added app2.py

app2.py

@@ -0,0 +1,40 @@
+import streamlit as st
+import cv2
+import matplotlib.pyplot as plt
+from deepface import DeepFace
+# from deepface import DeepFace
+import streamlit as st
+import test
+
+header  = st.beta_container()
+inp = st.beta_container()
+pred = st.beta_container()
+
+with header:
+    st.title('Emotion Detection and Song Recommendation')
+    st.text('Aim : To detect the emotion of the person and predict a song')
+
+with inp:
+    st.title("Taking the face of the user as input")
+    faceCascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
+    page = st.sidebar.selectbox("Input or Predict", ("Input", "Predict"))
+
+    if page == "Input":
+        test.take_input()
+#else:
+    #show_explore_page()
+
+with pred:
+    st.title("Prediction")
+    img = cv2.imread('photo.jpg')
+
+    plt.imshow(cv2.cvtColor(img,cv2.COLOR_BGR2RGB))
+
+    predictions = DeepFace.analyze(img)
+
+    st.text("Your emotion is {}".format(predictions['dominant_emotion']))  
+
+    if(predictions['dominant_emotion'] == 'happy'):
+        my_html = '<iframe src="https://open.spotify.com/embed/playlist/37i9dQZF1DWWQRwui0ExPn" width="300" height="380" frameborder="0" allowtransparency="true" allow="encrypted-media"></iframe>'
+
+        st.markdown(my_html, unsafe_allow_html=True)