Spaces:
Sleeping
Sleeping
import streamlit as st | |
from PIL import Image | |
import requests | |
from io import BytesIO | |
from sentence_transformers import SentenceTransformer | |
import faiss | |
import pandas as pd | |
# import sys | |
# import subprocess | |
# import streamlit as st | |
# # Debug: Print Python executable and installed packages | |
# st.write(f"Python executable: {sys.executable}") | |
# installed_packages = subprocess.run([sys.executable, "-m", "pip", "list"], capture_output=True, text=True).stdout | |
# st.text(installed_packages) | |
model = SentenceTransformer('cointegrated/rubert-tiny2') | |
index = faiss.read_index('faiss_index.index') | |
data = pd.read_csv('datasetf.csv') | |
def vectorize(descriptions): | |
embeddings = model.encode(descriptions) | |
return embeddings | |
def find_similar_shows(user_description, index, k=5): | |
query_vector = vectorize([user_description]) | |
_, indices = index.search(query_vector, k) | |
return data.iloc[indices.flatten()] | |
def load_image(url): | |
try: | |
response = requests.get(url) | |
img = Image.open(BytesIO(response.content)) | |
except Exception: | |
# If an error occurs, load the dummy image | |
img = Image.open("cat.jpg") # Update the path to your dummy image | |
return img | |
st.title('TV Show Recommender') | |
# User input for the show description | |
user_description = st.text_area("Describe the TV show you're looking for:") | |
# Slider for the number of recommendations | |
num_recommendations = st.slider('Number of recommendations:', min_value=1, max_value=10, value=5) | |
# Button to get recommendations | |
if st.button('Recommend') and user_description: | |
try: | |
recommended_shows = find_similar_shows(user_description, index, num_recommendations) | |
for idx in recommended_shows.index: | |
with st.container(): | |
link = data.loc[idx, 'url'] | |
poster_url = data.loc[idx, 'poster'] | |
title = data.loc[idx, 'title'] | |
description = data.loc[idx, 'description'] | |
img = load_image(poster_url) # Use the load_image function | |
col1, col2 = st.columns([1, 2]) | |
with col1: | |
st.image(img, caption=title, use_column_width='always') | |
with col2: | |
st.write(description) | |
st.markdown(f"[More Info]({link})", unsafe_allow_html=True) | |
except Exception as e: | |
st.error(f"An error occurred: {e}") | |