Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,67 @@
-import altair as alt
+import streamlit as st
+from huggingface_hub import hf_hub_download
 import numpy as np
 import pandas as pd
-import streamlit as st
-
-"""
-# Welcome to Streamlit!
+from annoy import AnnoyIndex
 
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+PLACEHOLDER_POSTER = "https://upload.wikimedia.org/wikipedia/commons/3/3f/Placeholder_view_vector.svg"
 
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
+# Download from your Hugging Face repo
+movies_path = hf_hub_download(repo_id="Smiley0707/Movie-recommendation", filename="movies_df.csv")
+feature_path = hf_hub_download(repo_id="Smiley0707/Movie-recommendation", filename="feature_array.npz")
+index_path = hf_hub_download(repo_id="Smiley0707/Movie-recommendation", filename="my_index.ann")
 
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
+# Load files
+new_movies = pd.read_csv(movies_path)
+feature_array = np.load(feature_path)['arr_0']
+f = feature_array.shape[1]
+annoy_index = AnnoyIndex(f, 'angular')
+annoy_index.load(index_path)
+new_movies['title'] = new_movies['title'].fillna('')
+new_movies['poster_path'] = new_movies['poster_path'].fillna('')
 
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
+# Main UI
+st.set_page_config(page_title="🎬 Movie Recommender", layout="wide")
+st.title("🎬 Movie Recommendation System")
 
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
+query = st.text_input("Search for a movie:")
 
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
+def get_poster(idx):
+    url = new_movies.iloc[idx]['poster_path']
+    if not url or str(url).strip() == "":
+        return PLACEHOLDER_POSTER
+    return url
 
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+if query:
+    mask = new_movies['title'].str.lower().str.contains(query.lower(), na=False)
+    results = new_movies[mask]
+    if results.empty:
+        st.info("No movies found.")
+    else:
+        st.subheader("Search Results:")
+        cols = st.columns(5)
+        movie_indices = list(results.index[:5])
+        for i, idx in enumerate(movie_indices):
+            with cols[i]:
+                st.markdown(
+                    f"""<div style="border-radius:15px;border:2px solid #eee;padding:10px;text-align:center;">
+                        <img src="{get_poster(idx)}" alt="poster" style="width:100px;height:150px;border-radius:10px;"><br>
+                        <b>{new_movies.iloc[idx]['title']}</b>
+                    </div>
+                    """,
+                    unsafe_allow_html=True
+                )
+        recommend_for_idx = movie_indices[0]
+        st.markdown(f"### Recommendations for {new_movies.iloc[recommend_for_idx]['title']}:")
+        recs = annoy_index.get_nns_by_item(recommend_for_idx, 6)[1:]
+        rec_cols = st.columns(min(len(recs), 5))
+        for i, r in enumerate(recs):
+            with rec_cols[i]:
+                st.markdown(
+                    f"""<div style="border-radius:15px;border:2px solid #eee;padding:10px;text-align:center;">
+                        <img src="{get_poster(r)}" alt="poster" style="width:100px;height:150px;border-radius:10px;"><br>
+                        <b>{new_movies.iloc[r]['title']}</b>
+                    </div>
+                    """,
+                    unsafe_allow_html=True
+                )