ag added service API using docker and fastAPI

2024-03-20 16:30:30 +01:00
parent 759f4c50d0
commit 08c5f672be
16 changed files with 3450 additions and 264 deletions
--- a/service/app/predict.py
+++ b/service/app/predict.py
@@ -0,0 +1,60 @@
+
+
+import json
+import xgboost as xgb
+import pickle
+import pandas as pd
+import numpy as np
+def predict(json_input:str):
+
+    with open('app/metadata.pkl','rb') as f:
+        to_remove,use_small,evacuations,encoders = pickle.load(f)
+    
+    
+    data = pd.DataFrame(json.loads(json_input[1:-1]),index=[0])
+    
+    data.drop(columns=['dateandtime','skiarea_id','day_of_year','minute_of_day','year'], inplace=True, errors='ignore')
+    
+    ##evacuation_vehicles must be explicitated and workaround for other!
+    evacuation = data.evacuation_vehicles.values[0]
+    if isinstance(evacuation,str):
+        evacuation = [evacuation]
+    for c in evacuations:
+        data[c] = False
+    for c in evacuation:
+        data[c] = True
+    
+    for c in evacuation:
+        if c not in evacuations:
+            data['other'] = True
+            break
+    
+    data.drop(columns=['town','province','evacuation_vehicles'],inplace=True, errors='ignore')
+    
+    
+    data['age'] =  data['age'].astype(np.float32)
+
+    
+
+    for c in data.columns:
+        if c not in ['india','age','season','skiarea_name','destination']:
+            data[c] = data[c].astype('str')  
+    if use_small:
+        for c in to_remove.keys():
+            for k in to_remove[c]:
+                data.loc[data[c]==k,c] = 'other'
+    for c in data.columns:
+        if c not in ['age','season','skiarea_name','india']:
+            data[c] =  data[c].fillna('None')
+            if use_small:
+                data[c] = pd.Categorical( encoders['small'][c].transform(data[c]), categories=list(range(len(encoders['small'][c].classes_))), ordered=False)
+            else:
+                data[c] = pd.Categorical( encoders['normal'][c].transform(data[c]), categories=list(range(len(encoders['normal'][c].classes_))), ordered=False)
+    
+    bst_FS = xgb.Booster()
+    bst_FS.load_model("app/model.json")
+
+    dtest_FS = xgb.DMatrix(data[bst_FS.feature_names],enable_categorical=True)
+    preds = bst_FS.predict(dtest_FS)
+    
+    return preds[0]*100