Fixed old python scripts

slope-evaluator/api.py

-from fastapi import FastAPI
+from fastapi import FastAPI,Request
 import trend_slope_computer as trend_slope_computer
 import logging
 import os
 import pandas as pd
+import json
+import numpy as np
+from fastapi.encoders import jsonable_encoder
 
 app = FastAPI()
 
+def execute(results, threshold):
 
-@app.get("/evaluate-slope")
-def evaluate_slope(total_lag):
-    print("request received")
-    print(total_lag)
-    execute(total_lag, 1000)
-    return {"suitable" : "false"}
-
+    d = []
+    for result in results:
+        #print(results)
+        group = result['metric']['group']
+        for value in result['values']:
+            # print(value)
+            d.append({'group': group, 'timestamp': int(
+                value[0]), 'value': int(value[1]) if value[1] != 'NaN' else 0})
 
+    df = pd.DataFrame(d)
 
-def execute(total_lag, threshold):
-    df = pd.DataFrame(total_lag)
+    print(df)
     try:
-        trend_slope = trend_slope_computer.compute(df, 60)
+        trend_slope = trend_slope_computer.compute(df, 0)
     except Exception as e:
         err_msg = 'Computing trend slope failed'
         print(err_msg)
@@ -30,3 +35,12 @@ def execute(total_lag, threshold):
     print(f"Trend Slope: {trend_slope}")
 
     return trend_slope < threshold
+
+@app.post("/evaluate-slope",response_model=bool)
+async def evaluate_slope(request: Request):
+    print("request received")
+    x = json.loads(await request.body())
+    #x = np.array(x['total_lag'])
+    y = execute(x['total_lag'], 1000)
+    print(print(y))
+    return y

slope-evaluator/trend_slope_computer.py

@@ -2,7 +2,8 @@ from sklearn.linear_model import LinearRegression
 import pandas as pd
 import os
 
-def compute(input, warmup_sec):
+def compute(x, warmup_sec):
+    input = x
     input['sec_start'] = input.loc[0:, 'timestamp'] - input.iloc[0]['timestamp']
     regress = input.loc[input['sec_start'] >= warmup_sec] # Warm-Up