Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,48 +1,127 @@
+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
 def load_model_files():
-    """Load model files from Hugging Face using direct URL download."""
+    """Load model files from Hugging Face Hub with caching."""
     try:
-        import requests
-        import tempfile
-        
-        # Create temporary files
-        with tempfile.NamedTemporaryFile(delete=False, suffix='.pkl') as tmp_movie:
-            movie_temp_path = tmp_movie.name
+        # Set environment variable to use /tmp for cache
+        os.environ['HF_HOME'] = '/tmp/huggingface'
+        os.makedirs('/tmp/huggingface', exist_ok=True)
         
-        with tempfile.NamedTemporaryFile(delete=False, suffix='.pkl') as tmp_similarity:
-            similarity_temp_path = tmp_similarity.name
+        # Download files using Hugging Face Hub
+        movie_list_path = hf_hub_download(
+            repo_id="N4F1U/Movie_Recommender_tmdb",
+            filename="movie_list.pkl",
+            cache_dir="/tmp/huggingface"
+        )
         
-        # Direct URLs to your files
-        base_url = "https://huggingface.co/N4F1U/Movie_Recommender_tmdb/resolve/main/"
+        similarity_path = hf_hub_download(
+            repo_id="N4F1U/Movie_Recommender_tmdb",
+            filename="similarity.pkl",
+            cache_dir="/tmp/huggingface"
+        )
         
-        # 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:
+        # Load the pickle files
+        with open(movie_list_path, 'rb') as f:
             movies_data = pickle.load(f)
         
-        with open(similarity_temp_path, 'rb') as f:
+        with open(similarity_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
+        st.error(f"Error type: {type(e).__name__}")
+        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')