import streamlit as st import random import os from datasets import load_dataset, concatenate_datasets from recommendation import get_recommendations START = random.randint(a=1,b=1000) END = START + 10 dataset = load_dataset("Ransaka/youtube_recommendation_data", token=os.environ.get('HF')) dataset = concatenate_datasets([dataset['train'], dataset['test']]) pil_images = dataset['image'][START:END] def show_image_metadata_and_related_info(image_index): selected_image = pil_images[image_index] image_title = dataset['title'][image_index] st.image(selected_image, caption=f"{image_title}", use_column_width=True) with st.expander("You May Also Like.."): dataset_s = get_recommendations(selected_image,image_title,8) col1_row1, col2_row1 = st.columns(2) with col1_row1: st.image(image=dataset_s['image'][0], caption=dataset_s['title'][0], width=200) with col2_row1: st.image(image=dataset_s['image'][1], caption=dataset_s['title'][1], width=200) # Second Row col1_row2, col2_row2 = st.columns(2) with col1_row2: st.image(image=dataset_s['image'][2], caption=dataset_s['title'][2], width=200) with col2_row2: st.image(image=dataset_s['image'][3], caption=dataset_s['title'][3], width=200) # Third row col1_row3, col2_row3 = st.columns(2) with col1_row3: st.image(image=dataset_s['image'][4], caption=dataset_s['title'][4], width=200) with col2_row3: st.image(image=dataset_s['image'][5], caption=dataset_s['title'][5], width=200) # Fourth Row col1_row4, col2_row4 = st.columns(2) with col1_row4: st.image(image=dataset_s['image'][6], caption=dataset_s['title'][6], width=200) with col2_row4: st.image(image=dataset_s['image'][7], caption=dataset_s['title'][7], width=200) def main(): st.title("Youtube Recommendation Engine") for i, image in enumerate(pil_images): show_image_metadata_and_related_info(i) if __name__ == '__main__': main()