from flask import Flask,render_template
from flask_socketio import SocketIO,emit
import base64
import numpy as np
import cv2
import time
from deepface import DeepFace
app = Flask(__name__)
app.config['SECRET_KEY'] = 'secret!'
socket = SocketIO(app,async_mode="eventlet")
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
def music_link(emo):
if emo == "fear":
res = ''
elif emo == "angry":
res = ''
elif emo == 'neutral':
res = ''
elif emo =='sad':
res= ''
elif emo == 'disgust':
res = ''
elif emo == 'happy':
res = ''
elif emo == 'surprise':
res = ''
else:
res = ''
return res
@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)
# Predicts the model
cv2.imwrite("./res.jpg",image)
objs = DeepFace.analyze(img_path = "./res.jpg",
actions = ['emotion'])
time.sleep(3)
emo = objs[0]['dominant_emotion']
res = music_link(emo)
emit("result",{"emo":str(emo),"res":res})
@app.route("/")
def home():
return render_template("index.html")
if __name__ == '__main__':
# app.run(debug=True)
socket.run(app, debug=True,port=7860,host="0.0.0.0")