recommender_streamlit.py

import streamlit as st
import time
import session_state
from datetime import datetime, timedelta
from util import get_db_connection
from PIL import Image
import ray

def retry_until_success(f, timeout=15):
    end = datetime.now() + timedelta(seconds=timeout)
    while True:
        try:
            r = f()
            return r
        except Exception as e:
            if datetime.now() < end:
                time.sleep(1)
                continue
            raise e


def rerun():
    raise st.script_runner.RerunException(
        st.script_request_queue.RerunData(None))


if __name__ == "__main__":
	# Draw a title and some text to the app:
	'''
	# Movie recommendations!
	'''
	ray.init(address="auto", ignore_reinit_error=True)
	state = session_state.get(liked_image_id="")

	progress_bar = st.sidebar.progress(0)
	status_text = st.sidebar.empty()
	img_width = st.sidebar.slider("Image size", 10, 300, 100)
	num_movies = st.sidebar.slider("Number of movies", 1, 20, 5)
	num_cols = st.sidebar.slider("Number of columns", 1, 5, 2)
	pick_img = st.sidebar.radio(
	    "Which movie?", [x for x in range(1, num_movies + 1)])
	cols = st.beta_columns(num_cols)
	ensemble_actor = retry_until_success(lambda: ray.get_actor("composed_model"))

	if st.button("Submit preference"):
		# Submit new data and fetch results
		result = ray.get(ensemble_actor.record_liked_id.remote(state.liked_image_id))
	else:
		result = ray.get(ensemble_actor.get_recommendation.remote())

	if result:
		movies = result["recs"]
		state.liked_image_id = str(movies[pick_img]['id'])
		### List[Movie] where Movie in model results
		### id: int
		### title: string
		for i in range(num_movies):
			col_idx = i % num_cols
			movie = movies[i]
			status_text.text("%i%% Complete" % (i * 20))
			progress_bar.progress(int((i / num_movies) * 100))
			image_name = 'assets/' + str(movie['id']) + '.jpg'
			image = Image.open(image_name)
			cols[col_idx].image(image, 
				caption=str(i+1)+':'+movie['title'],width=img_width)

		progress_bar.empty()
	else:
		st.write("Loading.")
		# time.sleep(4)
		# rerun()