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}")