diff --git a/README.md b/README.md
index f146e4c..e202abc 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,7 @@
 ![Alt text](http://i.imgur.com/2ngZopS.jpg "Screenshot")
 
+yuotube Vedio: https://youtu.be/_WUq3ABN3R0
+
 webcam-pulse-detector
 -----------------------
 
diff --git a/columns.txt b/columns.txt
new file mode 100644
index 0000000..42e2657
--- /dev/null
+++ b/columns.txt
@@ -0,0 +1,14 @@
+age
+sex
+cp
+trtbps
+chol
+fbs
+restecg
+thalachh
+exng
+oldpeak
+slp
+caa
+thall
+o2Saturation
diff --git a/dataset/heart.csv b/dataset/heart.csv
new file mode 100644
index 0000000..c211c93
--- /dev/null
+++ b/dataset/heart.csv
@@ -0,0 +1,304 @@
+age,sex,cp,trtbps,chol,fbs,restecg,thalachh,exng,oldpeak,slp,caa,thall,output
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diff --git a/dataset/o2Saturation.csv b/dataset/o2Saturation.csv
new file mode 100644
index 0000000..5470b6b
--- /dev/null
+++ b/dataset/o2Saturation.csv
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diff --git a/get_pulse.py b/get_pulse.py
index 5ba151d..83af61e 100644
--- a/get_pulse.py
+++ b/get_pulse.py
@@ -6,12 +6,12 @@
 import numpy as np
 import datetime
 #TODO: work on serial port comms, if anyone asks for it
-#from serial import Serial
 import socket
 import sys
+import joblib
+import pandas as pd
 
 class getPulseApp(object):
-
     """
     Python application that finds a face in a webcam stream, then isolates the
     forehead.
@@ -46,7 +46,7 @@ def __init__(self, args):
                 port = int(port)
             self.udp = (ip, port)
             self.sock = socket.socket(socket.AF_INET, # Internet
-                 socket.SOCK_DGRAM) # UDP
+                                      socket.SOCK_DGRAM) # UDP
 
         self.cameras = []
         self.selected_cam = 0
@@ -56,9 +56,9 @@ def __init__(self, args):
                 self.cameras.append(camera)
             else:
                 break
-        self.w, self.h = 0, 0
+        self.w, self.w, = 0, 0
         self.pressed = 0
-        # Containerized analysis of recieved image frames (an openMDAO assembly)
+        # Containerized analysis of received image frames (an openMDAO assembly)
         # is defined next.
 
         # This assembly is designed to handle all image & signal analysis,
@@ -67,7 +67,7 @@ def __init__(self, args):
 
         # Basically, everything that isn't communication
         # to the camera device or part of the GUI
-        self.processor = findFaceGetPulse(bpm_limits=[50, 160],
+        self.processor = findFaceGetPulse(bpm_limits=[60, 160],
                                           data_spike_limit=2500.,
                                           face_detector_smoothness=10.)
 
@@ -82,6 +82,11 @@ def __init__(self, args):
                              "c": self.toggle_cam,
                              "f": self.write_csv}
 
+        # Load the columns and model for heart attack prediction
+        with open('columns.txt', 'r') as f:
+            self.columns = [line.strip() for line in f]
+        self.heart_attack_model = joblib.load('heart_attack_model.pkl')
+
     def toggle_cam(self):
         if len(self.cameras) > 1:
             self.processor.find_faces = True
@@ -94,22 +99,66 @@ def write_csv(self):
         """
         Writes current data to a csv file
         """
-        fn = "Webcam-pulse" + str(datetime.datetime.now())
-        fn = fn.replace(":", "_").replace(".", "_")
+        fna = "Webcam-pulse" + str(datetime.datetime.now())
+        fna = fna.replace(":", "_").replace(".", "_")
         data = np.vstack((self.processor.times, self.processor.samples)).T
-        np.savetxt(fn + ".csv", data, delimiter=',')
-        print("Writing csv")
+        np.savetxt(fna + ".csv", data, delimiter=',')
+        print("Writing csv with bpm")
 
     def toggle_search(self):
         """
         Toggles a motion lock on the processor's face detection component.
 
