added all files from existing github repo

app.py

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+import pickle
+import streamlit as st
+import requests
+import pandas as pd
+
+def fetch_poster(movie_id):
+    # url = "https://api.themoviedb.org/3/movie/{}?api_key=8fe6f2aa0e5d863d7fd4dc7994cfbce9&language=en-US".format(movie_id)
+    # data = requests.get(url)
+    #st.write(movie_id)
+    url = "https://api.themoviedb.org/3/movie/{}?language=en-US".format(movie_id)
+    headers = {
+        "accept": "application/json",
+        "Authorization": "Bearer eyJhbGciOiJIUzI1NiJ9.eyJhdWQiOiI4ZmU2ZjJhYTBlNWQ4NjNkN2ZkNGRjNzk5NGNmYmNlOSIsIm5iZiI6MTcyMDk0MzA0MC41MTQyMDgsInN1YiI6IjY2OGY3NzQ2OGUzOWM5YzlmMjEwZWQxOSIsInNjb3BlcyI6WyJhcGlfcmVhZCJdLCJ2ZXJzaW9uIjoxfQ.zYIfFcNM8Y_Z33JOczRQO8PBoU-RlkoFQeFyAiqkLpw"
+    }
+
+    data = response = requests.get(url, headers=headers)
+    #st.write(data)
+    data = data.json()
+    #st.write(data)
+    poster_path = data['poster_path']
+    # print(response.text)
+    full_path = "https://image.tmdb.org/t/p/w500/" + poster_path
+    return full_path
+
+def recommend(movie):
+    index = movies[movies['title'] == movie].index[0]
+    # index = 385
+    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]].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_names.pkl','rb'))
+movies = pd.DataFrame(movies)
+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
+)
+print(selected_movie)
+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])
+
+
+
+
+

movie_names.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fa45385b2529ad9a788b2749bf1404c63bfa982e84a4b474dda229160265e282
+size 5033528

requirements.txt

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+streamlit
+numpy
+pickleshare
+requests
+pandas
+protobuf==3.19.1

similarity.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2d207507ed609ded5ddc22b24ef51a51efbe728517b3ad2d78e4b75b976e9905
+size 184781251