""", unsafe_allow_html=True)
# Modell-Auswahl Dropdown
model_choice = st.radio(
"Choose Recommendation Model:",
options=["Neural Network", "SVD"],
index=0,
horizontal=True,
key="model_selection"
)
query_params = st.query_params
page = query_params.get("rateflow")
search_query = query_params.get("search")
movie_id = query_params.get("movie_id")
def render_star_rating(rating):
return "".join(["★" for _ in range(rating)])
all_ratings_data = load_ratings()
if movie_id:
try:
movie_id = int(movie_id)
movie_info = movie_df[movie_df["movieId"] == movie_id].iloc[0]
st.title(movie_info["clean_title"])
poster_url, tmdb_link = get_tmdb_data(movie_info["clean_title"], movie_info["year"])
col1, col2 = st.columns([1, 3])
with col1:
if poster_url and "placeholder.com" not in poster_url:
st.image(poster_url, use_container_width=True)
else:
st.markdown("""
No picture available
""", unsafe_allow_html=True)
with col2:
st.subheader("Details")
st.write(f"**Genres:** {movie_info['genres']}")
st.write(f"**Year:** {movie_info['year']}")
if tmdb_link:
st.markdown(f"View on TMDb", unsafe_allow_html=True)
rating_key = f"detail_rating_{movie_id}"
user_rating = next((r["rating"] for r in all_ratings_data if r["movie_id"] == movie_id), None)
st.markdown("### Your Rating")
existing_rating = next((r["rating"] for r in all_ratings_data if r["movie_id"] == movie_id), None)
initial_index = existing_rating - 1 if existing_rating else None
new_rating = st.radio("Rate this movie:", [1, 2, 3, 4, 5], horizontal=True, index=initial_index, key=rating_key)
if st.button("Submit Rating"):
if new_rating:
save_rating_to_json({
"movie_id": movie_id,
"rating": new_rating,
"timestamp": datetime.now().isoformat()
})
st.success("Rating saved.")
st.rerun()
except Exception as e:
st.error("Could not load movie details.")
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, placeholder="All genres")
genre_filter = movie_df["genres"].apply(lambda g: any(genre in g.split("|") for genre in selected_genres)) if selected_genres else True
if isinstance(genre_filter, pd.Series):
filtered_df = movie_df[genre_filter].copy()
else:
filtered_df = movie_df.copy()
if filtered_df.empty:
st.warning("No movies match the selected filters.")
st.stop()
if "last_filter_state" not in st.session_state or st.session_state.last_filter_state != tuple(selected_genres):
st.session_state.movie_pool = filtered_df.sample(frac=1).reset_index(drop=True)
st.session_state.random_index = 0
st.session_state.last_filter_state = tuple(selected_genres)
st.session_state.current_random_movie = None
if "movie_pool" not in st.session_state or st.session_state.movie_pool.empty:
st.warning("Movie pool is empty. Try adjusting your filters.")
st.stop()
movie_pool = st.session_state.movie_pool
index = st.session_state.get("random_index", 0)
if index >= len(movie_pool):
st.info("No more movies to rate.")
st.stop()
try:
movie = movie_pool.iloc[index]
except IndexError:
st.error("Could not load a movie. Please try again.")
st.stop()
if movie is None or pd.isna(movie["clean_title"]):
st.error("Invalid movie data.")
st.stop()
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=300)
else:
st.markdown("""
No picture available
""", unsafe_allow_html=True)
with col2:
st.subheader(movie["clean_title"])
st.markdown(f"**Genres:** {movie['genres']}")
st.markdown(f"**Year:** {movie['year']}")
if tmdb_link:
st.markdown(f"View on TMDb", unsafe_allow_html=True)
st.markdown(f"### Movie {index + 1} of {len(movie_pool)}")
st.markdown("### Your Rating")
rating_key = f"rating_{movie['movieId']}"
if rating_key not in st.session_state:
st.session_state[rating_key] = None
st.session_state[rating_key] = st.radio(
"Rate this movie:", [1, 2, 3, 4, 5], horizontal=True, index=None, key=f"radio_{movie['movieId']}")
col_submit, col_skip = st.columns([1, 1])
with col_submit:
rating = st.session_state[rating_key]
if st.button("Submit Rating") and rating:
save_rating_to_json({
"movie_id": int(movie["movieId"]),
"rating": rating,
"timestamp": datetime.now().isoformat()
})
st.session_state["random_index"] = index + 1
st.session_state["just_submitted"] = True
st.rerun()
with col_skip:
if st.button("Didn't Watch"):
st.session_state["random_index"] = index + 1
st.session_state["just_submitted"] = True
st.rerun()
elif search_query:
st.title(f"Search Results for '{search_query}'")
search_clean = search_query.strip().lower()
def title_match_score(title):
title_lower = title.lower()
if title_lower == search_clean:
return 3
elif title_lower.startswith(search_clean):
return 2
elif search_clean in title_lower:
return 1
else:
return 0
movie_df["match_score"] = movie_df["clean_title"].apply(title_match_score)
strong_matches = movie_df[movie_df["match_score"] > 0].sort_values("match_score", ascending=False)
if strong_matches.empty:
close_titles = difflib.get_close_matches(search_query, movie_df["clean_title"], n=25, cutoff=0.5)
filtered = movie_df[movie_df["clean_title"].isin(close_titles)].head(25)
else:
filtered = strong_matches.head(25)
if filtered.empty:
st.warning("No movies found.")
else:
st.markdown("""
""", unsafe_allow_html=True)
for _, movie in filtered.iterrows():
poster_url, _ = get_tmdb_data(movie["clean_title"], movie["year"])
poster_html = (
f"" if poster_url and "placeholder.com" not in poster_url
else "