         Locking the forehead location in place significantly improves
-        data quality, once a forehead has been sucessfully isolated.
+        data quality, once a forehead has been successfully isolated.
         """
-        #state = self.processor.find_faces.toggle()
         state = self.processor.find_faces_toggle()
         print("face detection lock =", not state)
+        # Initialize attributes for heart attack prediction
+        self.age = None
+        self.sex = None
+        self.o2_saturation = None
+        self.cp = None
+
+    def set_age(self, age):
+        self.age = age
+
+    def set_sex(self, sex):
+        self.sex = sex
+
+    def set_o2_saturation(self, o2_saturation):
+        self.o2_saturation = o2_saturation
+
+    def set_cp(self, cp):
+        self.cp = cp
+
+    def predict_heart_attack(self):
+        # Collect the necessary features for prediction
+        features = {
+            'age': self.age,
+            'sex': self.sex,
+            'o2Saturation': self.o2_saturation,
+            'cp': self.cp
+        }
+        print(features.keys())
+        # Example: Populate features with default values if needed
+        if 'cp' not in features:
+            features['cp'] = 0  # Define a sensible default
+        for feature in self.columns:
+            if feature not in features:
+                print(f"Missing feature: {feature}")
+                # Optionally, handle missing keys with defaults or warnings
+                features[feature] = 0  # Example default, adjust as needed
+
+        # Ensure the features are in the correct order
+        feature_values = [features[feature] for feature in self.columns]
+        print(self.columns)
+        expected_columns = ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak', 'slope', 'ca', 'thal']
+        print(all(col in self.columns for col in expected_columns))  # Should be True
+
+        # Predict heart attack
+        prediction = self.heart_attack_model.predict([feature_values])
+        return prediction[0]
 
     def toggle_display_plot(self):
         """
@@ -195,6 +244,17 @@ def main_loop(self):
         if self.send_udp:
             self.sock.sendto(str(self.processor.bpm), self.udp)
 
+        # Example: Set the attributes with actual data
+        self.set_age(45)  # Replace with actual data
+        self.set_sex(1)   # Replace with actual data
+        self.set_o2_saturation(98.6)  # Replace with actual data
+        self.set_cp(3)  # Replace with actual data
+
+        # Predict heart attack
+        heart_attack_risk = self.predict_heart_attack()
+        if heart_attack_risk == 1:
+            print("Warning: High risk of heart attack detected!")
+
         # handle any key presses
         self.key_handler()
 
@@ -210,4 +270,4 @@ def main_loop(self):
     args = parser.parse_args()
     App = getPulseApp(args)
     while True:
-        App.main_loop()
+        App.main_loop()
\ No newline at end of file
diff --git a/get_pulse.spec b/get_pulse.spec
deleted file mode 100644
index cf298aa..0000000
--- a/get_pulse.spec
+++ /dev/null
@@ -1,28 +0,0 @@
-# -*- mode: python -*-
-a = Analysis(['get_pulse.py'],
-             pathex=[
-                 '/Users/tristanhearn/Documents/thearn_repos/webcam-pulse-detector'],
-             hiddenimports=[],
-             hookspath=None,
-             runtime_hooks=None)
-a.datas += [
-    ('haarcascade_frontalface_alt.xml', 'haarcascade_frontalface_alt.xml', 'DATA')]
-pyz = PYZ(a.pure)
-exe = EXE(pyz,
-          a.scripts,
-          exclude_binaries=True,
-          name='get_pulse',
-          debug=False,
-          strip=None,
-          upx=True,
-          console=False)
-coll = COLLECT(exe,
-               a.binaries,
-               a.zipfiles,
-               a.datas,
-               strip=None,
-               upx=True,
-               name='get_pulse')
-app = BUNDLE(coll,
-             name='get_pulse.app',
-             icon=None)
diff --git a/heart_attack_model.pkl b/heart_attack_model.pkl
new file mode 100644
index 0000000..a8fb72b
Binary files /dev/null and b/heart_attack_model.pkl differ
diff --git a/heart_attack_predictor.py b/heart_attack_predictor.py
new file mode 100644
index 0000000..fbed93f
--- /dev/null
+++ b/heart_attack_predictor.py
@@ -0,0 +1,37 @@
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score
+import joblib
+
+# Load datasets
+heart_data = pd.read_csv('dataset/heart.csv')
+o2_data = pd.read_csv('dataset/o2Saturation.csv', header=None, names=['o2Saturation'])
+
+# Preprocess data
+# Assuming o2Saturation is an additional feature to heart_data
+heart_data['o2Saturation'] = o2_data['o2Saturation']
+
+# Split data into features and target
+X = heart_data.drop('output', axis=1)
+y = heart_data['output']
+
+# Save the columns of X
+with open('columns.txt', 'w') as f:
+    for column in X.columns:
+        f.write(f"{column}\n")
+
+# Split data into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Train a RandomForestClassifier
+model = RandomForestClassifier(n_estimators=100, random_state=42)
+model.fit(X_train, y_train)
+
+# Evaluate the model
+y_pred = model.predict(X_test)
+accuracy = accuracy_score(y_test, y_pred)
+print(f'Model Accuracy: {accuracy * 100:.2f}%')
+
+# Save the model
+joblib.dump(model, 'heart_attack_model.pkl')
\ No newline at end of file
diff --git a/lib/device.py b/lib/device.py
index e80665c..9fc8232 100644
--- a/lib/device.py
+++ b/lib/device.py
@@ -1,7 +1,7 @@
 import cv2, time
-#TODO: fix ipcam
-#import urllib2, base64
+import  base64  # Add base64 import
 import numpy as np
+import urllib.request  # Add this import
 
 class ipCamera(object):
 
