Create app.py

app.py

@@ -0,0 +1,37 @@
+mport gradio as gr
+import numpy as np
+import pandas as pd
+from sklearn.metrics.pairwise import cosine_similarity
+
+pt = pickle.load(open('pt.pkl', 'rb'))
+user_similarity_scores = pickle.load(open('user_similarity_scores.pkl', 'rb'))
+
+def recommend_books_for_user(user_id):
+    """Recommends books for a user based on collaborative filtering"""
+    user_index = pt.columns.get_loc(user_id)
+
+    similar_users = sorted(list(enumerate(user_similarity_scores[user_index])), key=lambda x: x[1], reverse=True)[1:5]
+
+    recommended_books = []
+
+    for similar_user_index, similarity_score in similar_users:
+        user_ratings = pt.iloc[:, user_index]
+        similar_user_ratings = pt.iloc[:, similar_user_index]
+
+        unrated_books = similar_user_ratings[(user_ratings == 0) & (similar_user_ratings > 0)]
+
+        recommended_books.extend(unrated_books.index)
+
+    return recommended_books
+
+def recommend_books_gradio(user_id):
+  """Recommends books for a user based on collaborative filtering"""
+  recommended_books = recommend_books_for_user(int(user_id))
+  return recommended_books
+
+interface = gr.Interface(fn=recommend_books_gradio, 
+                         inputs=gr.Textbox(label="Enter User ID"),
+                         outputs=gr.List(label="Recommended Books"),
+                         title="Book Recommender System")
+
+interface.launch()