app.py

import os
os.environ["STREAMLIT_SERVER_PORT"] = "7860"
os.environ["STREAMLIT_SERVER_ADDRESS"] = "0.0.0.0"

import streamlit as st
import pickle
import pandas as pd
import requests
import gdown   # ✅ NEW


# ✅ Download similarity.pkl from Google Drive if not present
SIMILARITY_FILE_ID = "1cv7PuP80FxlvkRcVrOhRwOxZRGvJCe1d"
SIMILARITY_PATH = "similarity.pkl"

if not os.path.exists(SIMILARITY_PATH):
    url = f"https://drive.google.com/uc?id={SIMILARITY_FILE_ID}"
    gdown.download(url, SIMILARITY_PATH, quiet=False)



# 🔑 Replace with your OMDb API key
OMDB_API_KEY = "11fe30b0"

def fetch_poster(title):
    url = f"http://www.omdbapi.com/?t={title}&apikey={OMDB_API_KEY}"
    response = requests.get(url)
    data = response.json()

    if data.get("Response") == "True":
        return data.get("Poster")
    else:
        return None


def recommend(movie):
    movie_index = movies[movies['title'] == movie].index[0]
    distances = similarity[movie_index]

    movies_list = sorted(
        list(enumerate(distances)),
        reverse=True,
        key=lambda x: x[1]
    )[1:6]

    recommended_movies = []
    for i in movies_list:
        recommended_movies.append(movies.iloc[i[0]].title)

    return recommended_movies


# Load data
movies_dict = pickle.load(open('movie_dict.pkl', 'rb'))
movies = pd.DataFrame(movies_dict)

# ✅ This now loads the downloaded file
similarity = pickle.load(open(SIMILARITY_PATH, 'rb'))

# Streamlit UI
st.title("Movie Recommender System 🎬")

selected_movie_name = st.selectbox(
    "Select your movie",
    movies['title'].values
)

if st.button("Recommend"):
    recommendations = recommend(selected_movie_name)

    cols = st.columns(5)
    for col, movie in zip(cols, recommendations):
        with col:
            poster = fetch_poster(movie)
            if poster:
                st.image(poster, width=150)
            st.markdown(f"**{movie}**")