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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)
# 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('🎬 Video Recommender')
# 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?',
('CJ730Y', 'AL707G', 'WS920J', 'YP322C', 'HE156H',),
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()
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