Update app.py

app.py

@@ -1,11 +1,72 @@
 from flask import Flask,render_template
 from flask_socketio import SocketIO,emit
 import base64
+
+from keras.models import load_model
+from PIL import Image
 import numpy as np
 import cv2 
-import numpy as np
 
 
 app = Flask(__name__)
 app.config['SECRET_KEY'] = 'secret!'
 socket = SocketIO(app,async_mode="eventlet")
+
+
+#the following are to do with this interactive notebook code
+from matplotlib import pyplot as plt # this lets you draw inline pictures in the notebooks
+import pylab # this allows you to control figure size 
+pylab.rcParams['figure.figsize'] = (10.0, 8.0) # this controls figure size in the notebook
+
+###loading model###
+age_model = load_model('Copy of age_model_pretrained.h5')
+gender_model = load_model('Copy of gender_model_pretrained.h5')
+emotion_model = load_model('emotion_model_pretrained.h5')
+
+# Labels on Age, Gender and Emotion to be predicted
+age_ranges = ['1-2', '3-9', '10-20', '21-27', '28-45', '46-65', '66-116']
+gender_ranges = ['male', 'female']
+emotion_ranges= ['positive','negative','neutral']
+
+def base64_to_image(base64_string):
+    # Extract the base64 encoded binary data from the input string
+    base64_data = base64_string.split(",")[1]
+    # Decode the base64 data to bytes
+    image_bytes = base64.b64decode(base64_data)
+    # Convert the bytes to numpy array
+    image_array = np.frombuffer(image_bytes, dtype=np.uint8)
+    # Decode the numpy array as an image using OpenCV
+    image = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
+    return image
+
+@socket.on("connect")
+def test_connect():
+    print("Connected")
+    emit("my response", {"data": "Connected"})
+
+@socket.on("image")
+def receive_image(image):
+    # Decode the base64-encoded image data
+    image = base64_to_image(image)
+    image = cv2.resize(image, (224, 224), interpolation=cv2.INTER_AREA)
+    # emit("processed_image", image)
+    # Make the image a numpy array and reshape it to the models input shape.
+    image = np.asarray(image, dtype=np.float32).reshape(1, 224, 224, 3)
+    image = (image / 127.5) - 1
+    # Predicts the model
+    # prediction1 = age_model.predict(image)
+    prediction2 = gender_model.predict(image)
+    # prediction3 = emotion_model.predict(image)
+
+    index = np.argmax(prediction2)
+    gender_ranges = gender_ranges[index]
+    age = prediction1[0][index]
+    emit("result",{"gender":str(gender_ranges)})
+
+@app.route("/")
+def home():
+    return render_template("index.html")
+
+if __name__ == '__main__':
+    # app.run(debug=True)
+    socket.run(app,host="0.0.0.0", port=7860)