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import streamlit as st
import pickle
import pandas as pd
import requests
original_title = '<p style="color:White; font-size: 80px;">CinePicks</p>'
st.markdown(original_title, unsafe_allow_html=True)
st.markdown(
f"""
<style>
.stApp {{
background: url("https://wallup.net/wp-content/uploads/2018/03/20/414708-Iron_Man-Iron_Man_2-Iron_Man_3-iron_man__mark_XLIII-The_Avengers.jpg");
}}
</style>
""",
unsafe_allow_html=True
)
def fetch_poster(movie_id):
url='https://api.themoviedb.org/3/movie/{}?api_key=4e9dfb203bf90ff6a4fc33522c802f3b'.format(movie_id)
data=requests.get(url)
data=data.json()
poster_path=data['poster_path']
full_path="https://image.tmdb.org/t/p/w500/" + poster_path
return full_path
similarity=pickle.load(open('similarity.pkl','rb'))
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=[]
recommended_movie_posters = []
for i in movies_list:
movie_id=movies.iloc[i[0]].movie_id
# Fetch poster from API
recommended_movies.append(movies.iloc[i[0]].title)
recommended_movie_posters.append(fetch_poster(movie_id))
return recommended_movies,recommended_movie_posters
movies_dict=pickle.load(open('movies_dict.pkl','rb'))
movies=pd.DataFrame(movies_dict)
selected_movie_name=st.selectbox('Recently Watched',movies['title'].values)
if st.button('Show Recommendations'):
recommended_movie_names, recommended_movie_posters = recommend(selected_movie_name)
col1, col2, col3, col4, col5 = st.columns(5)
with col1:
st.text(recommended_movie_names[0])
st.image(recommended_movie_posters[0])
with col2:
st.text(recommended_movie_names[1])
st.image(recommended_movie_posters[1])
with col3:
st.text(recommended_movie_names[2])
st.image(recommended_movie_posters[2])
with col4:
st.text(recommended_movie_names[3])
st.image(recommended_movie_posters[3])
with col5:
st.text(recommended_movie_names[4])
st.image(recommended_movie_posters[4]) |