@@ -9,11 +9,11 @@ def __init__(self,url, user = None, password = None):
         self.url = url
         auth_encoded = base64.encodestring('%s:%s' % (user, password))[:-1]
 
-        self.req = urllib2.Request(self.url)
+        self.req = urllib.request.Request(self.url)  # Update urllib2 to urllib.request
         self.req.add_header('Authorization', 'Basic %s' % auth_encoded)
 
     def get_frame(self):
-        response = urllib2.urlopen(self.req)
+        response = urllib.request.urlopen(self.req)  # Update urllib2 to urllib.request
         img_array = np.asarray(bytearray(response.read()), dtype=np.uint8)
         frame = cv2.imdecode(img_array, 1)
         return frame
diff --git a/lib/imageProcess.py b/lib/imageProcess.py
index 8ce5da1..00845cd 100644
--- a/lib/imageProcess.py
+++ b/lib/imageProcess.py
@@ -1,5 +1,4 @@
-from openmdao.lib.datatypes.api import Float, Dict, Array, List, Int, Bool
-from openmdao.main.api import Component, Assembly
+from openmdao.api import ExplicitComponent, Problem, Group, IndepVarComp
 import numpy as np
 import time
 import cv2
@@ -8,8 +7,7 @@
 Whole-frame image processing components & helper methods
 """
 
-
-class RGBSplit(Component):
+class RGBSplit(ExplicitComponent):
 
     """
     Extract the red, green, and blue channels from an (n,m,3) shaped
@@ -20,19 +18,19 @@ class RGBSplit(Component):
 
     def __init__(self):
         super(RGBSplit, self).__init__()
-        self.add("frame_in", Array(iotype="in"))
-
-        self.add("R", Array(iotype="out"))
-        self.add("G", Array(iotype="out"))
-        self.add("B", Array(iotype="out"))
+        self.add_input("frame_in", val=np.zeros((1, 1, 3)))
+        self.add_output("R", val=np.zeros((1, 1)))
+        self.add_output("G", val=np.zeros((1, 1)))
+        self.add_output("B", val=np.zeros((1, 1)))
 
-    def execute(self):
-        self.R = self.frame_in[:, :, 0]
-        self.G = self.frame_in[:, :, 1]
-        self.B = self.frame_in[:, :, 2]
+    def compute(self, inputs, outputs):
+        frame_in = inputs['frame_in']
+        outputs['R'] = frame_in[:, :, 0]
+        outputs['G'] = frame_in[:, :, 1]
+        outputs['B'] = frame_in[:, :, 2]
 
 
-class RGBmuxer(Component):
+class RGBmuxer(ExplicitComponent):
 
     """
     Take three (m,n) matrices of equal size and combine them into a single
@@ -41,18 +39,19 @@ class RGBmuxer(Component):
 
     def __init__(self):
         super(RGBmuxer, self).__init__()
-        self.add("R", Array(iotype="in"))
-        self.add("G", Array(iotype="in"))
-        self.add("B", Array(iotype="in"))
-
-        self.add("frame_out", Array(iotype="out"))
+        self.add_input("R", val=np.zeros((1, 1)))
+        self.add_input("G", val=np.zeros((1, 1)))
+        self.add_input("B", val=np.zeros((1, 1)))
+        self.add_output("frame_out", val=np.zeros((1, 1, 3)))
 
-    def execute(self):
-        m, n = self.R.shape
-        self.frame_out = cv2.merge([self.R, self.G, self.B])
+    def compute(self, inputs, outputs):
+        R = inputs['R']
+        G = inputs['G']
+        B = inputs['B']
+        outputs['frame_out'] = cv2.merge([R, G, B])
 
 
-class CVwrapped(Component):
+class CVwrapped(ExplicitComponent):
 
     """
     Generic wrapper to take the simpler functions from the cv2 or scipy image
@@ -68,14 +67,15 @@ class CVwrapped(Component):
 
     def __init__(self, func, *args, **kwargs):
         super(CVwrapped, self).__init__()
-        self.add("frame_in", Array(iotype="in"))
-        self.add("frame_out", Array(iotype="out"))
+        self.add_input("frame_in", val=np.zeros((1, 1, 3)))
+        self.add_output("frame_out", val=np.zeros((1, 1, 3)))
         self._func = func
         self._args = args
         self._kwargs = kwargs
 
