Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import faiss
+from sentence_transformers import SentenceTransformer
+
+model=SentenceTransformer("Prashasst/anime-recommendation-model")
+
+
+
+embeddings = np.load('data/embeddings.npy')
+embeddings_id = np.load('data/embeddings_id.npy')
+
+index=faiss.read_index('data/anime_faiss.index')
+
+def recommend_anime(query, k=5):
+    """
+    Recommends anime based on a query using a FAISS index and a Prashasst's SentenceTransformer model.
+
+    Args:
+        query (str): The input query to find similar anime.
+        k (int): The number of recommendations to return.
+
+    Returns:
+        List[str]: A list of recommended anime ids.
+    """
+
+
+    # Encode the query
+    query_embedding = model.encode(query).reshape(1, -1)  # Reshape to 2D array
+
+    # Search for similar anime
+    distances, indices = index.search(query_embedding, k=k)
+
+    # Get the anime titles
+    recommended_anime = []
+    for i in indices[0]:
+        anime_id = embeddings_id[i]
+        # anime_name = df.loc[df['id'] == anime_id, 'title_english'].values[0]
+        # if pd.isna(anime_name):
+        #     anime_name = df.loc[df['id'] == anime_id, 'title_romaji'].values[0]
+        recommended_anime.append(anime_id)
+
+    return {"ids":recommended_anime}
+
+
+
+# Create the Gradio app
+with gr.Blocks() as app:
+    gr.Markdown("## Anime Recommendation System")
+    
+    with gr.Row():
+        query = gr.Textbox(label="Enter your anime preferences or query:")
+        top_k = gr.Slider(1, 10, value=5, label="Number of Recommendations")
+
+    with gr.Row():
+        recommend_button = gr.Button("Get Recommendations")
+        output = gr.JSON(label="Recommended Anime")
+
+    recommend_button.click(recommend_anime, inputs=[query, top_k], outputs=output)
+
+# Launch the app
+app.launch(share=True)