values in title column.")
+
+        df = df.drop_duplicates(subset=[title_column, summary_column], keep='first')
+        print("Removed duplicate rows.")
+
+        df = df[~(df[title_column] == '') | (df[summary_column] == '')]
+        print("Removed rows with blank title and summary.")
+
+        return df
+
+    def create_tfidf_matrix(self, df, summary_column='summary'):
+        tfidf = TfidfVectorizer(stop_words='english')
+        tfidf_matrix = tfidf.fit_transform(df[summary_column])
+        return tfidf_matrix, tfidf
+
+    def calculate_similarity(self, tfidf_matrix):
+        similarity_matrix = cosine_similarity(tfidf_matrix)
+        return similarity_matrix
+
+    def recommend_books(self, book_title):
+        try:
+            book_index = self.df[self.df['title'] == book_title].index[0]
+        except IndexError:
+            return "Book title not found."
+        except Exception as e:
+            return f"An error occurred: {e}"
+
+        similar_books_indices = self.similarity_matrix[book_index].argsort()[::-1][1:6] # Fixed top_n to 5
+        recommended_books = self.df['title'].iloc[similar_books_indices].tolist()
+        return recommended_books
+
+    def create_interface(self):
+        def upload_and_process(file_obj):
+            if file_obj is None:
+                return "Please upload a file first.", None
+            filepath = file_obj.name
+            try:
+                self.df = self.load_data(filepath)
+                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)
+            except Exception as e:
+                return f"Error: {e}", None
+
+        def recommend_book_interface(book_title):
+            if self.df is None or self.similarity_matrix is None:
+                return "Please upload and process a file first."
+
+            recommendations = self.recommend_books(book_title)
+            formatted_recommendations = [[rec] for rec in recommendations]
+            return formatted_recommendations
+
+        with gr.Blocks() as iface:
+            file_output = gr.File(label="Upload CSV or Excel file", file_types=[".csv", ".xls", ".xlsx"])
+            process_button = gr.Button("Process File")
+            status_text = gr.Textbox(label="Status")
+            text_input = gr.Textbox(lines=1, placeholder="Enter book title", interactive=False)
+            output_list = gr.List(label="Recommended Books")
+
+            process_button.click(upload_and_process, inputs=file_output, outputs=[status_text, text_input])
+            text_input.change(recommend_book_interface, inputs=text_input, outputs=output_list)
+
+        return iface  # Correct indentation here
+
+if __name__ == '__main__':
+    recommender = BookRecommender()
+    interface = recommender.create_interface()
+    interface.launch()