model

azure_functions/GetRecommendations/__init__.py

@@ -1,24 +1,24 @@
 import logging
-
+import json
 import azure.functions as func
 
+from utils.predict import predict_fun
 
-def main(req: func.HttpRequest) -> func.HttpResponse:
- logging.info('Python HTTP trigger function processed a request.')
 
- name = req.params.get('name')
- if not name:
- try:
- req_body = req.get_json()
- except ValueError:
- pass
- else:
- name = req_body.get('name')
+def main(req: func.HttpRequest) -> func.HttpResponse:
+ req_body = req.get_json()
+ n = req_body.get('n')
+ p = req_body.get('p')
+ k = req_body.get('k')
+ temperture = req_body.get('temperture')
+ ph = req_body.get('ph')
+ humidity = req_body.get('humidity')
+ rainfall = req_body.get('rainfall')
 
- if name:
- return func.HttpResponse(f"Hello, {name}. This HTTP triggered function executed successfully.")
+ if n and p and k and temperture and ph and humidity and rainfall:
+ return func.HttpResponse(predict_fun(n, p, k, temperture, ph, humidity, rainfall))
  else:
  return func.HttpResponse(
  "This HTTP triggered function executed successfully. Pass a name in the query string or in the request body for a personalized response.",
  status_code=200
- )
+ )

azure_functions/utils/predict.py

@@ -0,0 +1,10 @@
+import json
+import azure.functions as func
+import pickle
+
+model = pickle.load(open('../crop_recommendation_model/finalizedresult.pkl', 'rb'))
+output = -1
+def predict_fun(n, p, k, temperture, ph, humidity, rainfall):
+ global output
+ output = model.predict([[n, p, k, temperture, ph, humidity, rainfall]])
+ return json.dumps(output.tolist())