Upload app.py

app.py

@@ -98,14 +98,7 @@ json_output_embedding = instructor_model.predict(
 
 json_file = open(json_output_embedding, "r")
 json_dict = json.load(json_file)
-dense_embedding = json_dict["data"]
-
-#dense_embedding_arr = np.array(
-#    literal_eval(dense_embedding_output)[0], dtype=np.float64
-#)
-#dense_embedding = dense_embedding_arr.tolist()
-#print(type(dense_embedding[2]))
-# dense_embedding = [float(x) for x in dense_embedding_output[0]]
+dense_embedding = list(map(float, json_dict["data"][0]))
 
 text_embedding_instructions_choice = [
     "Represent the financial statement for retrieval:",