Update backupapp.py

backupapp.py

@@ -2,6 +2,8 @@ import streamlit as st
 import pandas as pd
 import numpy as np
 import tensorflow as tf
+import json
+import os
 
 # Dummy TensorFlow model for demonstration purposes
 def create_model():
@@ -31,18 +33,41 @@ def preprocess_user_preferences(preferences):
     user_data = np.array([preferences['age'], len(preferences['hobbies']), int(preferences['gender'] == "Male"), int(preferences['occupation'] == "Employed")])
     return user_data.reshape(1, -1)
 
-# Main app
+# Function to save user preferences to a text file
+def save_user_preferences(preferences):
+    file_path = f"{preferences['username']}.txt"
+    with open(file_path, 'w') as outfile:
+        json.dump(preferences, outfile)
+
+# Function to load user preferences from a text file
+def load_user_preferences(username):
+    file_path = f"{username}.txt"
+    if os.path.exists(file_path):
+        with open(file_path, 'r') as infile:
+            preferences = json.load(infile)
+        return preferences
+    return None
+
 def main():
     st.title("AI-driven Personalized Experience")
-    st.write("## User Preferences")
     
     preferences = get_user_preferences()
+    
+    if "username" in preferences and preferences["username"]:
+        loaded_preferences = load_user_preferences(preferences["username"])
+        if loaded_preferences:
+            preferences.update(loaded_preferences)
+        else:
+            save_user_preferences(preferences)
+
+    st.write("## User Preferences")
     st.write(preferences)
 
     user_data = preprocess_user_preferences(preferences)
     prediction = model.predict(user_data)
 
     st.write("## AI-driven Personalized Content")
+
     
     st.markdown("### Recommendation Score")
     st.write(f"{prediction[0][0] * 100:.2f}%")
@@ -55,8 +80,10 @@ def main():
         {"Activity": "Gaming Tournament", "Score": np.random.rand()}
     ])
     
+    # Sort activities by score in descending order and take the top 10
+    activities = activities.sort_values(by="Score", ascending=False).head(10)
     activities["Score"] = activities["Score"].apply(lambda x: f"{x * 100:.2f}%")
     st.table(activities)
 
 if __name__ == "__main__":
-    main()
+    main()