o bagulho eh louco

api/classifier.py

@@ -4,52 +4,35 @@
 
 #import handlers.pickle_handler as model_handler
 import handlers.data_transform as data_transform
+import sys
 
-def keystroke_classifier(req_json):
+def keystroke_classifier(df):
     # TODO: Implementar
     classifier = load_keystroke_model()
 
-    keystroke_array = data_transform.array_for_predict(req_json)
+    pred = classifier.predict(df)
 
-    pred = classifier.predict(keystroke_array)
-
-    return 1
+    return pred
 
 
 def face_classifier(req_decoded_image):
     # TODO: Implementar
     #classifier = load_facial_model()
     pic = face_recognition.load_image_file(req_decoded_image)
     encoding = face_recognition.face_encodings(pic)[0]
-    
+
     results = face_recognition.compare_faces(clientes, encoding,tolerance=0.6)
-    
+
     return max(results)
     #pred = classifier.predict(decoded_image)
     return "foo"
 
-def text_classify(text):
-    text_vectorized = vectorize_text(text)
-    classifier = load_text_model()
-
-    pred = classifier.predict(text_vectorized)
-
-    flag = None
-
-    if pred[0] == 0:
-        flag = 'Red flag'
-    elif pred[0] == 1:
-        flag = 'Green flag'
-
-    return flag
-
-
 
 #TODO enviar funções para o handlers
 
 # Keystroke
 def load_keystroke_model():
-    model = pickle.load(open('./bin_models/v1/model_keystroke.pickle', 'rb'))
+    model = pickle.load(open('/api/api/bin_models/v1/voting.pickle', 'rb'))
 
     return model
 
@@ -58,23 +41,4 @@ def load_keystroke_model():
 def load_facial_model():
     model = pickle.load(open('./bin_models/v1/model_facial.pickle', 'rb'))
 
-    return model
-
-
-# Text
-def load_text_model():
-    model = pickle.load(open('./bin_models/v1/rf-classifier', 'rb'))
-
-    return model
-
-def load_text_vectorizer():
-    vectorizer = pickle.load(open('./bin_models/v1/tf-idf-vectorizer', 'rb'))
-
-    return vectorizer
-
-def vectorize_text(text):
-    vectorizer = load_text_vectorizer()
-
-    text_vectorized = vectorizer.transform([text])
-
-    return text_vectorized
+    return model

api/handlers/data_transform.py

@@ -1,4 +1,34 @@
+import pandas as pd
+import sys
+
+
 def array_for_predict(req_json):
     print('ayn')
     #array precisa ser de 31, e preencher com 0 o que não tiver.
     return [0,1,0,1]
+
+
+def transform_keystroke(data):
+    df = pd.DataFrame.from_dict([data])
+    df.fillna(0, inplace=True)
+    print(df.columns)
+
+    df['DD.5.shift.r'] = df.get('DD.5.shift', 0) + df.get('DD.shift.r', 0)
+    df['UD.5.shift.r'] = df.get('UD.5.r', 0) + df.get('UD.5.shift', 0)
+
+    df['H.shift.r'] = df.get('H.r', 0)
+    df['DD.shift.r.o'] = df.get('DD.r.o', 0)
+    df['UD.shift.r.o'] = df.get('UD.r.o', 0) + df.get('UD.shift.o', 0)
+
+    df.drop(columns=['UD.r.o', 'UD.shift.o', 'H.r', 'UD.5.r', 'UD.5.shift', 'DD.shift.r', 'DD.5.shift'], inplace=True, errors='ignore')
+    df['H.return'] = df['H.l']
+
+    colsnew = ['H.period', 'DD.period.t', 'UD.period.t', 'H.t', 'DD.t.i', 'UD.t.i', 'H.i', 'DD.i.e', 'UD.i.e',
+               'H.e', 'DD.e.5', 'UD.e.5', 'H.5', 'DD.5.shift.r', 'UD.5.shift.r', 'H.shift.r', 'DD.shift.r.o',
+               'UD.shift.r.o', 'H.o', 'DD.o.a', 'UD.o.a', 'H.a', 'DD.a.n', 'UD.a.n', 'H.n',
+               'DD.n.l', 'UD.n.l', 'H.l', 'DD.l.return', 'UD.l.return', 'H.return'
+    ]
+
+    df = df[colsnew]
+
+    return df

api/server.py

@@ -1,17 +1,20 @@
 import numpy as np
+import sys
 import cv2
+import json
 
 from flask import Flask
 from flask import jsonify
 from flask import request
 from flasgger import Swagger
 from flasgger import swag_from
+from flask_cors import CORS
 
 from swagger.swagger_config import swagger_configuration
 
 from classifier import keystroke_classifier
 from classifier import face_classifier
-from classifier import text_classify 
+from handlers.data_transform import transform_keystroke
 
 #import os
 #import sys
@@ -22,6 +25,7 @@
 
 app = Flask(__name__)
 swagger = Swagger(app, config=swagger_configuration)
+CORS(app, origins="*")
 
 
 def decode_image(file):
@@ -44,11 +48,13 @@ def status():
 @app.route('/keystroke', methods=['POST'])
 @swag_from('swagger/keystroke.yml')
 def keystroke():
-    req_dict = request.get_json()
+    req_dict = json.loads(request.data)
 
-    classification = keystroke_classifier(req_dict)
+    data = transform_keystroke(req_dict)
 
-    return jsonify({'classification': classification})
+    classification = keystroke_classifier(data)
+
+    return jsonify({'classification': classification[0]})
 
 
 @app.route('/face', methods=['POST'])
@@ -60,24 +66,9 @@ def face_recognition():
         return response
 
     #precisa que manda a imagem e não decodificado
-
     decoded_image = decode_image(request.files['image'])
+
     #o que é essa função decode_image???
-
     classification = face_classifier(decoded_image)
 
     return jsonify({'classification': classification})
-
-#classificação textual para remover depois
-
-@app.route('/classify', methods=['POST'])
-@swag_from('swagger/classify.yml')
-def route_classify():
-    req_dict = request.get_json()
-
-    try:
-        classification = text_classify(req_dict['text'])
-    except KeyError:
-        return jsonify({'Error': 'Corpo da requisição inválido'}), 400
-
-    return jsonify({'classification': classification})

docker-compose.yml

@@ -10,3 +10,9 @@ services:
       FLASK_DEBUG: 1
     volumes:
       - .:/api
+    networks:
+      - lion
+
+networks:
+  lion:
+    driver: bridge

requirements.txt

@@ -6,4 +6,6 @@ opencv-python-headless
 gunicorn
 cmake
 dlib
-face_recognition
+face_recognition
+pandas
+flask-cors