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DockerFile

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+FROM python:3.9-slim-buster
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install -r requirements.txt
+
+COPY . .
+
+EXPOSE 5000
+
+CMD ["gunicorn", "--bind", "0.0.0.0:5000", "app:app"] 
+# OR, for development/testing (not recommended for production):
+# CMD ["python", "app.py"]

README.md

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----
-title: Book Recommender
-emoji: 👀
-colorFrom: indigo
-colorTo: indigo
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Book Recommender (Flask Version)
+
+This project implements a content-based book recommendation system using Python, Flask, and scikit-learn. It allows users to upload a CSV or Excel file containing book titles and summaries, and then enter a book title to receive recommendations for similar books.
+
+## Dependencies
+
+- Flask: Used for creating the web application.
+- pandas: Used for data loading and manipulation.
+- scikit-learn: Used for TF-IDF vectorization and cosine similarity calculation.
+
+You can install these dependencies using pip:
+
+```bash
+pip install Flask pandas scikit-learn

app.py

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+from flask import Flask, render_template, request, jsonify
+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+
+app = Flask(__name__)
+
+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 load_and_process_data(self, filepath):
+        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 True
+        except Exception as e:
+            print(f"Error during data loading/processing: {e}")
+            return False
+
+
+recommender = BookRecommender()
+
+@app.route("/", methods=["GET", "POST"])
+def index():
+    message = ""
+    recommendations = None # Initialize recommendations
+    if request.method == "POST":
+        if 'file' in request.files:
+            file = request.files['file']
+            if file.filename != '':
+                try:
+                    filepath = "uploaded_file." + file.filename.rsplit('.', 1)[1]
+                    file.save(filepath)
+                    if recommender.load_and_process_data(filepath):
+                        message = "File uploaded and processed successfully!"
+                    else:
+                        message = "Error processing the file."
+                except Exception as e:
+                    message = f"File upload failed: {e}"
+            else:
+                message = "No file selected."
+
+        elif 'book_title' in request.form:
+            book_title = request.form['book_title']
+            if recommender.df is None or recommender.similarity_matrix is None:
+                message = "Please upload and process a file first."
+            else:
+                recommendations = recommender.recommend_books(book_title)
+                if isinstance(recommendations, str): # Check if it is an error message.
+                    message = recommendations
+                else:
+                    message = "" # Clear any previous messages.
+    return render_template("index.html", message=message, recommendations=recommendations)
+
+
+if __name__ == "__main__":
+    app.run(debug=True)