File size: 2,103 Bytes
6bc88c9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 |
import streamlit as st
from predict_trial import return_data
@st.cache_resource
def fetch_transformed_data():
with st.spinner("Started training the model...",show_time=True):
data_path, matrix_path = return_data().predict()
return data_path,matrix_path
st.success("Deployment complete, Successfully created the system!")
#-----------------------------------------------------------------------------
from recommendationSystem.utils.common import recommend,find_anime
@st.cache_resource
def anime_info(anime_name,anime_data,similarity_matrix):
if anime_name is not None:
st.write('\n\n\n')
name, posters, link, score = recommend(data=anime_data,matrix=similarity_matrix,anime=anime_name)
_, col, _ = st.columns(3)
with col:
st.image(posters[0])
st.text(f"{find_anime(anime_name)}")
st.write(f'Similarity Score : {score[0]}')
st.write('')
st.link_button("Know More", link[0],use_container_width=True)
for i in range(1,9,3):
col1, col2, col3 = st.columns(3)
with col1:
st.image(posters[i])
st.write(name[i])
st.write(f'Similarity Score : {score[i]}')
st.write('')
st.link_button("Know More", link[i],use_container_width=True)
with col2:
st.image(posters[i+1])
st.write(name[i+1])
st.write(f'Similarity Score : {score[i+1]}')
st.write('')
st.link_button("Know More", link[i+1],use_container_width=True)
with col3:
st.image(posters[i+2])
st.write(name[i+2])
st.write(f'Similarity Score : {score[i+2]}')
st.write('')
st.link_button("Know More", link[i+2],use_container_width=True)
st.write('\n\n\n')
|