Update app.py

app.py

@@ -38,7 +38,7 @@ def recommend_laptops(age=None, category=None, gender=None, user_id=None, num_re
             'Gender': [gender]
         })
         new_user_data = new_user_data.merge(laptop_df, how='cross')
-        new_user_data['Predicted_Rating'] = new_user_data.apply(lambda row: model.predict(999, row['Laptop_ID']).est, axis=1)
+        new_user_data['Predicted_Rating'] = new_user_data.apply(lambda row: model.predict(10002, row['Laptop_ID']).est, axis=1)
         recommendations = new_user_data.sort_values(by='Predicted_Rating', ascending=False).head(num_recommendations)
 
     return recommendations
@@ -62,29 +62,15 @@ if user_type == 'New User':
     if st.button("Get Recommendations"):
         recommendations = recommend_laptops(age=new_user_age, category=new_user_category, gender=new_user_gender)
         st.subheader("Top 5 Recommended Laptops:")
-        for i, row in recommendations.iterrows():
-            st.subheader(f"{i + 1}. {row['name']} - Price: ₹{row['Price (in Indian Rupees)']}")
-            st.markdown(f"**Specifications:**\n"
-                        f"Type: {row['Type']}\n"
-                        f"Dedicated Graphic Memory Capacity: {row['Dedicated Graphic Memory Capacity']}\n"
-                        f"Processor Brand: {row['Processor Brand']}\n"
-                        f"SSD: {row['SSD']}\n"
-                        f"RAM (in GB): {row['RAM (in GB)']}\n"
-                        f"Expandable Memory: {row['Expandable Memory']}\n"
-                        f"Operating System: {row['Operating System']}\n"
-                        f"Touchscreen: {row['Touchscreen']}\n"
-                        f"Screen Size (in inch): {row['Screen Size (in inch)']}\n"
-                        f"Weight (in kg): {row['Weight (in kg)']}\n"
-                        f"Refresh Rate: {row['Refresh Rate']}\n"
-                        f"Screen Resolution: {row['screen_resolution']}\n"
-                        f"Company: {row['company']}\n"
-                        f"Storage: {row['Storage']}\n"
-                        f"Processor Name: {row['Processor name']}\n"
-                        f"CPU Ranking: {row['CPU_ranking']}\n"
-                        f"Battery Backup: {row['battery_backup']}\n"
-                        f"GPU Name: {row['gpu name ']}\n"
-                        f"GPU Benchmark: {row['gpu_benchmark']}\n")
-            st.markdown(f"[Buy Here]({row['link']})")
+        # for i, row in recommendations.iterrows():
+            recommendations_table = recommendations[['Laptop_Name', 'Price (in Indian Rupees)', 'Type', 'Dedicated Graphic Memory Capacity',
+                                          'Processor Brand', 'SSD', 'RAM (in GB)', 'RAM Type', 'Expandable Memory',
+                                          'Operating System', 'Touchscreen', 'Screen Size (in inch)', 'Weight (in kg)',
+                                          'Refresh Rate', 'screen_resolution', 'company', 'Storage', 'Processor name',
+                                          'CPU_ranking', 'battery_backup', 'gpu name ', 'gpu_benchmark',
+                                          'ram_type_tokenized', 'gpu_processor_tokenized', 'link']]
+
+        st.table(recommendations_table)
 
 # User input for existing users
 elif user_type == 'Existing User':
@@ -96,28 +82,14 @@ elif user_type == 'Existing User':
         if existing_user_id:
             recommendations = recommend_laptops(user_id=int(existing_user_id))
             st.subheader(f"Top 5 Recommended Laptops for User {existing_user_id}:")
-            for i, row in recommendations.iterrows():
-                st.subheader(f"{i + 1}. {row['name']} - Price: ₹{row['Price (in Indian Rupees)']}")
-                st.markdown(f"**Specifications:**\n"
-                            f"Type: {row['Type']}\n"
-                            f"Dedicated Graphic Memory Capacity: {row['Dedicated Graphic Memory Capacity']}\n"
-                            f"Processor Brand: {row['Processor Brand']}\n"
-                            f"SSD: {row['SSD']}\n"
-                            f"RAM (in GB): {row['RAM (in GB)']}\n"
-                            f"Expandable Memory: {row['Expandable Memory']}\n"
-                            f"Operating System: {row['Operating System']}\n"
-                            f"Touchscreen: {row['Touchscreen']}\n"
-                            f"Screen Size (in inch): {row['Screen Size (in inch)']}\n"
-                            f"Weight (in kg): {row['Weight (in kg)']}\n"
-                            f"Refresh Rate: {row['Refresh Rate']}\n"
-                            f"Screen Resolution: {row['screen_resolution']}\n"
-                            f"Company: {row['company']}\n"
-                            f"Storage: {row['Storage']}\n"
-                            f"Processor Name: {row['Processor name']}\n"
-                            f"CPU Ranking: {row['CPU_ranking']}\n"
-                            f"Battery Backup: {row['battery_backup']}\n"
-                            f"GPU Name: {row['gpu name ']}\n"
-                            f"GPU Benchmark: {row['gpu_benchmark']}\n")
-                st.markdown(f"[Buy Here]({row['link']})")
+            # for i, row in recommendations.iterrows():
+            recommendations_table = recommendations[['Laptop_Name', 'Price (in Indian Rupees)', 'Type', 'Dedicated Graphic Memory Capacity',
+                                      'Processor Brand', 'SSD', 'RAM (in GB)', 'RAM Type', 'Expandable Memory',
+                                      'Operating System', 'Touchscreen', 'Screen Size (in inch)', 'Weight (in kg)',
+                                      'Refresh Rate', 'screen_resolution', 'company', 'Storage', 'Processor name',
+                                      'CPU_ranking', 'battery_backup', 'gpu name ', 'gpu_benchmark',
+                                      'ram_type_tokenized', 'gpu_processor_tokenized', 'link']]
+
+    st.table(recommendations_table)
         else:
             st.warning("Please enter a valid user ID.")