Update app.py

app.py

@@ -120,7 +120,7 @@ else:
 
     # Only fetch and display movies if a genre is selected
     if genre_name:
-        st.warning("Please select at least one movie to generate recommendations.")
+        st.write("Please select at least one movie to generate recommendations.")
         genre_id = genres[genre_name]  # Get the genre ID based on the selected genre name
         top_movies = get_top_movies_by_genre(genre_id)  # Fetch top 10 movies in this genre, sorted by title length
 
@@ -172,27 +172,26 @@ else:
     if st.button("Show Recommendations"):
         if st.session_state['selected_movies']:
             # Step 1: Collect emotions of user-selected movies
-            st.write("Analyzing emotions of selected movies...")
-            selected_emotions = []
-            for movie_id in st.session_state['selected_movies']:
-                movie_details = get_movie_details(movie_id)
-                if movie_details:
-                    emotion = analyze_emotion(movie_details["description"])
-                    selected_emotions.append(emotion)
-                    st.write(f"**{movie_details['title']}** - Emotion: {emotion}")
-
-            # Step 2: Collect similar movies for all selected movies
-            all_similar_movies = []
-            for movie_id in st.session_state['selected_movies']:
-                similar_movies = get_similar_movies(movie_id, genre_id)
-                if similar_movies:
-                    all_similar_movies.extend(similar_movies)
-
-            # Step 3: Filter similar movies by matching emotions and remove duplicates
-            recommendations = []
-            seen_movie_ids = set(st.session_state['selected_movies'])  # Start with user-selected movies to exclude them
-
-            with st.spinner("Filtering movies by matching emotions..."):
+            with st.spinner("Analyzing emotions of selected movies..."):
+                selected_emotions = []
+                for movie_id in st.session_state['selected_movies']:
+                    movie_details = get_movie_details(movie_id)
+                    if movie_details:
+                        emotion = analyze_emotion(movie_details["description"])
+                        selected_emotions.append(emotion)
+
+                # Step 2: Collect similar movies for all selected movies
+                all_similar_movies = []
+                for movie_id in st.session_state['selected_movies']:
+                    similar_movies = get_similar_movies(movie_id, genre_id)
+                    if similar_movies:
+                        all_similar_movies.extend(similar_movies)
+
+                # Step 3: Filter similar movies by matching emotions and remove duplicates
+                recommendations = []
+                seen_movie_ids = set(st.session_state['selected_movies'])  # Start with user-selected movies to exclude them
+
+            with st.spinner("Filtering movies by description emotion..."):
                 for title, rating, poster_path, movie_id, description in all_similar_movies:
                     if movie_id not in seen_movie_ids:  # Exclude already-seen movies
                         movie_emotion = analyze_emotion(description)
@@ -205,7 +204,7 @@ else:
 
             # Step 4: Display final recommendations
             if st.session_state['recommendations']:
-                st.write("Based on your selections, you might also enjoy these top 5 movies:")
+                st.write("Based on your selections, you might also enjoy these top movies:")
                 for title, rating, poster_path, movie_id, emotion in sorted(st.session_state['recommendations'], key=lambda x: x[1], reverse=True)[:5]:
                     movie_details = get_movie_details(movie_id)
                     if movie_details:
@@ -215,7 +214,6 @@ else:
                         with col2:
                             st.markdown(f"**Title:** {movie_details['title']}")
                             st.markdown(f"**TMDb Rating:** {movie_details['rating']}")
-                            st.markdown(f"**Emotion:** {emotion}")
                             st.markdown(f"**Description:** {movie_details['description']}")
 
                             # Additional Information button with overlay using expander