Create app.py

app.py

@@ -0,0 +1,72 @@
+from keras.models import load_model
+from PIL import Image
+import numpy as np
+import cv2
+
+#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('age_model_pretrained.h5')
+gender_model = load_model('gender_model_pretrained.h5')
+emotion_model = load_model('emotion_model_pretrained.h5')
+
+# Labels on Age, Gender and Emotion to be predicted
+age_ranges = ['1-5', '6-10', '11-15', '16-20', '21-25', '26-30', '31-35','36-40','41-50','51-60','61-70','71-80','81-90']
+gender_ranges = ['male', 'female']
+emotion_ranges= ['positive','negative','neutral']
+
+############################
+import io
+import streamlit as st
+bytes_data=None
+
+img_file_buffer=st.camera_input("Take a picture")
+if img_file_buffer is not None:
+     
+     test_image = Image.open(img_file_buffer)
+     st.image(test_image, use_column_width=True)
+     st.write(type(test_image))
+     test_image = np.asarray(test_image)
+     gray = cv2.cvtColor(test_image,cv2.COLOR_BGR2GRAY)
+     face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
+     faces = face_cascade.detectMultiScale(gray, 1.3, 5)
+
+     i = 0
+
+     for (x,y,w,h) in faces:
+             i = i+1
+             cv2.rectangle(test_image,(x,y),(x+w,y+h),(203,12,255),2)
+
+             img_gray=gray[y:y+h,x:x+w]
+
+             emotion_img = cv2.resize(img_gray, (48, 48), interpolation = cv2.INTER_AREA)
+             emotion_image_array = np.array(emotion_img)
+             emotion_input = np.expand_dims(emotion_image_array, axis=0)
+             output_emotion= emotion_ranges[np.argmax(emotion_model.predict(emotion_input))]
+            
+             gender_img = cv2.resize(img_gray, (100, 100), interpolation = cv2.INTER_AREA)
+             gender_image_array = np.array(gender_img)
+             gender_input = np.expand_dims(gender_image_array, axis=0)
+             output_gender=gender_ranges[np.argmax(gender_model.predict(gender_input))]
+
+             age_image=cv2.resize(img_gray, (200, 200), interpolation = cv2.INTER_AREA)
+             age_input = age_image.reshape(-1, 200, 200, 1)
+             output_age = age_ranges[np.argmax(age_model.predict(age_input))]
+
+
+             output_str = str(i) + ": "+  output_gender + ', '+ output_age + ', '+ output_emotion
+             st.write(output_str)
+            
+             col = (0,255,0)
+
+             cv2.putText(test_image, str(i),(x,y),cv2.FONT_HERSHEY_SIMPLEX,1,col,2)
+     st.image(cv2.cvtColor(test_image, cv2.COLOR_BGR2RGB))
+if bytes_data is None:
+     st.stop()