-    def execute(self):
-        self.frame_out = self._func(self.frame_in, *self._args, **self._kwargs)
+    def compute(self, inputs, outputs):
+        frame_in = inputs['frame_in']
+        outputs['frame_out'] = self._func(frame_in, *self._args, **self._kwargs)
 
 
 class Grayscale(CVwrapped):
@@ -100,36 +100,37 @@ def __init__(self):
         super(equalizeContrast, self).__init__(cv2.equalizeHist)
 
 
-class showBPMtext(Component):
+class showBPMtext(ExplicitComponent):
 
     """
     Shows the estimated BPM in the image frame
     """
-    ready = Bool(False, iotype="in")
-    bpm = Float(iotype="in")
-    x = Int(iotype="in")
-    y = Int(iotype="in")
-    fps = Float(iotype="in")
-    size = Float(iotype="in")
-    n = Int(iotype="in")
+    ready = False
+    bpm = 0.0
+    x = 0
+    y = 0
+    fps = 0.0
+    size = 0.0
+    n = 0
 
     def __init__(self):
         super(showBPMtext, self).__init__()
-        self.add("frame_in", Array(iotype="in"))
-        self.add("frame_out", Array(iotype="out"))
+        self.add_input("frame_in", val=np.zeros((1, 1, 3)))
+        self.add_output("frame_out", val=np.zeros((1, 1, 3)))
         self.bpms = []
 
-    def execute(self):
+    def compute(self, inputs, outputs):
+        frame_in = inputs['frame_in']
         self.bpms.append([time.time(), self.bpm])
         if self.ready:
             col = (0, 255, 0)
-            text = "%0.1f bpm" % self.bpm
+            text = "%0.5f bpm" % self.bpm
             tsize = 2
         else:
             col = (100, 255, 100)
             gap = (self.n - self.size) / self.fps
-            text = "(estimate: %0.1f bpm, wait %0.0f s)" % (self.bpm, gap)
+            text = "(estimate: %0.5f bpm, wait %0.2f s)" % (self.bpm, gap)
             tsize = 1
-        cv2.putText(self.frame_in, text,
+        cv2.putText(frame_in, text,
                     (self.x, self.y), cv2.FONT_HERSHEY_PLAIN, tsize, col)
-        self.frame_out = self.frame_in
+        outputs['frame_out'] = frame_in
diff --git a/lib/processors_noopenmdao.py b/lib/processors_noopenmdao.py
index 2dc39db..0b98745 100644
--- a/lib/processors_noopenmdao.py
+++ b/lib/processors_noopenmdao.py
@@ -1,3 +1,5 @@
+from errno import ERANGE
+from matplotlib.widgets import RangeSlider
 import numpy as np
 import time
 import cv2
@@ -58,7 +60,7 @@ def find_faces_toggle(self):
     def get_faces(self):
         return
 
-    def shift(self, detected):
+    def shift(self, detected): 
         x, y, w, h = detected
         center = np.array([x + 0.5 * w, y + 0.5 * h])
         shift = np.linalg.norm(center - self.last_center)
@@ -98,18 +100,18 @@ def plot(self):
         idx = np.where((freqs > 50) & (freqs < 180))
         pylab.figure()
         n = data.shape[0]
-        for k in xrange(n):
+        for k in RangeSlider(n):
             pylab.subplot(n, 1, k + 1)
             pylab.plot(self.times, data[k])
         pylab.savefig("data.png")
         pylab.figure()
-        for k in xrange(self.output_dim):
+        for k in ERANGE(self.output_dim):
             pylab.subplot(self.output_dim, 1, k + 1)
             pylab.plot(self.times, self.pcadata[k])
         pylab.savefig("data_pca.png")
 
         pylab.figure()
-        for k in xrange(self.output_dim):
+        for k in np.arange(self.output_dim):
             pylab.subplot(self.output_dim, 1, k + 1)
             pylab.plot(freqs[idx], self.fft[k][idx])
         pylab.savefig("data_fft.png")
@@ -204,6 +206,12 @@ def run(self, cam):
             pfreq = freqs[idx]
             self.freqs = pfreq
             self.fft = pruned
+
+            if pruned.size == 0:
+                # Handle the case where pruned is empty
+                print("No valid frequency components found.")
+                return
+
             idx2 = np.argmax(pruned)
 
             t = (np.sin(phase[idx2]) + 1.) / 2.
diff --git a/setup.py b/setup.py
deleted file mode 100644
index 3fe12cf..0000000
--- a/setup.py
+++ /dev/null
@@ -1,19 +0,0 @@
-"""
-This is a setup.py script generated by py2applet
-
-Usage:
-    python setup.py py2app
-"""
-
-from setuptools import setup
-
-APP = ['get_pulse.py']
-DATA_FILES = ['cascades/haarcascade_frontalface_alt.xml']
-OPTIONS = {'argv_emulation': True}
-
-setup(
-    app=APP,
-    data_files=DATA_FILES,
-    options={'py2app': OPTIONS},
-    setup_requires=['py2app'],
-)