update code

app.py

@@ -30,24 +30,17 @@ class Disease(BaseModel):
 def greet_json():
     return {"Hello": "World!"}
 
-# @app.post("/")
-# async def greet_post():
-#     return {"Hello": "Post World!"}
-
 @app.post("/", response_model=list[Disease])
 async def predict(query: str):
     query_embedding = model.encode(query).astype('float')
     similarity_vectors = model.similarity(query_embedding, corpus)[0]
-    print("Similarity Vector Shape: ", similarity_vectors.shape)
     scores, indicies = torch.topk(similarity_vectors, k=len(corpus))
-    print("Scores Shape: ", scores.shape)
-    print("Indicies Shape: ", indicies.shape)
+    # print("Similarity Vector Shape: ", similarity_vectors.shape)
+    # print("Scores Shape: ", scores.shape)
+    # print("Indicies Shape: ", indicies.shape)
     id_ = df.iloc[indicies].reset_index(drop=True)
     id_ = id_.drop_duplicates("label")
-    print(id_.columns)
-    print(scores)
     scores = scores[id_.index]
-    print(scores)
     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)]