Update app.py

app.py

@@ -1,50 +1,50 @@
-from flask import Flask, request, jsonify
-from flask_cors import CORS
-import pandas as pd
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.metrics.pairwise import cosine_similarity
-
-app = Flask(__name__)
-CORS(app)
-
-# Load and clean the dataset
-df = pd.read_csv("cleaned.csv")
-df = df[df["Clean_Genre"].notna()].reset_index(drop=True)
-
-# Vectorize genres
-tfidf = TfidfVectorizer(stop_words='english')
-genre_vectors = tfidf.fit_transform(df['Clean_Genre'])
-
-@app.route('/recommend', methods=['POST'])
-def recommend():
-    data = request.get_json()
-    user_input_genre = data.get('genre', '').strip()
-
-    if not user_input_genre:
-        return jsonify({"error": "Genre input is empty."}), 400
-
-    # Transform user input to vector
-    input_vec = tfidf.transform([user_input_genre])
-    similarity_scores = cosine_similarity(input_vec, genre_vectors).flatten()
-
-    # Get top 20 matches by similarity
-    top_indices = similarity_scores.argsort()[-20:][::-1]
-
-    # Get matching books and filter by rating > 0
-    recommended_books = df.iloc[top_indices].copy()
-    recommended_books = recommended_books[recommended_books['average_rating'] > 0]
-
-    # Sort again by rating (optional)
-    recommended_books = recommended_books.sort_values(by='average_rating', ascending=False)
-
-    # Limit to top 10
-    top_books = recommended_books[['Title', 'Author', 'average_rating', 'Clean_Genre']].head(10)
-
-    # Fallback if no good books found
-    if top_books.empty:
-        return jsonify({"message": "No high-rated books found for this genre."}), 200
-
-    return jsonify(top_books.to_dict(orient='records'))
-
-if __name__ == '__main__':
-    app.run(debug=True)
+from flask import Flask, request, jsonify
+from flask_cors import CORS
+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+
+app = Flask(__name__)
+CORS(app)
+
+# Load and clean the dataset 2
+df = pd.read_csv("cleaned.csv")
+df = df[df["Clean_Genre"].notna()].reset_index(drop=True)
+
+# Vectorize genres
+tfidf = TfidfVectorizer(stop_words='english')
+genre_vectors = tfidf.fit_transform(df['Clean_Genre'])
+
+@app.route('/recommend', methods=['POST'])
+def recommend():
+    data = request.get_json()
+    user_input_genre = data.get('genre', '').strip()
+
+    if not user_input_genre:
+        return jsonify({"error": "Genre input is empty."}), 400
+
+    # Transform user input to vector
+    input_vec = tfidf.transform([user_input_genre])
+    similarity_scores = cosine_similarity(input_vec, genre_vectors).flatten()
+
+    # Get top 20 matches by similarity
+    top_indices = similarity_scores.argsort()[-20:][::-1]
+
+    # Get matching books and filter by rating > 0
+    recommended_books = df.iloc[top_indices].copy()
+    recommended_books = recommended_books[recommended_books['average_rating'] > 0]
+
+    # Sort again by rating (optional)
+    recommended_books = recommended_books.sort_values(by='average_rating', ascending=False)
+
+    # Limit to top 10
+    top_books = recommended_books[['Title', 'Author', 'average_rating', 'Clean_Genre']].head(10)
+
+    # Fallback if no good books found
+    if top_books.empty:
+        return jsonify({"message": "No high-rated books found for this genre."}), 200
+
+    return jsonify(top_books.to_dict(orient='records'))
+
+if __name__ == '__main__':
+    app.run(debug=True)