Update app.py

app.py

@@ -1,48 +1,54 @@
-import streamlit as st
-import pandas as pd
-import requests
-import pickle
-
-# Load the processed data and similarity matrix
-with open('movie_data.pkl', 'rb') as file:
-    movies, cosine_sim = pickle.load(file)
-
-# Function to get movie recommendations
-def get_recommendations(title, cosine_sim=cosine_sim):
-    idx = movies[movies['title'] == title].index[0]
-    sim_scores = list(enumerate(cosine_sim[idx]))
-    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
-    sim_scores = sim_scores[1:11]  # Get top 10 similar movies
-    movie_indices = [i[0] for i in sim_scores]
-    return movies[['title', 'movie_id']].iloc[movie_indices]
-
-# Fetch movie poster from TMDB API
-def fetch_poster(movie_id):
-    api_key = '7b995d3c6fd91a2284b4ad8cb390c7b8'  # Replace with your TMDB API key
-    url = f'https://api.themoviedb.org/3/movie/{movie_id}?api_key={api_key}'
-    response = requests.get(url)
-    data = response.json()
-    poster_path = data['poster_path']
-    full_path = f"https://image.tmdb.org/t/p/w500{poster_path}"
-    return full_path
-
-# Streamlit UI
-st.title("Movie Recommendation System")
-
-selected_movie = st.selectbox("Select a movie:", movies['title'].values)
-
-if st.button('Recommend'):
-    recommendations = get_recommendations(selected_movie)
-    st.write("Top 10 recommended movies:")
-
-    # Create a 2x5 grid layout
-    for i in range(0, 10, 5):  # Loop over rows (2 rows, 5 movies each)
-        cols = st.columns(5)  # Create 5 columns for each row
-        for col, j in zip(cols, range(i, i+5)):
-            if j < len(recommendations):
-                movie_title = recommendations.iloc[j]['title']
-                movie_id = recommendations.iloc[j]['movie_id']
-                poster_url = fetch_poster(movie_id)
-                with col:
-                    st.image(poster_url, width=130)
-                    st.write(movie_title)
+import streamlit as st
+import pandas as pd
+import requests
+import pickle
+import os
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+
+# Load the processed data and similarity matrix
+with open('movie_data.pkl', 'rb') as file:
+    movies, cosine_sim = pickle.load(file)
+
+# Function to get movie recommendations
+def get_recommendations(title, cosine_sim=cosine_sim):
+    idx = movies[movies['title'] == title].index[0]
+    sim_scores = list(enumerate(cosine_sim[idx]))
+    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
+    sim_scores = sim_scores[1:11]  # Get top 10 similar movies
+    movie_indices = [i[0] for i in sim_scores]
+    return movies[['title', 'movie_id']].iloc[movie_indices]
+
+# Fetch movie poster from TMDB API
+def fetch_poster(movie_id):
+    api_key = os.getenv("API_KEY")  # Replace with your TMDB API key
+    url = f'https://api.themoviedb.org/3/movie/{movie_id}?api_key={api_key}'
+    response = requests.get(url)
+    data = response.json()
+    poster_path = data['poster_path']
+    full_path = f"https://image.tmdb.org/t/p/w500{poster_path}"
+    return full_path
+
+# Streamlit UI
+st.title("Movie Recommendation System")
+
+selected_movie = st.selectbox("Select a movie:", movies['title'].values)
+
+if st.button('Recommend'):
+    recommendations = get_recommendations(selected_movie)
+    st.write("Top 10 recommended movies:")
+
+    # Create a 2x5 grid layout
+    for i in range(0, 10, 5):  # Loop over rows (2 rows, 5 movies each)
+        cols = st.columns(5)  # Create 5 columns for each row
+        for col, j in zip(cols, range(i, i+5)):
+            if j < len(recommendations):
+                movie_title = recommendations.iloc[j]['title']
+                movie_id = recommendations.iloc[j]['movie_id']
+                poster_url = fetch_poster(movie_id)
+                with col:
+                    st.image(poster_url, width=130)
+                    st.write(movie_title)