Update app.py

app.py

@@ -1,58 +1,66 @@
-import pandas as pd
-import streamlit as st
-import pickle
-import requests
-
-def fetch_image(movie_id):
-    response = requests.get('https://api.themoviedb.org/3/movie/{}?api_key=8265bd1679663a7ea12ac168da84d2e8&language=en-US'.format(movie_id))
-
-    data = response.json()
-
-    return "https://image.tmdb.org/t/p/w500/" + data['poster_path']
-
-def recommand(movie):
-    movie_index = movies[movies['title'] == movie].index[0]
-    distences = similarity[movie_index]
-    movies_list = sorted(list(enumerate(distences)),reverse=True,key = lambda x : x[1])[1:6]
-
-    recommanded_movie = []
-    recommaned_poster=[]
-    for i in movies_list:
-        movie_id = movies.iloc[i[0]].movie_id
-
-        recommanded_movie.append(movies.iloc[i[0]].title)
-        recommaned_poster.append(fetch_image(movie_id)) # Fetch poster from API
-    return recommanded_movie,recommaned_poster
-
-movies_dict = pickle.load(open('movie_dict.pkl','rb'))
-movies = pd.DataFrame(movies_dict)
-
-similarity = pickle.load(open('similarity.pkl','rb'))
-
-st.title('Movie Recommender System')
-
-selected_movie = st.selectbox(
-    'What is your taste in movie ? ',
-    movies['title'].values)
-st.write('You selected:', selected_movie)
-
-
-if st.button('Recommend movie'):
-    name,poster =recommand(selected_movie)
-
-    col1, col2, col3, col4, col5 = st.columns(5)
-    with col1:
-        st.text(name[0])
-        st.image(poster[0])
-    with col2:
-        st.text(name[1])
-        st.image(poster[1])
-    with col3:
-        st.text(name[2])
-        st.image(poster[2])
-    with col4:
-        st.text(name[3])
-        st.image(poster[3])
-    with col5:
-        st.text(name[4])
-        st.image(poster[4])
+import pandas as pd
+import streamlit as st
+import pickle
+import requests
+
+def fetch_image(movie_id):
+    response = requests.get('https://api.themoviedb.org/3/movie/{}?api_key=8265bd1679663a7ea12ac168da84d2e8&language=en-US'.format(movie_id))
+
+    data = response.json()
+
+    return "https://image.tmdb.org/t/p/w500/" + data['poster_path']
+
+def recommand(movie):
+    movie_index = movies[movies['title'] == movie].index[0]
+    distences = similarity[movie_index]
+    movies_list = sorted(list(enumerate(distences)),reverse=True,key = lambda x : x[1])[1:6]
+
+    recommanded_movie = []
+    recommaned_poster=[]
+    for i in movies_list:
+        movie_id = movies.iloc[i[0]].movie_id
+
+        recommanded_movie.append(movies.iloc[i[0]].title)
+        recommaned_poster.append(fetch_image(movie_id)) # Fetch poster from API
+    return recommanded_movie,recommaned_poster
+
+movies_dict = pickle.load(open('movie_dict.pkl','rb'))
+movies = pd.DataFrame(movies_dict)
+
+similarity = pickle.load(open('similarity.pkl','rb'))
+
+st.title('Movie Recommender System')
+
+selected_movie = st.selectbox(
+    'What is your taste in movie ? ',
+    movies['title'].values)
+st.write('You selected:', selected_movie)
+
+
+if st.button('Recommend movie'):
+    name,poster =recommand(selected_movie)
+
+    col1, col2, col3, col4, col5 = st.columns(5)
+    with col1:
+        st.text(name[0])
+        st.image(poster[0])
+    with col2:
+        st.text(name[1])
+        st.image(poster[1])
+    with col3:
+        st.text(name[2])
+        st.image(poster[2])
+    with col4:
+        st.text(name[3])
+        st.image(poster[3])
+    with col5:
+        st.text(name[4])
+        st.image(poster[4])
+
+st.header('', divider='rainbow')
+st.markdown('''
+    Developed by KARTAVYA MASTER :8ball:
+''')
+
+link='PORTFOLIO : [CLICK ME](https://mydawjbhdas.my.canva.site/aiwithkartavya)'
+st.markdown(link,unsafe_allow_html=True)