Update app.py

app.py

@@ -1,88 +1,88 @@
-import streamlit as st
-import pandas as pd
-import pickle
-import requests
-
-
-
-# Load data
-movie_list = pickle.load(open('movie_list.pkl', 'rb'))
-df = pickle.load(open('movies.pkl', 'rb'))
-similarity = pickle.load(open('similarity.pkl', 'rb'))
-
-movie_list = pd.DataFrame(movie_list)
-
-# Title of the app
-st.title("🎥 Movie Recommender System")
-
-# TMDb API key
-# TMDb API key
-TMDB_API_KEY = "4142bd9f69dc53d39b4f3661066099ef"
-
-
-# Fetch poster path
-def get_poster_path(movie_id):
-    response = requests.get(
-        f"https://api.themoviedb.org/3/movie/{movie_id}?api_key={TMDB_API_KEY}&language=en-US"
-    )
-    data = response.json()
-    return f"https://image.tmdb.org/t/p/w500/{data.get('poster_path', '')}"
-
-# Fetch trailer link
-def get_trailer(movie_id):
-    response = requests.get(
-        f"https://api.themoviedb.org/3/movie/{movie_id}/videos?api_key={TMDB_API_KEY}&language=en-US"
-    )
-    data = response.json()
-    for video in data.get("results", []):
-        if video["type"] == "Trailer":
-            return f"https://www.youtube.com/watch?v={video['key']}"
-    return None
-
-# Recommendation logic
-def recommend(movie):
-    movie_index = df[df['title'] == movie].index[0]
-    distances = similarity[movie_index]
-    movies_list = sorted(list(enumerate(distances)), reverse=True, key=lambda x: x[1])[1:6]
-
-    recommend_movies = []
-    recommend_movie_posters = []
-    recommend_movie_trailers = []
-
-    for i in movies_list:
-        movie_id = df.iloc[i[0]]['id']
-        recommend_movies.append(df.iloc[i[0]]['title'])
-        recommend_movie_posters.append(get_poster_path(movie_id))
-        recommend_movie_trailers.append(get_trailer(movie_id))
-
-    return recommend_movies, recommend_movie_posters, recommend_movie_trailers
-
-# Search for a movie
-selected_movie = st.selectbox("🔎 Search for a movie you watched:",movie_list['title'].values)
-if selected_movie not in movie_list['title'].values:
-    st.warning("Movie not found. Please select a valid movie.")
-else:
-    st.success(f"You selected: {selected_movie}")
-
-# Recommend button
-if st.button("Recommend"):
-    if selected_movie in movie_list['title'].values:
-        names, posters, trailers = recommend(selected_movie)
-
-        # Create a dynamic grid for recommendations
-        st.markdown("## Recommended Movies:")
-        cols = st.columns(5)  # Create 5 columns
-        for idx, (name, poster, trailer) in enumerate(zip(names, posters, trailers)):
-            with cols[idx % 5]:  # Use modulus to cycle columns
-                st.image(poster, use_container_width=True)
-
-                st.caption(name)
-                if trailer:
-                    st.markdown(f"[🎥 Watch Trailer]({trailer})")
-
-
-
-
-# Footer
-st.markdown("---")
-st.markdown("Powered by [TMDb API](https://www.themoviedb.org/) | Developed with ❤️ by Shaik")
+import streamlit as st
+import pandas as pd
+import pickle
+import requests
+
+
+
+# Load data
+movie_list = pickle.load(open('movie_list.pkl', 'rb'))
+df = pickle.load(open('movies.pkl', 'rb'))
+similarity = pickle.load(open('similarity.pkl', 'rb'))
+
+movie_list = pd.DataFrame(movie_list)
+
+# Title of the app
+st.title("🎥 Movie Recommender System")
+
+# TMDb API key
+# TMDb API key
+TMDB_API_KEY = "4142bd9f69dc53d39b4f3661066099ef"
+
+
+# Fetch poster path
+def get_poster_path(movie_id):
+    response = requests.get(
+        f"https://api.themoviedb.org/3/movie/{movie_id}?api_key={TMDB_API_KEY}&language=en-US"
+    )
+    data = response.json()
+    return f"https://image.tmdb.org/t/p/w500/{data.get('poster_path', '')}"
+
+# Fetch trailer link
+def get_trailer(movie_id):
+    response = requests.get(
+        f"https://api.themoviedb.org/3/movie/{movie_id}/videos?api_key={TMDB_API_KEY}&language=en-US"
+    )
+    data = response.json()
+    for video in data.get("results", []):
+        if video["type"] == "Trailer":
+            return f"https://www.youtube.com/watch?v={video['key']}"
+    return None
+
+# Recommendation logic
+def recommend(movie):
+    movie_index = df[df['title'] == movie].index[0]
+    distances = similarity[movie_index]
+    movies_list = sorted(list(enumerate(distances)), reverse=True, key=lambda x: x[1])[1:6]
+
+    recommend_movies = []
+    recommend_movie_posters = []
+    recommend_movie_trailers = []
+
+    for i in movies_list:
+        movie_id = df.iloc[i[0]]['id']
+        recommend_movies.append(df.iloc[i[0]]['title'])
+        recommend_movie_posters.append(get_poster_path(movie_id))
+        recommend_movie_trailers.append(get_trailer(movie_id))
+
+    return recommend_movies, recommend_movie_posters, recommend_movie_trailers
+
+# Search for a movie
+selected_movie = st.selectbox("🔎 Search for a movie you watched:",movie_list['title'].values)
+if selected_movie not in movie_list['title'].values:
+    st.warning("Movie not found. Please select a valid movie.")
+else:
+    st.success(f"You selected: {selected_movie}")
+
+# Recommend button
+if st.button("Recommend"):
+    if selected_movie in movie_list['title'].values:
+        names, posters, trailers = recommend(selected_movie)
+
+        # Create a dynamic grid for recommendations
+        st.markdown("## Recommended Movies:")
+        cols = st.columns(5)  # Create 5 columns
+        for idx, (name, poster, trailer) in enumerate(zip(names, posters, trailers)):
+            with cols[idx % 5]:  # Use modulus to cycle columns
+                st.image(poster, use_container_width=True)
+
+                st.caption(name)
+                if trailer:
+                    st.markdown(f"[🎥 Watch Trailer]({trailer})")
+
+
+
+
+# Footer
+st.markdown("---")
+st.markdown("Powered by [TMDb API](https://www.themoviedb.org/) | Developed with ❤️ by [Shaik](https://www.linkedin.com/in/shaik-hidaythulla/)")