Add necessary files

app.py

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+import numpy as np
+import pandas as pd
+from deepface import DeepFace
+import streamlit as st
+import cv2
+import base64
+import time
+
+st.set_page_config(layout="wide")
+
+
+
+
+
+cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+
+import tempfile
+import os
+
+weights_paths = {
+    'age': '/home/appuser/.deepface/weights/age_model_weights.h5',
+    'gender': '/home/appuser/.deepface/weights/gender_model_weights.h5',
+    'race': '/home/appuser/.deepface/weights/race_model_single_batch.h5',
+    'emotion': '/home/appuser/.deepface/weights/facial_expression_model_weights.h5'
+}
+
+def upload():
+    image=None
+    initial_image = st.camera_input('Take a picture')
+    original_image = initial_image
+    temp_path = None
+    if initial_image is not None:
+        bytes_data = initial_image.getvalue()
+        image = cv2.imdecode(np.frombuffer(bytes_data, np.uint8), cv2.IMREAD_COLOR)
+
+    return image, original_image
+
+
+    
+
+def main(options):
+    
+   
+    
+    
+   
+    if st.checkbox('Take a picture for prediction'):
+        
+        image, original_image= upload()
+        if original_image is not None and original_image is not None and st.button('Prediction'):  # Check if original_image is not None
+            st.warning('Wait for few seconds!!')
+            progress_bar = st.progress(0.0)
+            status_text = st.empty()
+            
+            result = DeepFace.analyze(image,detector_backend=options,actions=['age','gender','emotion'])
+            
+            for i in range(100):
+                progress_bar.progress((i + 1) / 100)
+                status_text.text(f"Processing {i+1}%")
+                time.sleep(0.01)
+            
+            progress_bar.empty()
+            gray_frame = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+            faces = cascade.detectMultiScale(gray_frame, 1.1, 3)
+            
+
+            for x,y,w,h in faces:
+                
+                
+                cv2.rectangle(image, (x, y), (x+w, y+h), (4, 29, 255), 2, cv2.LINE_4)
+                user_selected_items = list(result[0].keys())
+                if 'age' in user_selected_items:
+                    age_label='Age: '+str(result[0]['age'])
+                    cv2.putText(image, age_label, (x ,y+h+30), cv2.FONT_ITALIC,1 ,(255,255,0), 2)
+                if 'dominant_gender' in user_selected_items:
+                    gender_label='Gender: '+str(result[0]['dominant_gender'])
+                    cv2.putText(image, gender_label, (x, y+h+70), cv2.FONT_ITALIC,1, (0,255,255), 2)
+                
+                if 'dominant_emotion' in user_selected_items:
+                    emotion_label='Emotion: '+str(result[0]['dominant_emotion']).title()
+                    cv2.putText(image, emotion_label, (x, y+h+110), cv2.FONT_ITALIC,1 ,(255,0,255), 2)
+
+            st.image(image, channels='BGR')
+           
+
+   
+if __name__ == '__main__':
+    def get_options():
+        actions = ['opencv','mtcnn','retinaface']
+        option2 = st.selectbox('Choose the following backend:', actions)
+        return option2
+   
+    main(get_options())

requirements.txt

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+opencv-contrib-python-headless
+deepface
+mtcnn