def Loggedin(): if request.method == 'POST': #patient_id = request.form['patient_id'] age = int(request.form['age']) sex = int(request.form['sex']) cp = int(request.form['cp']) bp = int(request.form['resting_bp']) cholesterol = int(request.form['serum_cholestrol']) blood_sugar = int(request.form['fasting_sugar']) heart_rate = int(request.form['max_heart_rate']) exercise = int(request.form['exercise']) global g_bp, g_cholesterol, g_bloodsugar, g_heartrate, g_exercise, g_out g_bp = bp g_cholesterol = cholesterol g_bloodsugar = blood_sugar g_heartrate = heart_rate g_exercise = exercise #blood_sugar=int(blood_sugar) #print "down " #dbHandler.insertPatientDetails(patient_id,age,sex,bp,cholesterol,blood_sugar, # heart_rate,exercise) #print "down 1" reload(test) output = test.prediction(age, sex, cp, bp, cholesterol, blood_sugar, heart_rate, exercise) g_out = output return render_template('products.html', **globals()) else: return render_template('index.html')
def test(): data = request.get_json() pred = prediction(data) response_pickled = jsonpickle.encode(pred) return Response(response=response_pickled, status=200, mimetype="application/json")
def home(): if request.method == 'GET': return render_template("index.html") else: # var_list = ['Pclass','Sex','Age_band','Title','Embarked','Fare_cat','Fare_cat','Alone','Family_Size','SibSp','Parch'] var_list = ['Pclass','Sex','Age_band','Title','Embarked','Fare_cat','SibSp','Parch'] data = {} for var in var_list: data[var] = int(request.form[var]) result = t.prediction(data) return render_template("index.html", result=result)
# Prepare TCNN extractor print("Prepare TCNN extractor") conv1_1_weigths = get_conv_1_1_weights(vggCustom_weights_path) tcnn_bottom = create_tcnn_bottom(vggCustom_weights_path, conv1_1_weigths) tcnn_extractor = tcnn_bottom.predict img_size = 224 elif tcnn_type == 'squeezenet': tcnn_model = load_model(squeezenet_tcnn_model_path) # Prepare TCNN extractor print("Prepare TCNN extractor") conv1_weights = get_conv1_weights(squeezeNetCustom_weights_path) tcnn_bottom = create_squeezenet_tcnn_bottom( squeezeNetCustom_weights_path, conv1_weights) tcnn_extractor = tcnn_bottom.predict img_size = 227 client = init(mqttHost="test.mosquitto.org", client_name="JSON", port=1883) K.get_session().run(tf.global_variables_initializer()) prediction(tcnn_model, phrnn_model, tcnn_extractor, test_file, client, img_size=img_size, tcnn_type=tcnn_type, phrnn_type="landmarks")
def evaluate_image(image): return prediction(image)
tcnn_model = load_model(squeezenet_tcnn_model_path) # Prepare TCNN extractor print("Prepare TCNN extractor") conv1_weights = get_conv1_weights(squeezeNetCustom_weights_path) tcnn_bottom = create_squeezenet_tcnn_bottom( squeezeNetCustom_weights_path, conv1_weights) tcnn_extractor = tcnn_bottom.predict img_size = 227 input_host = "169.254.163.109" input_port = 5005 width = 640 height = 480 output_client = init(mqttHost="test.mosquitto.org", client_name="JSON", port=1883) K.get_session().run(tf.global_variables_initializer()) prediction(tcnn_model, phrnn_model, tcnn_extractor, output_client, input_host, input_port, width, height, img_size=img_size, tcnn_type=tcnn_type, phrnn_type="landmarks")