first version

app.py

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+import streamlit as st
+import hopsworks
+from mimesis import Generic
+from mimesis.locales import Locale
+import pandas as pd
+import random
+
+# Function to print a styled header
+def print_header(text, font_size=22):
+    res = f'<span style=" font-size: {font_size}px;">{text}</span>'
+    st.markdown(res, unsafe_allow_html=True)
+
+# Function to retrieve and start model deployments
+@st.cache_resource()
+def get_deployments():
+    # Displaying a message indicating the process has started
+    st.write("🚀 Retrieving and Starting Deployments...")
+
+    # Logging into the Hopsworks project
+    project = hopsworks.login()
+
+    fs = project.get_feature_store()
+
+    interactions_fg = fs.get_feature_group(
+        name="interactions",
+        version=1,
+    )
+
+    videos_fg = fs.get_feature_group(
+        name="videos",
+        version=1,
+    )
+
+    # Getting the model serving instance from the project
+    ms = project.get_model_serving()
+
+    # Retrieving deployments for the query model and ranking model
+    query_model_deployment =  ms.get_deployment("querydeployment")
+    ranking_deployment =  ms.get_deployment("rankingdeployment")
+
+    # Starting the ranking deployment with a maximum waiting time of 180 seconds
+    ranking_deployment.start(await_running=180)
+
+    # Starting the query model deployment with a maximum waiting time of 180 seconds
+    query_model_deployment.start(await_running=180)
+
+    # Displaying a message indicating that deployments are ready
+    st.write('✅ Deployments are ready!')
+
+    # Returning deployment instances
+    return interactions_fg, videos_fg, ranking_deployment, query_model_deployment
+
+def insert_interaction(user_id, video_id, interactions_fg):
+    generic = Generic(locale=Locale.EN)
+    interaction_id = generic.person.identifier(mask='####-##-####')
+    interaction_type = random.choices(
+        ['like', 'dislike', 'view', 'comment', 'share', 'skip'],
+        weights=[1.5, 0.2, 3, 0.5, 0.8, 10], k=1
+    )[0]
+    watch_time = random.randint(1, 50)
+
+    interaction_df = pd.DataFrame({
+        'interaction_id': [interaction_id],
+        'interaction_type': [interaction_type],
+        'user_id': [user_id],
+        'video_id': [video_id],
+        'watch_time': [watch_time]
+    })
+
+    interactions_fg.insert(interaction_df, write_options={"start_offline_materialization": False})
+
+
+# Define function to fetch recommendations
+def fetch_recommendations(user_id, query_model_deployment):
+    st.write('🔮 Getting recommendations...')
+    deployment_input = {"instances": {"user_id": user_id}}
+    prediction = query_model_deployment.predict(deployment_input)['predictions']['ranking']
+    return prediction
+
+# Function to insert interaction and fetch new recommendations
+def handle_interaction(user_id, video_id, interactions_fg, query_model_deployment):
+    insert_interaction(user_id, video_id, interactions_fg)
+    return fetch_recommendations(user_id, query_model_deployment)
+
+
+# Main Streamlit application logic
+def main():
+    st.title('🎬 Tiktok Personalized Video Recommendations')
+    # Initialize or re-use existing deployments
+    if 'deployments_initialized' not in st.session_state:
+        st.session_state.interactions_fg, st.session_state.videos_fg, st.session_state.ranking_deployment, st.session_state.query_model_deployment = get_deployments()
+        st.session_state['deployments_initialized'] = True
+
+    # User selection box
+    user_id_option = st.selectbox(
+        'For which user?',
+        ('SW104K', 'RA693D', 'DR282A', 'SN496G',),
+        key='user_select'
+    )
+
+    # Initialize or refresh recommendations
+    if 'recommendations' not in st.session_state or 'refresh' in st.session_state:
+        recommendations = fetch_recommendations(user_id_option, st.session_state.query_model_deployment)
+        random.shuffle(recommendations)
+        st.session_state.recommendations = recommendations
+        st.session_state.pop('refresh', None)
+
+    print_header('📝 Top 3 Recommendations:')
+    displayed_recommendations = st.session_state.recommendations[:3]
+    for recommendation in displayed_recommendations:
+        video_id = recommendation[1]
+        if st.button(f"🔗 Video ID: {video_id}", key=video_id):
+            new_recommendations = handle_interaction(
+                user_id_option,
+                video_id,
+                st.session_state.interactions_fg,
+                st.session_state.query_model_deployment,
+            )
+            random.shuffle(new_recommendations)
+            st.session_state.recommendations = new_recommendations
+            st.experimental_rerun()
+
+    if st.button("Stop Streamlit"):
+        st.write('⚙️ Stopping Deployments...')
+        st.session_state.ranking_deployment.stop()
+        st.session_state.query_model_deployment.stop()
+        st.success('✅ App finished successfully!')
+        st.stop()
+
+if __name__ == '__main__':
+    main()

requirements.txt

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+mimesis==15.1.0
+tensorflow==2.13
+tensorflow-recommenders==0.7.2
+catboost==1.2.1
+streamlit==1.33.0
+hopsworks