import streamlit as st
import pandas as pd
import requests
import pickle

# Load the processed data and similarity matrix
with open('movie_data.pkl', 'rb') as file:
    movies, cosine_sim = pickle.load(file)

# Function to get movie recommendations
def get_recommendations(title, cosine_sim=cosine_sim):
    idx = movies[movies['title'] == title].index[0]
    sim_scores = list(enumerate(cosine_sim[idx]))
    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
    sim_scores = sim_scores[1:11]  # Get top 10 similar movies
    movie_indices = [i[0] for i in sim_scores]
    return movies[['title', 'movie_id']].iloc[movie_indices]

# Fetch movie poster and additional details using append_to_response
def fetch_movie_data(movie_id):
    api_key = "5c1c27e14a6a61a873db79d82528056f"
    url = f'https://api.themoviedb.org/3/movie/{movie_id}?api_key={api_key}&append_to_response=videos,images'
    
    try:
        response = requests.get(url)
        response.raise_for_status()
        data = response.json()

        # Handle poster path
        poster_path = data.get('poster_path')
        poster_url = f"https://image.tmdb.org/t/p/w500{poster_path}" if poster_path else \
                    "https://via.placeholder.com/500x750?text=No+Image+Available"

        # Handle trailer safely
        videos = data.get('videos', {}).get('results', [])
        trailer = next((v for v in videos if v.get('type') == 'Trailer'), None)

        # Handle backdrop safely
        backdrops = data.get('images', {}).get('backdrops', [])
        backdrop_path = backdrops[0].get('file_path') if backdrops else ''

        # Handle overview
        overview = data.get('overview', 'No description available')[:100] + "..." 

        return {
            'poster': poster_url,
            'trailer_key': trailer.get('key') if trailer else None,
            'backdrop': backdrop_path,
            'overview': overview
        }
    
    except requests.exceptions.RequestException as e:
        st.error(f"API Error: {str(e)}")
        return {
            'poster': "https://via.placeholder.com/500x750?text=No+Image+Available",
            'trailer_key': None,
            'backdrop': '',
            'overview': 'No description available'
        }

# Streamlit UI
st.title("Movie Recommendation System")

selected_movie = st.selectbox("Select a movie:", movies['title'].values)

if st.button('Recommend'):
    recommendations = get_recommendations(selected_movie)
    st.write("Top 10 recommended movies:")

    # Create a 2x5 grid layout
    for i in range(0, 10, 5):  # Loop over rows (2 rows, 5 movies each)
        cols = st.columns(5)  # Create 5 columns for each row
        for col, j in zip(cols, range(i, i+5)):
            if j < len(recommendations):
                movie_title = recommendations.iloc[j]['title']
                movie_id = recommendations.iloc[j]['movie_id']
                movie_data = fetch_movie_data(movie_id)
                
                with col:
                    st.image(movie_data['poster'], width=130, caption=movie_title)
                    if movie_data['trailer_key']:
                        st.markdown(f"[Watch Trailer](https://www.youtube.com/watch?v={movie_data['trailer_key']})")
                    st.write(movie_data['overview'][:100] + "...")