Update src/streamlit_app.py

src/streamlit_app.py

@@ -240,64 +240,16 @@ if movie_id:
 
 
 elif page:
-    st.title("Rate Random Movies")
-
-    all_genres = sorted(set(
-        genre.strip()
-        for sublist in movie_df["genres"].dropna().str.split("|")
-        for genre in sublist
-    ))
-
-    selected_genres = st.multiselect(
-        "Select genres:",
-        options=all_genres,
-        default=st.session_state.get("selected_genres", []),
-        placeholder="Choose a genre"
-    )
-    st.session_state["selected_genres"] = selected_genres
-
-    if "rating_mode_state" not in st.session_state:
-        st.session_state.rating_mode_state = {
-            "movie_pool": pd.DataFrame(),
-            "index": 0
-        }
-
-    mode = st.session_state.rating_mode_state
-
-    if mode["movie_pool"].empty or mode.get("genre_filter") != selected_genres:
-        if selected_genres:
-            genre_filter = movie_df["genres"].apply(
-                lambda g: any(genre in g.split("|") for genre in selected_genres)
-            )
-            filtered_df = movie_df[genre_filter].copy()
-            if not filtered_df.empty:
-                mode["movie_pool"] = filtered_df.sample(frac=1, random_state=42).reset_index(drop=True)
-                mode["index"] = 0
-                mode["genre_filter"] = selected_genres
-            else:
-                st.warning("No movies found for selected genres.")
-                st.stop()
-        else:
-            st.info("Please select at least one genre.")
-            st.stop()
+    st.title("🎬 Rate Random Movies")
 
-    movie_pool = mode["movie_pool"]
-    index = mode["index"]
-
-    if index >= len(movie_pool):
-        st.success("You've gone through all movies in this selection.")
-        if st.button("Restart"):
-            mode["index"] = 0
-            st.rerun()
-        st.stop()
-
-    movie = movie_pool.iloc[index]
+    # Zufälligen Film ziehen
+    movie = movie_df.sample(1).iloc[0]
     poster_url, tmdb_link = get_tmdb_data(movie["clean_title"], movie["year"])
 
     col1, col2 = st.columns([1, 2])
     with col1:
         if poster_url and "placeholder.com" not in poster_url:
-            st.image(poster_url, width=200)  # Einheitlich kleine Breite
+            st.image(poster_url, width=200)
         else:
             st.markdown("""
             <div style='width:200px;height:300px;border:2px dashed gray;display:flex;align-items:center;justify-content:center;color:gray;font-size:12px;'>
@@ -312,17 +264,9 @@ elif page:
         if tmdb_link:
             st.markdown(f"<a href='{tmdb_link}' target='_blank'>View on TMDb</a>", unsafe_allow_html=True)
 
-        existing_rating = next((r["rating"] for r in all_ratings_data if r["movie_id"] == movie["movieId"]), None)
-        default_rating = existing_rating if existing_rating else 1
-
+        # Bewertung
         rating_key = f"rating_{movie['movieId']}"
-        rating = st.radio(
-            "Rate this movie:",
-            options=[1, 2, 3, 4, 5],
-            horizontal=True,
-            key=rating_key,
-            index=[1, 2, 3, 4, 5].index(default_rating)
-        )
+        rating = st.radio("Rate this movie:", [1, 2, 3, 4, 5], horizontal=True, key=rating_key)
 
         col_submit, col_skip = st.columns([1, 1])
         with col_submit:
@@ -332,12 +276,10 @@ elif page:
                     "rating": rating,
                     "timestamp": datetime.now().isoformat()
                 })
-                mode["index"] += 1
                 st.rerun()
 
         with col_skip:
             if st.button("Didn't Watch", key=f"skip_{movie['movieId']}"):
-                mode["index"] += 1
                 st.rerun()
 
 elif search_query:
@@ -450,7 +392,7 @@ else:
     # Modell-Auswahl Dropdown
     model_choice = st.radio(
         "Choose Recommendation Model:",
-        options=["Neural Network", "SVD"],
+        options=["SVD"],
         index=0,
         horizontal=True,
         key="model_selection"
@@ -523,6 +465,7 @@ else:
             }
             .styled-table th, .styled-table td {
                 padding: 12px 15px;
+                text-align: left;
             }
             .styled-table tbody tr {
                 border-bottom: 1px solid #333;
@@ -550,19 +493,6 @@ else:
 
         user_ratings_dict = {r["movie_id"]: r["rating"] for r in all_ratings_data}
 
-        #if user_ratings_dict:
-        #    if st.session_state["model_selection"] == "Neural Network":
-        #        available_movies = movie_df["movieId"].tolist()
-        #        recommendations = recommend_with_nn(user_ratings_dict, nn_model, encodings, top_n=10)
-
-        #    else:
-        #        ratings_full = pd.DataFrame(all_ratings_data)
-        #        ratings_full["userId"] = 999999  # Dummy user
-        #        ratings_full["rating"] = ratings_full["rating"].astype(float)
-        #        recommendations = recommend_with_svd(svd_model, trainset, ratings_full, user_ratings_dict, top_n=10)
-
-        #    recommended_df = pd.merge(recommendations, movie_df, on="movieId", how="left")
-
         import random 
         
         if user_ratings_dict: