update code

app.py

@@ -33,12 +33,12 @@ async def predict(query: str):
     query_embedding = model.encode(query).astype('float')
     similarity_vectors = model.similarity(q, all_embeddings)
     scores, indicies = torch.topk(similarity_vectors, k=len(all_embeddings))
-    id = df.iloc[indicies]
-    id = df.drop_duplicates("label")
-    scores = scores[id.index]
-    diseases = label_encoder.inverse_transform(id.label.values)
-    id = id.label.values
-    diseases = [dict("id": value[0], "name": value[1], "score" : value[2]) for value in zip(id, diseases, scores)]
+    id_ = df.iloc[indicies]
+    id_ = df.drop_duplicates("label")
+    scores = scores[id_.index]
+    diseases = label_encoder.inverse_transform(id_.label.values)
+    id_ = id_.label.values
+    diseases = [dict("id": value[0], "name": value[1], "score" : value[2]) for value in zip(_id, diseases, scores)]
     return diseases