Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,125 +1,48 @@
-import pickle
-import streamlit as st
-import requests
-from huggingface_hub import hf_hub_download
-import os
-
-# --- PAGE CONFIGURATION ---
-st.set_page_config(
-    page_title="Movie Recommender",
-    page_icon="🎬",
-    layout="wide"
-)
-
-# --- STYLING ---
-st.markdown("""
-<style>
-h1 { text-align: center; color: #FF4B4B; }
-.movie-title { font-size: 16px; font-weight: bold; text-align: center; min-height: 3rem; color: #FFFFFF; }
-.stButton > button { width: 100%; border-radius: 50px; font-size: 18px; font-weight: bold; margin: 0.5em 0; background-color: #FF4B4B; color: white; }
-.stButton > button:hover { background-color: #FFFFFF; color: #FF4B4B; border: 2px solid #FF4B4B; }
-</style>
-""", unsafe_allow_html=True)
-
-# --- API AND RECOMMENDATION FUNCTIONS ---
-@st.cache_data
-def fetch_poster(movie_id):
-    """Fetches the movie poster URL from TMDB API."""
-    url = f"https://api.themoviedb.org/3/movie/{movie_id}?api_key=8265bd1679663a7ea12ac168da84d2e8&language=en-US"
-    try:
-        response = requests.get(url)
-        response.raise_for_status()
-        data = response.json()
-        poster_path = data.get('poster_path')
-        if poster_path:
-            return "https://image.tmdb.org/t/p/w500/" + poster_path
-        else:
-            return "https://via.placeholder.com/500x750.png?text=No+Poster+Available"
-    except requests.exceptions.RequestException as e:
-        st.error(f"Error fetching data: {e}")
-        return "https://via.placeholder.com/500x750.png?text=API+Error"
-
-def recommend(movie):
-    """Recommends 5 movies based on similarity."""
-    try:
-        index = movies[movies['title'] == movie].index[0]
-        distances = sorted(list(enumerate(similarity[index])), reverse=True, key=lambda x: x[1])
-        
-        recommended_movie_names = []
-        recommended_movie_posters = []
-        
-        for i in distances[1:6]:
-            movie_id = movies.iloc[i[0]].movie_id
-            recommended_movie_posters.append(fetch_poster(movie_id))
-            recommended_movie_names.append(movies.iloc[i[0]].title)
-            
-        return recommended_movie_names, recommended_movie_posters
-    except IndexError:
-        st.error("Movie not found in the dataset. Please select another one.")
-        return [], []
-
 # --- LOAD DATA FROM HUGGING FACE HUB ---
 @st.cache_resource
-@st.cache_resource
 def load_model_files():
-    """Load model files from Hugging Face Hub with caching."""
+    """Load model files from Hugging Face using direct URL download."""
     try:
-        from huggingface_hub import hf_hub_download
-        import pickle
+        import requests
+        import tempfile
+        
+        # Create temporary files
+        with tempfile.NamedTemporaryFile(delete=False, suffix='.pkl') as tmp_movie:
+            movie_temp_path = tmp_movie.name
         
-        # Download files directly to memory
-        movie_list_bytes = hf_hub_download(
-            repo_id="N4F1U/Movie_Recommender_tmdb",
-            filename="movie_list.pkl",
-            repo_type="model",
-            local_dir_use_symlinks=False
-        )
+        with tempfile.NamedTemporaryFile(delete=False, suffix='.pkl') as tmp_similarity:
+            similarity_temp_path = tmp_similarity.name
         
-        similarity_bytes = hf_hub_download(
-            repo_id="N4F1U/Movie_Recommender_tmdb",
-            filename="similarity.pkl",
-            repo_type="model",
-            local_dir_use_symlinks=False
-        )
+        # Direct URLs to your files
+        base_url = "https://huggingface.co/N4F1U/Movie_Recommender_tmdb/resolve/main/"
         
-        # Load from the downloaded file paths
-        movies_data = pickle.load(open(movie_list_bytes, 'rb'))
-        similarity_data = pickle.load(open(similarity_bytes, 'rb'))
+        # Download movie_list.pkl
+        response = requests.get(base_url + "movie_list.pkl", stream=True)
+        response.raise_for_status()
+        with open(movie_temp_path, 'wb') as f:
+            for chunk in response.iter_content(chunk_size=8192):
+                f.write(chunk)
+        
+        # Download similarity.pkl
+        response = requests.get(base_url + "similarity.pkl", stream=True)
+        response.raise_for_status()
+        with open(similarity_temp_path, 'wb') as f:
+            for chunk in response.iter_content(chunk_size=8192):
+                f.write(chunk)
+        
+        # Load the files
+        with open(movie_temp_path, 'rb') as f:
+            movies_data = pickle.load(f)
+        
+        with open(similarity_temp_path, 'rb') as f:
+            similarity_data = pickle.load(f)
+        
+        # Clean up temporary files
+        os.unlink(movie_temp_path)
+        os.unlink(similarity_temp_path)
         
         return movies_data, similarity_data
         
     except Exception as e:
         st.error(f"Error loading model files: {e}")
-        return None, None
-
-# Load the data
-movies, similarity = load_model_files()
-
-if movies is None or similarity is None:
-    st.error("Failed to load model data. Please check your repository and try again.")
-    st.stop()
-
-movie_list = movies['title'].values
-
-# --- APP LAYOUT ---
-st.title('Movie Recommender System 🍿')
-
-# Center the selection box and button using columns
-_, col_centered, _ = st.columns([1, 2, 1])
-with col_centered:
-    selected_movie = st.selectbox(
-        "Type or select a movie to get recommendations",
-        movie_list
-    )
-
-    if st.button('Show Recommendation'):
-        with st.spinner('Finding similar movies for you...'):
-            recommended_names, recommended_posters = recommend(selected_movie)
-        
-        if recommended_names:
-            st.success("Here are your top 5 recommendations!")
-            cols = st.columns(5, gap="medium")
-            for i, col in enumerate(cols):
-                with col:
-                    st.markdown(f'<p class="movie-title">{recommended_names[i]}</p>', unsafe_allow_html=True)
-                    st.image(recommended_posters[i], use_container_width='always')
+        return None, None