update code

app.py

@@ -29,21 +29,21 @@ 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(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)]
-#     return diseases
+# @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(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)]
+    return diseases