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import streamlit as st
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from predict_trial import return_data
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@st.cache_resource
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def fetch_transformed_data():
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with st.spinner("Started training the model...",show_time=True):
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data_path, matrix_path = return_data().predict()
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return data_path,matrix_path
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st.success("Deployment complete, Successfully created the system!")
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from recommendationSystem.utils.common import recommend,find_anime
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@st.cache_resource
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def anime_info(anime_name,anime_data,similarity_matrix):
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if anime_name is not None:
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st.write('\n\n\n')
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name, posters, link, score = recommend(data=anime_data,matrix=similarity_matrix,anime=anime_name)
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_, col, _ = st.columns(3)
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with col:
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st.image(posters[0])
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st.text(f"{find_anime(anime_name)}")
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st.write(f'Similarity Score : {score[0]}')
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st.write('')
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st.link_button("Know More", link[0],use_container_width=True)
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for i in range(1,9,3):
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col1, col2, col3 = st.columns(3)
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with col1:
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st.image(posters[i])
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st.write(name[i])
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st.write(f'Similarity Score : {score[i]}')
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st.write('')
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st.link_button("Know More", link[i],use_container_width=True)
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with col2:
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st.image(posters[i+1])
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st.write(name[i+1])
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st.write(f'Similarity Score : {score[i+1]}')
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st.write('')
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st.link_button("Know More", link[i+1],use_container_width=True)
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with col3:
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st.image(posters[i+2])
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st.write(name[i+2])
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st.write(f'Similarity Score : {score[i+2]}')
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st.write('')
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st.link_button("Know More", link[i+2],use_container_width=True)
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st.write('\n\n\n')
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