Upload 4 files

.gitattributes

@@ -26,3 +26,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+movie_list.pkl filter=lfs diff=lfs merge=lfs -text
+similarity.pkl filter=lfs diff=lfs merge=lfs -text

app.py

@@ -1,14 +1,60 @@
+import pickle
 import streamlit as st
-import pandas as pd
+import requests
+
+def fetch_poster(movie_id):
+    url = "https://api.themoviedb.org/3/movie/{}?api_key=8265bd1679663a7ea12ac168da84d2e8&language=en-US".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
+
+def recommend(movie):
+    index = movies[movies['title'] == movie].index[0]
+    distances = sorted(list(enumerate(similarity[index])), reverse=True, key=lambda x: x[1])
+    recommended_movie_names = []
+    recommended_movie_posters = []
+    for i in distances[1:6]:
+        # fetch the movie poster
+        movie_id = movies.iloc[i[0]].movie_id
+        recommended_movie_posters.append(fetch_poster(movie_id))
+        recommended_movie_names.append(movies.iloc[i[0]].title)
+
+    return recommended_movie_names,recommended_movie_posters
+
+
+st.header('Movie Recommender System')
+movies = pickle.load(open('movie_list.pkl','rb'))
+similarity = pickle.load(open('similarity.pkl','rb'))
+
+movie_list = movies['title'].values
+selected_movie = st.selectbox(
+    "Type or select a movie from the dropdown",
+    movie_list
+)
+
+if st.button('Show Recommendation'):
+    recommended_movie_names,recommended_movie_posters = recommend(selected_movie)
+    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])
 
-st.title("A Simple Streamlit Web App")
 
-name = st.text_input("Enter your name", '')
 
-st.write(f"Hello {name}!")
 
-x = st.slider("Select an integer x", 0, 10, 1)
-y = st.slider("Select an integer y", 0, 10, 1)
 
-df = pd.DataFrame({"x": [x], "y": [y] , "x + y": [x + y]}, index = ["addition row"])
-st.write(df)

movie_list.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:58d379d5cf1d989f7da913398e7666a760acd0c15d47a959096ea78454aa90e6
+size 2396328

requirements.txt

@@ -1,2 +1,2 @@
 streamlit
-pandas
+requests

similarity.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:68067b69939937d822dc88f29ebd7d08240541a875b652b19c7e0bc050a433a8
+size 184781251