Create backupapp.py

backupapp.py

@@ -0,0 +1,62 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import tensorflow as tf
+
+# Dummy TensorFlow model for demonstration purposes
+def create_model():
+    model = tf.keras.Sequential([
+        tf.keras.layers.Dense(8, activation='relu', input_shape=(4,)),
+        tf.keras.layers.Dense(4, activation='relu'),
+        tf.keras.layers.Dense(1, activation='sigmoid')
+    ])
+    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+    return model
+
+model = create_model()
+
+# Function to get user preferences
+def get_user_preferences():
+    preferences = {
+        "age": st.sidebar.number_input("Age", min_value=0, max_value=120, value=30),
+        "gender": st.sidebar.selectbox("Gender", options=["Male", "Female", "Other"]),
+        "hobbies": st.sidebar.multiselect("Hobbies", options=["Sports", "Reading", "Travel", "Cooking", "Gaming"]),
+        "occupation": st.sidebar.selectbox("Occupation", options=["Student", "Employed", "Unemployed", "Retired"])
+    }
+    return preferences
+
+# Function to preprocess user preferences for TensorFlow model
+def preprocess_user_preferences(preferences):
+    # Preprocess the user data as needed for your specific model
+    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
+def main():
+    st.title("AI-driven Personalized Experience")
+    st.write("## User Preferences")
+    
+    preferences = get_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}%")
+
+    st.markdown("### Recommended Activities")
+    activities = pd.DataFrame([
+        {"Activity": "Outdoor Adventure", "Score": np.random.rand()},
+        {"Activity": "Book Club", "Score": np.random.rand()},
+        {"Activity": "Cooking Class", "Score": np.random.rand()},
+        {"Activity": "Gaming Tournament", "Score": np.random.rand()}
+    ])
+    
+    activities["Score"] = activities["Score"].apply(lambda x: f"{x * 100:.2f}%")
+    st.table(activities)
+
+if __name__ == "__main__":
+    main()