Create app.py

app.py

@@ -0,0 +1,47 @@
+import gradio as gr
+
+from sentence_transformers import SentenceTransformer
+import duckdb
+from huggingface_hub import get_token
+
+model = SentenceTransformer("Snowflake/snowflake-arctic-embed-m-v1.5")
+
+def similarity_search(
+    query: str, 
+    k: int = 5, 
+    dataset_name: str = "smol-blueprint-project/hf-blogs-text-embeddings", 
+    embedding_column: str = "embedding",
+):
+    # Use same model as used for indexing
+    query_vector = model.encode(query)
+    embedding_dim = model.get_sentence_embedding_dimension()
+    
+    sql = f"""
+        SELECT 
+            title,
+            author,
+            date,
+            local,
+            tags,
+            URL,
+            chunk,
+            array_cosine_distance(
+                {embedding_column}::float[{embedding_dim}], 
+                {query_vector.tolist()}::float[{embedding_dim}]
+            ) as distance
+        FROM 'hf://datasets/{dataset_name}/**/*.parquet'
+        ORDER BY distance
+        LIMIT {k}
+    """
+    
+    return duckdb.sql(sql).to_df()
+
+with gr.Blocks() as demo:
+    query = gr.Textbox(label="Query")
+    k = gr.Slider(1, 10, value=5, label="Number of results")
+    btn = gr.Button("Search")
+    results = gr.Dataframe(headers=["title", "url", "content", "distance"])
+    btn.click(fn=similarity_search, inputs=[query, k], outputs=[results])
+    
+
+demo.launch()