update code

app.py

@@ -28,17 +28,21 @@ class Disease(BaseModel):
 def greet_json():
     return {"Hello": "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