diff options
Diffstat (limited to 'backend/microservice/api/controller.py')
| -rw-r--r-- | backend/microservice/api/controller.py | 28 |
1 files changed, 15 insertions, 13 deletions
diff --git a/backend/microservice/api/controller.py b/backend/microservice/api/controller.py index f870b2b1..988ad987 100644 --- a/backend/microservice/api/controller.py +++ b/backend/microservice/api/controller.py @@ -53,7 +53,7 @@ class train_callback(tf.keras.callbacks.Callback): @app.route('/train', methods = ['POST']) def train(): - #print("******************************TRAIN*************************************************") + print("******************************TRAIN*************************************************") f = request.files.get("file") data = pd.read_csv(f) @@ -88,11 +88,10 @@ def train(): "h5FileId" : fileId, "metrics" : m } - #print(predictor) - #print('\n') + print(predictor) url = config.api_url + "/Predictor/add" r = requests.post(url, json=predictor).text - #print(r) + print(r) return r @app.route('/predict', methods = ['POST']) @@ -101,34 +100,37 @@ def predict(): model = tf.keras.models.load_model(h5) paramsExperiment = json.loads(request.form["experiment"]) paramsPredictor = json.loads(request.form["predictor"]) - #print("********************************model loaded*******************************") + print("********************************model loaded*******************************") result = newmlservice.predict(paramsExperiment, paramsPredictor, model) return result @app.route('/preprocess',methods=['POST']) def returnColumnsInfo(): - #print("********************************PREPROCESS*******************************") + print("********************************PREPROCESS*******************************") + dataset = json.loads(request.form["dataset"]) file = request.files.get("file") data=pd.read_csv(file) - - #dataset={} + ''' #f = request.json['filepath'] #data=pd.read_csv(f) - + dataset={} + ''' preprocess = newmlservice.returnColumnsInfo(data) #samo 10 jedinstvenih posto ih ima previse, bilo bi dobro da promenimo ovo da to budu 10 najzastupljenijih vrednosti for col in preprocess["columnInfo"]: - col["uniqueValues"] = col["uniqueValues"][0:10] - col["uniqueValuesCount"] = col["uniqueValuesCount"][0:10] + col["uniqueValues"] = col["uniqueValues"][0:6] + col["uniqueValuesCount"] = col["uniqueValuesCount"][0:6] + col['uniqueValuesPercent']=col['uniqueValuesPercent'][0:6] dataset["columnInfo"] = preprocess["columnInfo"] dataset["nullCols"] = preprocess["allNullColl"] dataset["nullRows"] = preprocess["allNullRows"] dataset["colCount"] = preprocess["colCount"] dataset["rowCount"] = preprocess["rowCount"] + dataset["cMatrix"]=preprocess['cMatrix'] dataset["isPreProcess"] = True - #print(dataset) + return jsonify(dataset) -#print("App loaded.") +print("App loaded.") app.run()
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