Update app.py

app.py

@@ -35,6 +35,35 @@ datafile_path = "fine_food_reviews_with_embeddings_1k.csv"
 df = pd.read_csv(datafile_path)
 df["embedding"] = df.embedding.apply(eval).apply(np.array)
 
+# search through the reviews for a specific product
+def search_reviews(df, product_description, n=3, pprint=True):
+    product_embedding = get_embedding(
+        product_description,
+        engine="text-embedding-ada-002"
+    )
+    df["similarity"] = df.embedding.apply(lambda x: cosine_similarity(x, product_embedding))
+
+    results = (
+        df.sort_values("similarity", ascending=False)
+        .head(n)
+        .combined.str.replace("Title: ", "")
+        .str.replace("; Content:", ": ")
+    )
+
+    product = (
+                df.sort_values("similarity", ascending=False)
+        .head(n)
+        .ProductId
+    )
+
+    if pprint:
+        for r in range(n):
+          idx = results.index[r]
+          print("Product : ",product[idx])
+          print(results[idx])
+          print()
+    return results,product
+
 prompt = input("What do you want to search for? : ")
 top_n = int(input("How many results do you want to see? : "))
 print()