Create app.py

app.py

@@ -0,0 +1,33 @@
+import pickle
+import json
+import pandas as pd
+import gradio as gr
+
+ct_load = pickle.load(open('ct.pkl', 'rb'))
+xgb_load = pickle.load(open('xgb_classifier.pkl', 'rb'))
+label_name_list = ['1', '2', '3']
+
+one_data_str = '{"Box_type": "光分箱", "Scenes": "【光分】箱体缺盖", "OBD_number": 1, "ONU_number": 1, "Budget": 440.0, "Average_price": 440.0, "Low_light_obd_number": 0, "Low_light_onu_number": 0, "Low_light_frequency": 0, "Low_light_ratio": 0.0, "Is_low_ligh_over_25": "否", "Is_low_ligh_over_5_users": "否", "Is_obd_all_low_light": "否", "break_off_frequency": 0, "break_off_duration": 0, "break_off_resources_number": 0, "Single_user_break_off_duration": 0.0, "label": 3}'
+one_data_str
+
+def predict_one(one_data_str):
+  one_data_dict = json.loads(one_data_str)
+  one_df = pd.DataFrame([one_data_dict])
+  one_array = ct_load.transform(one_df)
+  pred_num = xgb_load.predict(one_array)[0]
+  pred_pro = max(xgb_load.predict_proba(one_array)[0])
+  pred_name = label_name_list[pred_num]
+  # 转为json字符串格式
+  result_dict = {'pred_pro':str(pred_pro),'pred_num':str(pred_num),'pred_name':pred_name }
+  result_json = json.dumps(result_dict)
+
+  return result_json
+
+demo = gr.Interface(fn=predict_one, 
+             inputs="text",
+             outputs="text",
+             examples=[one_data_str,one_data_str ],
+             )
+             
+# demo.launch(debug=True)
+demo.launch()