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README.md

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+
+# Movie Recommendation System🚀
+
+A movie recommender that recommends five movies based on the movie input.
+It uses a KNN model that works on the concept text vectorization.
+
+
+## Screenshots
+
+![](https://github.com/VividhPandey003/movie_recommendation_system/blob/main/images/1.png)
+---
+![](https://github.com/VividhPandey003/movie_recommendation_system/blob/main/images/2.png)
+---
+![](https://github.com/VividhPandey003/movie_recommendation_system/blob/main/images/3.png)
+---
+![](https://github.com/VividhPandey003/movie_recommendation_system/blob/main/images/4.png)
+
+
+### Try it on your own 💻
+  [Movie Recommendation System Deployment](https://huggingface.co/spaces/coder003/movie-recommender)

app.py

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+import streamlit as st
+import pickle
+import pandas as pd
+import requests
+
+
+def fetch_poster(movie_id):
+    url = "https://api.themoviedb.org/3/movie/{}?api_key=fe34a557846e9a676a98fd362f059b28&language=en-US".format(
+        movie_id)
+    response = requests.get(url)
+    data = response.json()
+    poster_path = data['poster_path']
+    full_path = "https://image.tmdb.org/t/p/w500/" + poster_path
+    return full_path
+
+
+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_movies_posters = []
+    for i in movies_list:
+        movie_id = movies.iloc[i[0]].movie_id
+
+        recommended_movies.append(movies.iloc[i[0]].title)
+        # using movie_id fetch poster from API
+        recommended_movies_posters.append(fetch_poster(movie_id))
+    return recommended_movies,recommended_movies_posters
+
+
+st.title('Movie Recommender System')
+st.text('👨🏻‍💻 by Vividh Pandey')
+movies_dict = pickle.load(open('movie_dict.pkl', 'rb'))
+movies = pd.DataFrame(movies_dict)
+
+similarity = pickle.load(open('similarity.pkl', 'rb'))
+
+movie_list = movies['title'].values
+selected_movie_name = st.selectbox(
+    "Type or select a movie from the dropdown",
+    movies['title'].values
+)
+
+if st.button('Show Recommendation'):
+    names,posters = recommend(selected_movie_name)
+    col1, col2, col3, col4, col5 = st.columns(5)
+    with col1:
+        st.text(names[0])
+        st.image(posters[0])
+    with col2:
+        st.text(names[1])
+        st.image(posters[1])
+    with col3:
+        st.text(names[2])
+        st.image(posters[2])
+    with col4:
+        st.text(names[3])
+        st.image(posters[3])
+    with col5:
+        st.text(names[4])
+        st.image(posters[4])

movie_dict.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e9a65940b6abc3091a4a6b8891226aa9d18c98e536138b6f79ceaef8ac8e95ec
+size 2216684

movies.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1aed44f66872a07f849a2472c6aeb2294cfb0e01562ed41db3925cd2e6d22c48
+size 2235322